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Reviewed commit: 7596150e95
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- Use platform-aware fio ioengine (libaio on Linux, posixaio elsewhere) - Average memory bandwidth over 10 iterations to reduce measurement noise - Label benchmark results with iteration number when --num-iterations > 1 - Add JSON serializer fallback for numpy types and torch.dtype
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Codex Review: Didn't find any major issues. What shall we delve into next? ℹ️ About Codex in GitHubYour team has set up Codex to review pull requests in this repo. Reviews are triggered when you
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Summary
libaioon Linux, falls back toposixaioon other platforms (e.g. macOS, containers without libaio)--num-iterations > 1, each result row now shows(iter N)in the task name so runs are distinguishable in the output tableMiB × 1048576 / 1e9, consistent with the CUDA device properties path that usesbytes / 1e9defaulthandler tojson.dumpsfor numpy integer/float/array types andtorch.dtype, preventingTypeErrorcrashes on non-serializable result valuesTest plan
python gpubench.py --memory-bandwidthand verify bandwidth output is stable across runspython gpubench.py --disk-ioon Linux and confirm fio uses libaiopython gpubench.py --num-iterations 2 --cpu-single-threadand verify iteration labels appear in tablepython gpubench.py --json --cpu-single-threadand verify JSON output parses without errorsnvidia-smioutput