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Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform
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  • Published: 07 June 2011

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

  • Shiming Xu1,
  • Wei Xue2 &
  • Hai Xiang Lin3 

The Journal of Supercomputing volume 63, pages 710–721 (2013)Cite this article

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Abstract

In this article, we discuss the performance modeling and optimization of Sparse Matrix-Vector Multiplication () on NVIDIA GPUs using CUDA. has a very low computation-data ratio and its performance is mainly bound by the memory bandwidth. We propose optimization of based on ELLPACK from two aspects: (1) enhanced performance for the dense vector by reducing cache misses, and (2) reduce accessed matrix data by index reduction. With matrix bandwidth reduction techniques, both cache usage enhancement and index compression can be enabled. For GPU with better cache support, we propose differentiated memory access scheme to avoid contamination of caches by matrix data. Performance evaluation shows that the combined speedups of proposed optimizations for GT-200 are 16% (single-precision) and 12.6% (double-precision) for GT-200 GPU, and 19% (single-precision) and 15% (double-precision) for GF-100 GPU.

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References

  1. Zone CUDA. http://www.nvidia.com/cuda

  2. decuda. http://wiki.github.com/laanwj/decuda

  3. GPGPU.org. http://www.gpgpu.org

  4. Belgin M, Back G, Ribbens C (2011) A library for pattern-based sparse matrix vector multiply. Intl J Parallel Program 39(1):62–67

    Article  Google Scholar 

  5. Buatois L, Caumon G, Levy B (2009) Concurrent number cruncher—a GPU implementation of a general sparse linear solver. Intl J of Parallel, Emergent and Distributed Systems 24(3):205–223

    Article  MathSciNet  Google Scholar 

  6. Chen D, Li D, Xiong M, Bao H, Li X (2010) GPGPU-aided ensemble empirical mode decomposition for EEG analysis during anaesthesia. IEEE Trans Inf Technol BioMed 14(6):1417–1427

    Article  Google Scholar 

  7. Choi JW, Singh A, Vuduc RW (2010) Model-driven autotuning of sparse matrix-vector multiply on CPUs. ACM SIGPLAN Not 45(5):115–126

    Article  Google Scholar 

  8. Cuthill E, McKee J (1969) Reducing the bandwidth of sparse symmetric matrices. In: Proc 24th nat conf ACM, pp 157–172

    Google Scholar 

  9. Kourtis K, Goumas G, Koziris N (2008) Optimizing sparse matrix-vector multiplication using index and value compression, pp 87–96

  10. Bell N, Garland M (2009) Implementing sparse matrix-vector multiplication on throughput-oriented processors. In: Proc SC’09

    Google Scholar 

  11. Vuduc RW (2002) Automatic performance tuning of sparse matrix kernels. PhD thesis, University of California, Berkeley, 2002

  12. Willcock J, Lumsdaine A (2006) Accelerating sparse matrix computations via data compression. In: Proc of the 20th annual intl conf on supercomputing, ICS ’06. ACM, New York, pp 307–316

    Chapter  Google Scholar 

  13. Williams S, Oliker L, Vuduc R, Shalf J, Yelick K, Demmel JW (2007) Optimization of sparse matrix-vector multiplication on emerging multicore platforms. In: Proc 2007 ACM/IEEE conference on supercomputing, SC ’07. ACM, New York, pp 38:1–38:12

    Google Scholar 

  14. Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. SIAM, Philadelphia

    Book  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Mekelweg 4, 2628 CD, Delft, The Netherlands

    Shiming Xu

  2. Tsinghua University, RM. 8-210, East Main Bldg., 100084, Beijing, China

    Wei Xue

  3. Mekelweg 4, 2628 CD, Delft, The Netherlands

    Hai Xiang Lin

Authors
  1. Shiming Xu
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  2. Wei Xue
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  3. Hai Xiang Lin
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Corresponding author

Correspondence to Shiming Xu.

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Open Access This is an open access article distributed under the terms of the Creative Commons Attribution Noncommercial License (https://creativecommons.org/licenses/by-nc/2.0), which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.

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Cite this article

Xu, S., Xue, W. & Lin, H.X. Performance modeling and optimization of sparse matrix-vector multiplication on NVIDIA CUDA platform. J Supercomput 63, 710–721 (2013). https://doi.org/10.1007/s11227-011-0626-0

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  • Published: 07 June 2011

  • Issue Date: March 2013

  • DOI: https://doi.org/10.1007/s11227-011-0626-0

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Keywords

  • Sparse matrices-vector multiplication
  • GPU
  • CUDA
  • Matrix permutation
  • Cache optimization
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