期刊
SIAM JOURNAL ON SCIENTIFIC COMPUTING
卷 34, 期 4, 页码 C170-C191出版社
SIAM PUBLICATIONS
DOI: 10.1137/110848244
关键词
parallel computing; numerical linear algebra; sparse matrix-matrix multiplication; SpGEMM; sparse matrix indexing; sparse matrix assignment; two-dimensional data decomposition; hypersparsity; graph algorithms; sparse SUMMA; subgraph extraction; graph contraction; graph batch update
资金
- NSF [CNS-0709385]
- Intel Corporation
- Microsoft Corporation
- ASCR Office in the DOE Office of Science [DE-AC02-05CH11231]
Generalized sparse matrix-matrix multiplication (or SpGEMM) is a key primitive for many high performance graph algorithms as well as for some linear solvers, such as algebraic multigrid. Here we show that SpGEMM also yields efficient algorithms for general sparse-matrix indexing in distributed memory, provided that the underlying SpGEMM implementation is sufficiently flexible and scalable. We demonstrate that our parallel SpGEMM methods, which use two-dimensional block data distributions with serial hypersparse kernels, are indeed highly flexible, scalable, and memory-efficient in the general case. This algorithm is the first to yield increasing speedup on an unbounded number of processors; our experiments show scaling up to thousands of processors in a variety of test scenarios.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据