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Title: Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.

Abstract

Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scienti c computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.

Authors:
 [1];  [1];  [1]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1429721
Report Number(s):
SAND-2017-13679J
659607
DOE Contract Number:
AC04-94AL85000
Resource Type:
Program Document
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Deveci, Mehmet, Rajamanickam, Sivasankaran, and Trott, Christian Robert. Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.. United States: N. p., 2017. Web.
Deveci, Mehmet, Rajamanickam, Sivasankaran, & Trott, Christian Robert. Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.. United States.
Deveci, Mehmet, Rajamanickam, Sivasankaran, and Trott, Christian Robert. Fri . "Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.". United States. doi:.
@article{osti_1429721,
title = {Multi-threaded Sparse Matrix-Matrix Multiplication for Many-Core and GPU Architectures.},
author = {Deveci, Mehmet and Rajamanickam, Sivasankaran and Trott, Christian Robert},
abstractNote = {Sparse Matrix-Matrix multiplication is a key kernel that has applications in several domains such as scienti c computing and graph analysis. Several algorithms have been studied in the past for this foundational kernel. In this paper, we develop parallel algorithms for sparse matrix-matrix multiplication with a focus on performance portability across different high performance computing architectures. The performance of these algorithms depend on the data structures used in them. We compare different types of accumulators in these algorithms and demonstrate the performance difference between these data structures. Furthermore, we develop a meta-algorithm, kkSpGEMM, to choose the right algorithm and data structure based on the characteristics of the problem. We show performance comparisons on three architectures and demonstrate the need for the community to develop two phase sparse matrix-matrix multiplication implementations for efficient reuse of the data structures involved.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Fri Dec 01 00:00:00 EST 2017},
month = {Fri Dec 01 00:00:00 EST 2017}
}

Program Document:
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