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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Beschreibung
Zusammenfassung: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 performance on CPUs, its use has not been demonstrated on the GPU because of the serial nature of many compression and decompression schemes. In this paper, we introduce a family of bit-representation-optimized (BRO) compression schemes for representing sparse matrices on GPUs. The proposed schemes, BRO-ELL, BRO-COO, and BRO-HYB, perform compression on index data and help to speed up SpMV on GPUs through reduction of memory traffic. Furthermore, we formulate a BRO-aware matrix reordering scheme as a data clustering problem and use it to increase compression ratios. With the proposed schemes, experiments show that average speedups of 1.5x compared to ELLPACK and HYB can be achieved for SpMV on GPUs.
ISBN:9781450323789
1450323782
ISSN:2167-4329
DOI:10.1145/2503210.2503234