## Abstract

Sparse matrix multiplication is an important component of linear algebra computations. Implementing sparse matrix multiplication on an associative processor (AP) enables high level of parallelism, where a row of one matrix is multiplied in parallel with the entire second matrix, and where the execution time of vector dot product does not depend on the vector size. Four sparse matrix multiplication algorithms are explored in this paper, combining AP and baseline CPU processing to various levels. They are evaluated by simulation on a large set of sparse matrices. The computational complexity of sparse matrix multiplication on AP is shown to be an O(nnz) where nnz is the number of nonzero elements. The AP is found to be especially efficient in binary sparse matrix multiplication. AP outperforms conventional solutions in power efficiency.

Original language | English |
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Article number | 6954570 |

Pages (from-to) | 3175-3183 |

Number of pages | 9 |

Journal | IEEE Transactions on Parallel and Distributed Systems |

Volume | 26 |

Issue number | 11 |

DOIs | |

State | Published - 1 Nov 2015 |

Externally published | Yes |

### Bibliographical note

Publisher Copyright:© 2015 IEEE.

## Keywords

- Associative Processor
- In-Memory Computing
- Memory Intensive Computing
- SIMD
- Sparse Linear Algebra