Name
Title | ||
|---|---|---|
SparseP: Efficient Sparse Matrix Vector Multiplication on Real Processing-In-Memory Architectures |
Abstract | ||
|---|---|---|
Sparse Matrix Vector Multiplication (SpMV) is one of the most thoroughly studied scientific computation kernels, be-cause it lies at the heart of many important applications from the scientific computing, machine learning, and graph analyt-ics domains. SpMV performs indirect memory references as a result of storing the sparse matrix in a compressed format, and irregular memory accesses to the input vector due to the spar-sity pattern of the input matrix [1]–[3]. Thus, in CPU and GPU systems, SpMV is a primarily memory-bandwidth-bound ker-nel for the majority of real sparse matrices, and is bottlenecked by data movement between memory and processors [3]–[6]. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/ISVLSI54635.2022.00063 | 2022 IEEE Computer Society Annual Symposium on VLSI (ISVLSI) |
Keywords | DocType | ISSN |
sparse matrices,processors,SparseP,efficient sparse Matrix Vector Multiplication,Real Processing-In-Memory Architectures,SpMV,thoroughly studied scientific computation kernels,scientific computing,machine learning,analyt-ics domains,indirect memory references,irregular memory accesses,input matrix,primarily memory-bandwidth-bound ker-nel | Conference | 2159-3469 |
ISBN | Citations | PageRank |
978-1-6654-6606-6 | 0 | 0.34 |
References | Authors | |
59 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christina Giannoula | 1 | 0 | 0.34 |
| Ivan Fernandez | 2 | 0 | 0.34 |
| Juan Gómez-Luna | 3 | 22 | 3.88 |
| N. Koziris | 4 | 1015 | 107.53 |
| Georgios Goumas | 5 | 268 | 22.03 |
| Onur Mutlu | 6 | 9446 | 357.40 |