This repository aims to benchmark Matrix Multiply (SGEMM) hand-tuned libraries and code generation stacks. The focus will be on machine learning workloads so FP32 or smaller and irregular sizes of matrices, but the suite can be extended for other sizes easily.
To build the code, run
cmake -GNinja -DCMAKE_CXX_COMPILER=clang++-11 -DCMAKE_C_COMPILER=clang-11 -DUSE_MLIR=ON -B build .
cmake --build build
This code has been tested with clang-11 but older versions will also work. To plot the results, you will need to install matplotlib.
pip install matplotlib
We use AOT compilation to generate the binaries for matrix multiplication and then run them to generate the benchmarking numbers. To run all the tests, do
cmake --build build/matmul --target run_all_tests
To plot the results against MKL (and generate a plot like above), run
python3 plot_results.py
To run a specific matrix size (say 24x64x512), run
./build/matmul/matmul_24x64x512
The linalg codegen pass is in matmul/matmul-compile/matmul-compile.cpp.
This benchmark was run on an Intel Xeon CPU running at 3.1GHz. The machine has 256Kb L1 cache, 8Mb L2 cache and 24.8Mb L3 cache. It supports AVX-512 instructions. The peak performance of the machine is 3.1 x 8 x 2 x 2 = 99.2 GFLOPS for double precision and 198.4 GFLOPS for single precision.