Accelerating sparse matrix-vector multiplication on GPUs using bit-representation-optimized schemes

The sparse matrix-vector (SpMV) multiplication routine is an important building block used in many iterative algorithms for solving scientific and engineering problems. One of the main challenges of SpMV is its memory-boundedness. Although compression has been proposed previously to improve SpMV per...

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Veröffentlicht in:	2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC) S. 1 - 12
Hauptverfasser:	Tang, Wai Teng, Tan, Wen Jun, Ray, Rajarshi, Wong, Yi Wen, Chen, Weiguang, Kuo, Shyh-hao, Goh, Rick Siow Mong, Turner, Stephen John, Wong, Weng-Fai
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	New York, NY, USA ACM 17.11.2013
Schriftenreihe:	ACM Conferences
Schlagworte:	Abstracts Acceleration Computer systems organization > Architectures > Parallel architectures > Multiple instruction, multiple data Computing methodologies > Parallel computing methodologies > Parallel programming languages data compression Educational institutions GPU Indexes Instruction sets Mathematics of computing > Information theory Mathematics of computing > Information theory > Coding theory matrix-vector multiplication memory bandwidth Optimization parallelism Security and privacy > Cryptography > Mathematical foundations of cryptography Software and its engineering > Software notations and tools > General programming languages > Language types > Parallel programming languages Sparse matrix format memory bandwidth data compression parallelism sparse matrix format compression GPU matrix-vector multiplication
ISBN:	9781450323789, 1450323782
ISSN:	2167-4329
Online-Zugang:	Volltext
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