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Volumn 63, Issue 3, 2013, Pages 710-721

Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform

Author keywords

Cache optimization; CUDA; GPU; Matrix permutation; Sparse matrices vector multiplication

Indexed keywords

BANDWIDTH; BANDWIDTH COMPRESSION; BIOMIMETICS; CACHE MEMORY; DATA REDUCTION; GRAPHICS PROCESSING UNIT; GREEN COMPUTING; PROGRAM PROCESSORS; VECTORS;

EID: 84886727304     PISSN: 09208542     EISSN: 15730484     Source Type: Journal    
DOI: 10.1007/s11227-011-0626-0     Document Type: Article
Times cited : (18)

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    • Buatois, L.1    Caumon, G.2    Levy, B.3
  • 6
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    • GPGPU-aided ensemble empirical mode decomposition for EEG analysis during anaesthesia
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    • Model-driven autotuning of sparse matrix-vector multiply on CPUs
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    • Choi JW, Singh A, Vuduc RW (2010) Model-driven autotuning of sparse matrix-vector multiply on CPUs. ACM SIGPLAN Not 45(5):115-126
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  • 8
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    • Reducing the bandwidth of sparse symmetric matrices
    • Cuthill E, McKee J (1969) Reducing the bandwidth of sparse symmetric matrices. In: Proc 24th nat conf ACM, pp 157-172
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    • Bell N, Garland M (2009) Implementing sparse matrix-vector multiplication on throughput-oriented processors. In: Proc SC'09
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.