benchmark

name	benchmark
description	Run matmul performance benchmarks. Use when user wants to measure TFLOPS, compare kernel performance, or verify correctness after code changes.

Benchmark PyGPUkit

Run comprehensive matmul benchmarks for all supported dtypes.

Usage

# Full benchmark
python scripts/benchmark.py

# Quick mode (fewer iterations)
python scripts/benchmark.py --quick

# Specific sizes
python scripts/benchmark.py --sizes 4096,8192

# TF32 kernel version
python scripts/benchmark.py --tf32-version v2

Options

• --sizes: Comma-separated matrix sizes (default: 2048,4096,8192)
• --quick: Fewer warmup/iterations for faster results
• --dtypes: Which dtypes to test (default: fp32,tf32,fp16,bf16)
• --tf32-version: v1 (WMMA) or v2 (PTX mma.sync, default)

Instructions

1. Ensure the project is built before benchmarking
2. Run python scripts/benchmark.py [options]
3. Report the performance results including:
   • TFLOPS for each dtype and size
   • Correctness verification (PASS/FAIL)
   • Comparison with theoretical peak

Expected Results (RTX 5090)

Dtype	Target TFLOPS
FP32	~18
TF32	~27
FP16	~15
BF16	~15

Environment Variables

• PYGPUKIT_ALLOW_TF32=1: Enable TF32 TensorCore
• PYGPUKIT_TF32_V2=1: Use PTX mma.sync kernel