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.
Download conference paper PDF
References
Garland, M., Kirk, D.B.: Understanding Throughput-oriented Architectures. Commun. ACM 53, 58–66 (2010)
OpenMP Application Programming Interface (2008)
NVIDIA. CUDA Programming Guide 4.0 (2011)
Khronos. The OpenCL Specification 1.1 (2010)
Bungartz, H.-J., Griebel, M.: Sparse Grids. Acta Numerica 13(-1), 147–269 (2004)
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)
MAGMA, Matrix Algebra on GPU and Multicore Architectures, http://icl.cs.utk.edu/magma/index.html
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)
Osman, A., Ammar, H.: Dynamic Load Balancing Strategies for Parallel Computers. In: ISPDC, Romania (July 2002)
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)
Intel. Intel Advanced Vector Extensions Programming Reference (2011)
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Muraraşu, A., Weidendorfer, J., Bode, A. (2012). Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29740-3_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-29740-3_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29739-7
Online ISBN: 978-3-642-29740-3
eBook Packages: Computer ScienceComputer Science (R0)
