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Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations

  • Vasileios Karakasis
  • Georgios Goumas
  • Konstantinos Nikas
  • Nectarios Koziris
  • Juha Ruokolainen
  • Peter Råback
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7782)

Introduction

Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the analysis of multiple, simultaneously acting physical phenomena. These simulations often rely on Finite Element Methods (FEM) and the solution of large linear systems which, in turn, end up in multiple calls of the costly Sparse Matrix-Vector Multiplication (SpM×V) kernel. The major—and mostly inherent—performance problem of the this kernel is its very low flop:byte ratio, meaning that the algorithm must retrieve a significant amount of data from the memory hierarchy in order to perform a useful operation.

Keywords

Sparse Matrix Storage Format Total Execution Time Column Index Large Linear System 
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

  1. 1.
    Goumas, G., Kourtis, K., Anastopoulos, N., Karakasis, V., Koziris, N.: Performance evaluation of the sparse matrix-vector multiplication on modern architectures. The Journal of Supercomputing 50(1), 36–77 (2009)CrossRefGoogle Scholar
  2. 2.
    Kourtis, K., Karakasis, V., Goumas, G., Koziris, N.: CSX: An Extended Compression Format for SpMV on Shared Memory Systems. In: Proceedings of the 16th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming (PPoPP 2011), pp. 247–256. ACM, San Antonio (2011)Google Scholar
  3. 3.
    Lyly, M., Ruokolainen, J., Järvinen, E.: ELMER – a finite element solver for multiphysics. In: CSC Report on Scientific Computing (1999–2000)Google Scholar
  4. 4.
    Pinar, A., Heath, M.T.: Improving performance of sparse matrix-vector multiplication. In: Proceedings of the 1999 ACM/IEEE Conference on Supercomputing. ACM, Portland (1999)Google Scholar
  5. 5.
    Vuduc, R., Demmel, J.W., Yelick, K.A.: OSKI: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series 16(521) (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Vasileios Karakasis
    • 1
  • Georgios Goumas
    • 1
  • Konstantinos Nikas
    • 1
  • Nectarios Koziris
    • 1
  • Juha Ruokolainen
    • 2
  • Peter Råback
    • 2
  1. 1.National Technical University of AthensGreece
  2. 2.CSC - IT Center for Science Ltd.Finland

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