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Title: Brief announcement: Hypergraph parititioning for parallel sparse matrix-matrix multiplication

The performance of parallel algorithms for sparse matrix-matrix multiplication is typically determined by the amount of interprocessor communication performed, which in turn depends on the nonzero structure of the input matrices. In this paper, we characterize the communication cost of a sparse matrix-matrix multiplication algorithm in terms of the size of a cut of an associated hypergraph that encodes the computation for a given input nonzero structure. Obtaining an optimal algorithm corresponds to solving a hypergraph partitioning problem. Furthermore, our hypergraph model generalizes several existing models for sparse matrix-vector multiplication, and we can leverage hypergraph partitioners developed for that computation to improve application-specific algorithms for multiplying sparse matrices.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  2. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  3. Univ. of California, Berkeley, CA (United States)
  4. Hebrew Univ., Jerusalem (Israel)
Publication Date:
OSTI Identifier:
1303161
Report Number(s):
SAND--2016-4718J
640462
Grant/Contract Number:
AC04-94AL85000
Type:
Accepted Manuscript
Journal Name:
ACM Transactions on Parallel Computing
Additional Journal Information:
Journal Name: ACM Transactions on Parallel Computing
Research Org:
Sandia National Laboratories (SNL-CA), Livermore, CA (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING