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

Alin Muraraşu; Josef Weidendorfer; Arndt Bode

doi:10.1007/978-3-642-29740-3_39

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

Alin Muraraşu, Josef Weidendorfer, Arndt Bode

Research output: Chapter in Book/Report/Conference proceeding › Chapter

3 Scopus citations

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.

Original language	English (US)
Title of host publication	Euro-Par 2011: Parallel Processing Workshops
Publisher	Springer Nature
Pages	345-354
Number of pages	10
ISBN (Print)	9783642297397
DOIs	https://doi.org/10.1007/978-3-642-29740-3_39
State	Published - 2012
Externally published	Yes

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): UK-C0020
Acknowledgements: This publication is based on work supported by AwardNo. UK-C0020, made by King Abdullah University of Science and Technology(KAUST).
This publication acknowledges KAUST support, but has no KAUST affiliated authors.

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10.1007/978-3-642-29740-3_39

Cite this

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title = "Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation",

abstract = "Multi-core parallelism and accelerators are becoming common features of today{\textquoteright}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.",

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AU - Muraraşu, Alin

AU - Weidendorfer, Josef

AU - Bode, Arndt

N1 - KAUST Repository Item: Exported on 2020-10-01 Acknowledged KAUST grant number(s): UK-C0020 Acknowledgements: This publication is based on work supported by AwardNo. UK-C0020, made by King Abdullah University of Science and Technology(KAUST). This publication acknowledges KAUST support, but has no KAUST affiliated authors.

PY - 2012

Y1 - 2012

N2 - 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.

AB - 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.

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DO - 10.1007/978-3-642-29740-3_39

M3 - Chapter

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SP - 345

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BT - Euro-Par 2011: Parallel Processing Workshops

PB - Springer Nature

ER -

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

Abstract

Bibliographical note

UN SDGs

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