Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation

  • Alin Muraraşu
  • Josef Weidendorfer
  • Arndt Bode
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7156)

Abstract

Multi-core parallelism and accelerators are becoming common features of today’s computer systems, as they allow for computational power without sacrificing energy efficiency. Due to heterogeneity, tuning for each type of compute unit and adequate load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression. Its performance critical part is the interpolation routine used for decompression. Results show that our load balancing scheme allows for an efficient acceleration of interpolation on heterogeneous systems containing multi-core CPUs and GPUs.

Keywords

Execution Time Load Balance Heterogeneous System Sparse Grid Dynamic Task 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Garland, M., Kirk, D.B.: Understanding Throughput-oriented Architectures. Commun. ACM 53, 58–66 (2010)CrossRefGoogle Scholar
  2. 2.
    OpenMP Application Programming Interface (2008)Google Scholar
  3. 3.
    NVIDIA. CUDA Programming Guide 4.0 (2011)Google Scholar
  4. 4.
    Khronos. The OpenCL Specification 1.1 (2010)Google Scholar
  5. 5.
    Bungartz, H.-J., Griebel, M.: Sparse Grids. Acta Numerica 13(-1), 147–269 (2004)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Murarasu, A.F., Weidendorfer, J., Buse, G., Butnaru, D., Pflüger, D.: Compact Data Structure and Scalable Algorithms for the Sparse Grid Technique. In: PPOPP, pp. 25–34 (2011)Google Scholar
  7. 7.
    MAGMA, Matrix Algebra on GPU and Multicore Architectures, http://icl.cs.utk.edu/magma/index.html
  8. 8.
    Augonnet, C., Thibault, S., Namyst, R., Wacrenier, P.-A.: StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 863–874. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  9. 9.
    Osman, A., Ammar, H.: Dynamic Load Balancing Strategies for Parallel Computers. In: ISPDC, Romania (July 2002)Google Scholar
  10. 10.
    Butnaru, D., Pflüger, D., Bungartz, H.-J.: Towards High-Dimensional Computational Steering of Precomputed Simulation Data using Sparse Grids. Procedia CS 4, 56–65 (2011)CrossRefGoogle Scholar
  11. 11.
    Intel. Intel Advanced Vector Extensions Programming Reference (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alin Muraraşu
    • Josef Weidendorfer
      • Arndt Bode

        There are no affiliations available

        Personalised recommendations