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International Conference on Computational Science and Its Applications

ICCSA 2013: Computational Science and Its Applications – ICCSA 2013 pp 211–223Cite as

Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7975))

Abstract

Sparse matrix-vector multiplication (SpMV) is an important operation in scientific and engineering computing. This paper presents optimization techniques for SpMV for the Compressed Row Storage (CRS) format on NVIDIA Kepler architecture GPUs using CUDA. Our implementation is based on an existing method proposed for the Fermi architecture, an earlier generation of the GPU, and takes advantage of some of the new features of the Kepler architecture. On a Tesla K20 Kepler architecture GPU on double precision operations, our implementation is, on average, approximately 1.29 times faster than that the Fermi optimized implementation for 200 different types of matrices. As a result, our implementation outperforms the NVIDIA cuSPARSE library’s CRS format SpMV in CUDA 5.0 on 174 of the 200 matrices, and the average speedup compared to the cuSPARSE SpMV routine across all 200 matrices is approximately 1.45.

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References

  1. Baskaran, M.M., Bordawekar, R.: Optimizing Sparse Matrix-Vector Multiplication on GPUs. IBM Research Report RC24704 (2009)

    Google Scholar 

  2. Bell, N., Garland, M.: Efficient Sparse Matrix-Vector Multiplication on CUDA. NVIDIA Technical Report NVR-2008-004 (2008)

    Google Scholar 

  3. NVIDIA Corporation: Whitepaper NVIDIAs Next Generation CUDA Compute Architecture: Kepler GK110. itepaper.pdf (2012), http://www.nvidia.com/content/PDF/kepler/NVIDIA-Kepler-GK110-Architecture-Wh

  4. Davis, J.D., Chung, E.S.: SpMV: A Memory-Bound Application on the GPU Stuck Between a Rock and a Hard Place. Microsoft Technical Report MSR–TR–2012–95 (2012)

    Google Scholar 

  5. Davis, T., Hu, Y.: The University of Florida Sparse Matrix Collection, http://www.cise.ufl.edu/research/sparse/matrices/

  6. El Zein, A.H., Rendell, A.P.: Generating Optimal CUDA Sparse Matrix Vector Product Implementations for Evolving GPU Hardware. Concurrency and Computation: Practice and Experience 24, 3–13 (2012)

    Article  Google Scholar 

  7. Feng, X., Jin, H., Zheng, R., Hu, K., Zeng, J., Shao, Z.: Optimization of Sparse Matrix-Vector Multiplication with Variant CSR on GPUs. In: Proc. IEEE 17th International Conference on Parallel and Distributed Systems (ICPADS 2011), pp. 165–172 (2011)

    Google Scholar 

  8. Guo, P., Wang, L.: Auto-Tuning CUDA Parameters for Sparse Matrix-Vector Multiplication on GPUs. In: Proc. International Conference on Computational and Information Sciences (ICCIS 2010), pp. 1154–1157 (2010)

    Google Scholar 

  9. Kubota, Y., Takahashi, D.: Optimization of Sparse Matrix-Vector Multiplication by Auto Selecting Storage Schemes on GPU. In: Murgante, B., Gervasi, O., Iglesias, A., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2011, Part II. LNCS, vol. 6783, pp. 547–561. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  10. Matam, K., Kothapalli, K.: Accelerating Sparse Matrix Vector Multiplication in Iterative Methods Using GPU. In: Proc. International Conference on Parallel Processing (ICPP 2011), pp. 612–621 (2011)

    Google Scholar 

  11. NVIDIA Corporation: cuSPARSE Library (included in CUDA Toolkit), https://developer.nvidia.com/cusparse

  12. Reguly, I., Giles, M.: Efficient sparse matrix-vector multiplication on cache-based GPUs. In: Proc. Innovative Parallel Computing: Foundations and Applications of GPU, Manycore, and Heterogeneous Systems (InPar 2012), pp. 1–12 (2012)

    Google Scholar 

  13. Xu, W., Zhang, H., Jiao, S., Wang, D., Song, F., Liu, Z.: Optimizing Sparse Matrix Vector Multiplication Using Cache Blocking Method on Fermi GPU. In: Proc. 13th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2012), pp. 231–235 (2012)

    Google Scholar 

  14. Yoshizawa, H., Takahashi, D.: Automatic Tuning of Sparse Matrix-Vector Multiplication for CRS format on GPUs. In: Proc. 15th IEEE International Conference on Computational Science and Engineering (CSE 2012), pp. 130–136 (2012)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Systems and Information Engineering, University of Tsukuba, Japan

    Daichi Mukunoki

  2. Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Daisuke Takahashi

Authors
  1. Daichi Mukunoki
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  2. Daisuke Takahashi
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Editor information

Editors and Affiliations

  1. L-I.S.U.T. - D.A.P.I.t. Facoltà Ingegneria, Università degli Studi della Basilicata, Viale dell’Ateneo Lucano, 10, 85100, Potenza, Italy

    Beniamino Murgante

  2. Covenant University, Canaanland OTA, Nigeria

    Sanjay Misra

  3. Partimento di Scienze e Tecnologie per LAgricoltura, le Foreste, la Natura e lEnergia, Università degli Studi della Tuscia, Via S. Camillo de Lellis, snc, 01100, Viterbo, Italy

    Maurizio Carlini

  4. Dipartimento di Scienze dell’Ingegneria Civile e dell’Architecttura, Politecnico di Bari, Via Orabona, 4, 70125, Bari, Italy

    Carmelo M. Torre

  5. International University VNU-HCM, Quarter 6, Linh Trung, Thu Duc, Ho Chi Minh City, Vietnam

    Hong-Quang Nguyen

  6. School of Business Systems, Monash University, 3800, Clayton, VIC, Australia

    David Taniar

  7. Department of Intelligent Informatics, Kyushu Sangyo University, 2-3-1 Matsukadai, 813-8503, Higashi-ku, Fukuoka, Japan

    Bernady O. Apduhan

  8. Department of Mathematics and Computer Science, University of Perugia, Via Vanvitelli, 1, 06123, Perugia, Italy

    Osvaldo Gervasi

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Mukunoki, D., Takahashi, D. (2013). Optimization of Sparse Matrix-Vector Multiplication for CRS Format on NVIDIA Kepler Architecture GPUs. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2013. ICCSA 2013. Lecture Notes in Computer Science, vol 7975. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39640-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-39640-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39639-7

  • Online ISBN: 978-3-642-39640-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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