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An Improved Sparse Matrix-Vector Multiply Based on Recursive Sparse Blocks Layout

  • Michele Martone
  • Marcin Paprzycki
  • Salvatore Filippone
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7116)

Abstract

The Recursive Sparse Blocks (RSB) is a sparse matrix layout designed for coarse grained parallelism and reduced cache misses when operating with matrices, which are larger than a computer’s cache. By laying out the matrix in sparse, non overlapping blocks, we allow for the shared memory parallel execution of transposed SParse Matrix-Vector multiply (SpMV), with higher efficiency than the traditional Compressed Sparse Rows (CSR) format. In this note we cover two issues. First, we propose two improvements to our original approach. Second, we look at the performance of standard and transposed shared memory parallel SpMV for unsymmetric matrices, using the proposed approach. We find that our implementation’s performance is competitive with that of both the highly optimized, proprietary Intel MKL Sparse BLAS library’s CSR routines, and the Compressed Sparse Blocks (CSB) research prototype.

Keywords

Shared Memory Sparse Matrix Sparse Matrice Potential Parallelism Coarse Grained Parallelism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

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    Martone, M., Filippone, S., Paprzycki, M., Tucci, S.: About the assembly of recursive sparse matrices. In: Proceedings of the International Multiconference on Computer Science and Information Technology, Wisła. Poland, pp. 317–325. IEEE Computer Society Press, Los Alamitos (2010)Google Scholar
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    Martone, M., Filippone, S., Paprzycki, M., Tucci, S.: On BLAS operations with recursively stored sparse matrices. In: Proceedings of the International Symposium on Symbolic and Numeric Algorithms for Scientific Computing. Timisoara, Romania, pp. 49–56. IEEE (September 2010)Google Scholar
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    The University of Florida Sparse Matrix Collection, http://www.cise.ufl.edu/research/sparse/matrices

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Michele Martone
    • 1
  • Marcin Paprzycki
    • 2
  • Salvatore Filippone
    • 1
  1. 1.University of Rome “Tor Vergata”RomeItaly
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

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