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| Title | Flexible Batched Sparse Matrix-Vector Product on GPUs |
| Publication Type | Conference Paper |
| Year of Publication | 2017 |
| Authors | Anzt, H., G. Collins, J. Dongarra, G. Flegar, and E. S. Quintana-Orti |
| Conference Name | 8th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA '17) |
| Date Published | 2017-11 |
| Publisher | ACM Press |
| Conference Location | Denver, CO |
| Abstract | We propose a variety of batched routines for concurrently processing a large collection of small-size, independent sparse matrix-vector products (SpMV) on graphics processing units (GPUs). These batched SpMV kernels are designed to be flexible in order to handle a batch of matrices which differ in size, nonzero count, and nonzero distribution. Furthermore, they support three most commonly used sparse storage formats: CSR, COO and ELL. Our experimental results on a state-of-the-art GPU reveal performance improvements of up to 25X compared to non-batched SpMV routines. |
| DOI | 10.1145/3148226.3148230 |
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