These sets of benchmarks are for the sparse matrix functionality using a popular real dataset collection called the Deep Learning Matrix Collection (DLMC), which were used in recent studies [1, 2].
Performance benchmarks scripts for matrix-matrix and matrix-vector ops (dense-sparse, sparse-sparse, and compare to dense-dense) are implemented here.
matmul_bench.py with --operation sparse@sparse|sparse@dense is for Sparse matrix-matrix multiplication (SPMM) performance test. It can run in forward and backward mode with --backward_test, on CPU or CUDA with --with_cuda, using different datasets from the dataset collection DLMC. For more details see test.sh file.
matmul_bench.py with --operation sparse@vector is for Sparse matrix-vector multiplication (SPMV) performance test.
References:
Trevor Gale, Matei Zaharia, Cliff Young, Erich Elsen. Sparse GPU Kernels for Deep Learning. Proceedings of the International Conference for High Performance Computing, 2020. https://github.com/google-research/google-research/tree/master/sgk
Trevor Gale, Erich Elsen, Sara Hooker. The State of Sparsity in Deep Neural Networks. https://github.com/google-research/google-research/tree/master/state_of_sparsity