id stringlengths 16 16 | domain_id int64 1 5 | domain stringclasses 5
values | tool_spec stringclasses 5
values | prompt stringclasses 20
values | response stringlengths 5.88k 7.62k | code stringlengths 5.12k 6.85k | eval_assertion stringlengths 786 898 | benchmark_metric stringclasses 5
values | lines_count int64 95 130 | messages stringlengths 6.75k 8.53k |
|---|---|---|---|---|---|---|---|---|---|---|
p19_sft_d01_0000 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_0():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_0", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_000(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0001 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_1():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_1", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_001(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0002 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_2():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_2", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_002(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0003 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_3():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_3", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_003(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0004 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_4():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_4", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_004(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0005 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_5():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_5", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_005(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 122 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0006 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_6():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_6", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_006(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0007 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_7():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_7", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_007(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 129 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0008 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_8():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_8", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_008(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0009 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_9():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_9", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_009(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
assert ... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0010 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_10():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_10", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_010(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0011 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_11():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_11", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_011(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0012 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_12():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_12", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_012(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0013 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_13():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_13", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_013(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0014 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_14():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_14", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_014(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0015 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_15():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_15", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_015(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0016 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_16():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_16", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_016(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0017 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_17():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_17", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_017(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0018 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_18():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_18", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_018(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0019 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_19():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_19", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_019(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 128 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0020 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_20():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_20", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_020(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0021 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_21():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_21", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_021(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0022 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_22():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_22", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_022(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0023 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_23():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_23", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_023(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0024 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_24():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_24", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_024(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0025 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_25():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_25", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_025(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0026 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_26():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_26", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_026(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0027 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_27():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_27", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_027(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 128 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0028 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_28():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_28", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_028(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0029 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_29():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_29", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_029(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0030 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_30():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_30", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_030(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0031 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_31():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_31", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_031(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0032 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_32():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_32", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_032(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 116 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0033 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_33():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_33", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_033(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0034 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_34():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_34", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_034(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0035 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_35():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_35", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_035(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 125 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0036 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_36():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_36", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_036(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0037 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_37():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_37", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_037(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0038 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_38():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_38", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_038(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0039 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_39():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_39", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_039(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0040 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_40():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_40", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_040(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0041 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_41():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_41", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_041(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0042 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_42():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_42", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_042(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0043 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_43():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_43", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_043(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0044 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_44():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_44", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_044(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0045 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_45():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_45", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_045(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 122 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0046 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_46():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_46", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_046(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0047 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_47():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_47", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_047(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 129 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0048 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_48():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_48", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_048(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0049 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_49():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_49", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_049(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0050 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_50():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_50", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_050(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0051 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_51():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_51", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_051(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0052 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_52():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_52", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_052(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0053 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_53():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_53", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_053(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0054 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_54():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_54", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_054(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0055 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_55():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_55", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_055(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0056 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_56():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_56", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_056(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0057 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_57():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_57", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_057(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0058 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_58():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_58", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_058(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0059 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_59():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_59", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_059(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 128 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0060 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_60():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_60", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_060(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0061 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_61():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_61", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_061(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0062 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_62():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_62", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_062(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0063 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_63():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_63", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_063(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0064 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_64():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_64", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_064(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0065 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_65():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_65", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_065(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 122 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0066 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_66():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_66", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_066(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0067 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_67():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_67", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_067(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 129 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0068 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_68():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_68", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_068(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0069 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_69():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_69", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_069(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0070 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_70():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_70", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_070(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0071 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_71():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_71", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_071(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0072 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_72():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_72", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_072(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0073 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_73():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_73", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_073(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0074 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_74():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_74", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_074(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0075 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_75():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_75", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_075(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0076 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_76():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_76", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_076(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0077 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_77():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_77", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_077(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0078 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_78():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_78", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_078(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0079 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_79():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_79", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_079(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 128 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0080 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_80():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_80", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_080(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0081 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_81():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_81", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_081(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0082 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_82():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_82", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_082(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0083 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_83():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_83", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_083(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0084 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_84():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_84", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_084(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0085 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_85():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_85", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_085(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 121 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0086 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_86():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_86", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_086(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0087 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_87():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_87", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_087(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 128 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0088 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_88():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_88", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_088(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0089 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_89():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_89", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_089(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0090 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_90():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_90", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_090(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0091 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_91():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_91", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_091(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 126 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0092 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_92():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_92", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_092(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 116 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0093 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_93():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_93", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_093(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0094 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_94():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_94", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_094(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 117 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0095 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_95():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_95", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_095(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 125 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0096 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Design and implement a production-grade Slurm Cluster Scheduler & GPU Topology Allocator for an autonomous cloud GPU cluster running 1024x NVIDIA H100 GPUs. Ensure zero dynamic heap allocation on critical serving paths, full lifecycle state synchronization, and strict telemetry bounding. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig(BaseModel):
model_config = ConfigDict(extra="forbid", frozen=True)
engine_id: str = Field(default="slurmgputopologys... | def test_slurmgputopologyscheduler_96():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_96", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_096(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 118 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0097 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Implement an enterprise-ready Slurm Cluster Scheduler & GPU Topology Allocator module supporting distributed LLM training and vLLM serving. The architecture must enforce strict NUMA/NVLink affinity, atomic synchronization, and non-blocking circuit-breaker defenses against silent node degradation. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_97():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_97", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_097(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 120 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0098 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Construct a mission-critical Slurm Cluster Scheduler & GPU Topology Allocator subsystem for multi-tenant AI supercomputing clusters. Guard against buffer exhaustion, handle edge-case teardown transitions gracefully, and integrate real-time DCGM telemetry and RoCE v2 congestion controls. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
__slots__ = ("engine_id", "max_nodes", "vram_budget_mb", "heartbeat_timeout_ms")
def __init__(self, **kwargs):
... | def test_slurmgputopologyscheduler_98():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_98", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_098(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 119 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
p19_sft_d01_0099 | 1 | Slurm Cluster Scheduler & GPU Topology Allocator | {"subsystem": "SlurmGPUTopologyScheduler", "domain_id": 1, "capabilities": ["lifecycle_tracking", "vram_budgeting", "telemetry_metrics", "circuit_breaker"], "concurrency": "reentrant_threadsafe", "target_hardware": "NVIDIA H100/H200 SXM5 / Grace-Hopper GH200"} | Develop a robust Slurm Cluster Scheduler & GPU Topology Allocator engine for large-scale speculative decoding and chunked prefill serving. Provide full operational lifecycle management, verifiable execution invariants, and zero-defect memory arena tracking. | <thinking>
Analyzing Cloud GPU Infrastructure & Distributed Serving requirements for: Slurm Cluster Scheduler & GPU Topology Allocator
Target cluster scale: 1024x NVIDIA H100 GPUs, RoCE v2 400Gbps fabrics, NVLink 900GB/s meshes.
Invariants to uphold:
1. Zero dynamic heap allocation during active model forward passes; a... | import time
import math
import struct
import threading
from typing import Dict, Any, List, Optional, Tuple
from pydantic import BaseModel, Field, ConfigDict
class SlurmGPUTopologySchedulerConfig:
def __init__(self, engine_id: str = "slurmgputopologyscheduler_core", max_nodes: int = 128, vram_budget_mb: int = 81920... | def test_slurmgputopologyscheduler_99():
cfg = SlurmGPUTopologySchedulerConfig(engine_id="test_gpu_unit_99", max_nodes=16, vram_budget_mb=40960)
inst = SlurmGPUTopologyScheduler_099(cfg)
res1 = inst.register_gpu_node("node-h100-01", total_gpus=8, nvlink_peers=[(0,1),(1,2),(2,3),(4,5),(5,6),(6,7)])
asser... | {"benchmark": "Slurm-Bench / ClusterSched", "baseline_pass1": 52.8, "finetuned_pass1": 81.4, "delta": "+28.6%"} | 127 | [{"role": "system", "content": "You are a Principal Cloud GPU Infrastructure Architect and Distributed LLM Serving Systems Engineer specializing in Slurm scheduling, NVLink/NVSwitch topologies, NCCL collectives, RoCE v2 lossless RDMA, DCGM hardware diagnostics, vLLM PagedAttention virtual memory, and high-throughput in... |
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