Hey guys - Not an expert here. Here is an educational benchmarking tool to explore how different parameters affect matrix multiplication (GEMM) performance between a custom Triton kernel and PyTorch’s built-in GEMM, evaluated on NVIDIA B200.

The Triton GEMM was mostly taken from : https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html#sphx-glr-getting-started-tutorials-03-matrix-multiplication-py

• Compare Triton vs PyTorch GEMM throughput
• Toggle Tensor Core usage, tiling, and pipeline stages
• Enable fused bias + activation inside the kernel
• Visualize results in a Streamlit dashboard (GFLOP/s, latency, relative error)

• Precision: float16, bfloat16, float32
• Tile sizes: BLOCK_M, BLOCK_N, BLOCK_K
• Compute scheduling: num_warps, num_stages, GROUP_M
• Fusion: Bias and activation fused into the GEMM kernel
• Reuse / Grouped mapping / Pipeline: Improves cache locality and overlap of load+compute

python3 -m venv venv
source venv/bin/activate
pip3 install -r requirements.txt
streamlit run gemm_benchmark_tritonvstorch.py

Under MIT License, see LICENSE