README.md

metadata

license: mit
pretty_name: BoLT Parallelism Configuration (64 GPUs)
task_categories:
  - other
tags:
  - bayesian-optimization
  - benchmark
  - parallelism-configuration
  - tabular
size_categories:
  - n<1K

pco64

Throughput of distributed LLM training as a function of the parallelism configuration, on 64 GPUs across 16 hosts. Tabular benchmark for the bolt problem pco64: the 780 rows are the full candidate set.

• Objective throughput_mean, to maximise. NaN where the run OOMed, since no throughput is observed at all -- a hidden (crash) constraint, not a bad value.
• Constraint ran_successfully. 560 of 780 configurations run; the rest exhaust GPU memory. Feasibility is only learnable by attempting the run, so it cannot be decoupled from the objective.
• Auxiliary peak_mem (GB, inf when infeasible), peak_mem_normalised (the run's own feasibility margin, < 0 iff feasible, a fixed pad value when infeasible) and overall_time (wall clock in seconds, NaN when infeasible).

peak_mem does not define the constraint: feasible runs here reach 79.3 GB, so no memory threshold separates the classes. peak_mem_normalised does, by construction.

Instance context

Constant for every row; these describe the instance, not the search space.

field	value
num_layers	64
num_attention_heads	64
hidden_size	5120
ffn_hidden_size	25600
batch_size	512
seq_length	8192
log2_num_gpus	6
log2_num_hosts	4
log2_grad_accum_steps	0

Columns

The table is the raw sweep export, unchanged. Every knob halves or doubles, so the sizes arrive as log2_* and their levels are evenly spaced. zero_stage is the raw stage (0/2/3); bolt searches it as an ordinal 0/1/2.

column	role	meaning
log2_dp_size	input	log2 of data-parallel size
log2_tp_size	input	log2 of tensor-parallel size
log2_pp_size	input	log2 of pipeline-parallel size
log2_cp_size	input	log2 of context-parallel size
log2_sp_size	input	log2 of sequence-parallel size
log2_dp_bucket_size_mb	input	log2 of DDP gradient bucket size in MB
zero_stage	input	ZeRO stage, one of [0, 2, 3]
log2_num_model_chunks	input	log2 of number of model chunks
log2_grad_accum_steps	context	log2 of gradient accumulation steps
ran_successfully	output	ground-truth constraint label
throughput_mean	output	objective, NaN when infeasible
throughput_std	output	spread over the timed steps, NaN when infeasible
peak_mem_normalised	output	feasibility margin, < 0 iff feasible
overall_time	output	wall clock in seconds, NaN when infeasible
all_time_steps	output	per-step times, as a string list
peak_mem	output	peak GPU memory in GB, inf when infeasible
peak_mem_per_gpu	output	per-GPU peak memory, as a string list

Best observed

throughput_mean = 0.27068 at log2_dp_size=4, log2_tp_size=2, log2_pp_size=0, log2_cp_size=0, log2_sp_size=2, log2_dp_bucket_size_mb=6, zero_stage=2, log2_num_model_chunks=0 (peak memory 74.2 GB).

Provenance

data_64depth-64gpus.csv, converted to parquet unchanged.