gfxgraph-benchmarking

name	gfxgraph-benchmarking
description	Benchmark gfxGRAPH internals, run the public benchmark suite, and compare performance between Python and Rust.

gfxGRAPH Benchmarking

Goal Run and analyze performance benchmarks on gfxGRAPH.

When to use this skill

• When verifying the performance impact of modifications to the routing logic (e.g. conditional runner, shape bucketing).
• When validating the package's overall latency and throughput improvements.
• When generating performance report JSON files containing provenance tracking (e.g. commit SHA, ROCm parameters).

How to use it

1. Use real hardware (e.g. AMD Radeon RX 6700 XT) under ROCm 7.2 when running GPU benchmarks.
2. For micro-benchmarking CPU/mock pathways, ensure standard mocks (like mocking torch_cuda_execution_probe) are set up.
3. Run the public benchmark script:

   PYTHONPATH=python python benchmarks/bench_readme_public.py --run-count 3 --output benchmarks/results/readme_benchmark_latest.json
   

4. Run python/rust micro-benchmarks:
   • Routing: python benchmarks/bench_routing.py vs. python benchmarks/bench_routing_rust.py
   • Conditional: python benchmarks/bench_conditional_mock.py

Preferred checks

• Check latency output (avg_replay_us) in gfxgraph.stats().
• Compare ops/sec throughput differences on dynamic-shape bucketed runs.

Pass criteria

• Public benchmark completes successfully and writes a populated JSON output containing system provenance metrics (commit SHA, ROCm runtime, etc.).
• Performance regression checking shows throughput remains within target bounds (e.g. >= 90% static graph replay for shape bucketing, >= 85% for conditional branching).