Near-memory data transformation for efficient sparse matrix multi-vector multiplication
Daichi Fujiki, Niladrish Chatterjee, Donghyuk Lee, Mike O’Connor · 2019
Efficient manipulation of sparse matrices is critical to a wide range of HPC applications. Increasingly, GPUs are used to accelerate these sparse matrix operations. We study one common operation, Sparse Matrix Multi-Vector Multiplication (SpMM), and evaluate the impact of the sparsity, distribution of non-zero elements, and tile-traversal strategies on GPU implementations. Using these insights, we determine that operating on these sparse matrices in a Densified Compressed Sparse Row (DCSR) is well-suited to the parallel warp-synchronous execution model of the GPU processing elements.