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license: mit
tags:
- synthetic
- rl
- multi-agent
- cuda
- triton
- inference
- kernel-optimization
---
# kernelswarm — multi-agent kernel-optimization episodes (RL)
Synthetic dataset of **multi-agent long-horizon optimization campaigns** on
AI-inference kernels. Each row is one whole episode: a lead orchestrator plus
2–20 specialist agents split the work, exchange dispatches/statuses/handoffs,
submit full kernel candidates, and receive simulated compile/verify/bench
feedback — with a reward attached to every environment result.
**Inference-focused only.** All ops are inference primitives (decode/prefill
attention, quantized GEMM/GEMV, sampling, norms, KV-cache, MoE, Mamba).
> ⚠️ All benchmarks, compiles and correctness checks are **simulated
> analytically** — no kernel was ever compiled or executed on a real GPU.
## Row format (one row = one episode)
```json
{
"episode_id": "flash_decode_h100_B=8,..._dispatch_74",
"scenario": "dispatch", // dispatch | parallel_sweep | pipeline | campaign
"n_agents": 6,
"op": "flash_decode", "impl": "cuda",
"gpu": "h100", "arch": "sm_90a", "dtype": "fp16",
"shape": {...}, "baseline_ms": 0.42, "best_ms": 0.006,
"speedup": 66.4, "elapsed_s": 17335,
"agents": [{"id": "a01", "role": "worker", "skill": 0.9,
"tries": 8, "kept": 5, "best_ms": 0.008}, ...],
"num_messages": 74,
"messages": [ ... ]
}
```
## Message kinds
| kind | role | meaning |
|-------------|-----------------|------------------------------------------------|
| `plan` | lead | campaign split strategy |
| `dispatch` | lead | subtask assignment (`to`, `subtask`) |
| `candidate` | agent | **full kernel source** + `params` |
| `result` | env | compile/verify/bench outcome + `reward` |
| `status` | agent | terse progress report |
| `review` | reviewer | pass/fail verdict |
| `verify` | verifier | correctness check report |
| `leaderboard` | env | ranked agent best-times |
| `merge` | lead | pick winner, update shared best |
| `redirect` | lead | re-assign agent to winner's variant/region |
| `handoff` | agent | pass artifact to another agent |
| `verdict` | lead | final outcome + terminal reward |
## Rewards
- `result` msgs: `min(1, log2(prev_best / new))` on kept improvement;
`-0.05` no-improvement, `-0.1` compile error, `-0.3` wrong result,
`-0.05` unsupported arch.
- `verdict` msg: `log2(speedup) / 4` terminal bonus.
## Scenarios
- **dispatch** — lead splits variants across workers, merges, redirects losers (2–8 agents)
- **parallel_sweep** — N workers each own a cfg slice of one variant; leaderboard + top-2 refine (4–20)
- **pipeline** — proposer → reviewer → verifier → tuner chain over variants (3–6)
- **campaign** — fleet dispatch over variant×cfg space + verifier rebench (5–20)
## Coverage
- 17 ops (14 CUDA + 3 Triton impls): flash_decode, swa_decode, gemm_fp16,
int8_gemm, fp8_gemm, int4_gemv, rmsnorm, rope, swiglu_act, topk_sample,
nucleus_sample, mamba_scan, moe_scatter, kv_append, fused_qkv_rope,
triton_flash_decode, triton_rmsnorm
- GPUs: h100, h200, a100, a10, rtx_a6000, rtx_5090 (arch features gated:
wgmma/tma/fp8/fp4/cp.async)
- 2400 episodes · ~170k messages · avg 71 msgs/episode · avg 8.6 agents
- Simulated horizon: 1–5 h per episode
## Files
- `batches/batch_*.jsonl` — 24 shards × 100 episodes
- `episodes.jsonl` — all episodes concatenated
- `tasks_holdout.jsonl` — (op, gpu, shape) task specs for eval
- `stats.json` — aggregate stats
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