High-Performance SGEMM on NVIDIA GPUs

Important note: while the implementation is expected to be high-performant on all Ada/Ampere/Volta/Turing devices, it was specifically fine-tuned for and tested on NVIDIA RTX 3090 (GA102 chip - RTX 3080, A10, A40, A6000).

Benchmark

Avoid using WSL for performance measurements. To ensure accurate and reliable results, please use a native Linux environment.

To benchmark the code, specify compute capability of your CUDA device and run benchmark.sh. For example, on RTX 3090:

bash benchmark.sh 86

The benchmark settings such as minimum/maximum matrix sizes, step size, number of warm-up iterations etc. can be adjusted in the benchmark.sh file.

To visualize benchmark results, please install matplotlib and run

python plot_benchmark_data.py benchmark_results

Tests

Use test.sh to test the implementation for correctness. For example, on RTX 3090:

bash test.sh 86

Performance

Test environment:

• OS: Ubuntu 24.04.1 LTS
• GPU: NVIDIA RTX 3090
• Driver Version: 550.120
• CUDA Driver: 12.4, CUDA Runtime: 12.6, V12.6.85
• CMake 3.28.3
• g++ 13.3