Data-driven mixed precision sparse matrix vector multiplication for GPUs

Abstract

We optimize Sparse Matrix Vector multiplication (SpMV) using a mixed precision strategy (MpSpMV) for Nvidia V100 GPUs. The approach has three benefits: (1) It reduces computation time, (2) it reduces the size of the input matrix and therefore reduces data movement, and (3) it provides an opportunity for increased parallelism. MpSpMV’s decision to lower to single precision is data driven, based on individual nonzero values of the sparse matrix. On all real-valued matrices from the Sparse Matrix Collection, we obtain a maximum speedup of 2.61× and average speedup of 1.06× over double precision, while maintaining higher accuracy compared to single precision.