gpuSparse

Matlab mex wrappers to NVIDIA cuSPARSE (https://developer.nvidia.com/cusparse).

Uses int32 and single precision to save memory (Matlab sparse uses int64 and double).

Installation

1. Save in a folder called @gpuSparse on the Matlab path

2. A = gpuSparse('recompile') to trigger compilation of mex

3. Recommended: CUDA-11 for much faster transpose-multiply

Timings

Due to memory layout (row/col-major) multiply and transpose-multiply differ in performance.

size(A) = 221,401 x 213,331
nnz(A)  = 23,609,791 (0.05%)
AT      = precomputed transpose of A

CPU sparse
A*x  (sparse)   : Elapsed time is 1.370207 seconds.
AT*y (sparse)   : Elapsed time is 1.347447 seconds.
A'*y (sparse)   : Elapsed time is 0.267259 seconds.

GPU sparse
A*x  (gpuArray) : Elapsed time is 0.137195 seconds.
AT*y (gpuArray) : Elapsed time is 0.106331 seconds.
A'*y (gpuArray) : Elapsed time is 0.232057 seconds. (CUDA 11)
A'*y (gpuArray) : Elapsed time is 16.733638 seconds.

GPU gpuSparse
A*x  (gpuSparse): Elapsed time is 0.068451 seconds.
At*y (gpuSparse): Elapsed time is 0.063651 seconds.
A'*y (gpuSparse): Elapsed time is 0.059236 seconds. (CUDA 11)
A'*y (gpuSparse): Elapsed time is 3.094271 seconds.