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The Journal of Supercomputing
Erschienen in: The Journal of Supercomputing 12/2020

09.03.2020

clusterCL: comprehensive support for multi-kernel data-parallel applications in heterogeneous asymmetric clusters

Erschienen in: The Journal of Supercomputing | Ausgabe 12/2020

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Abstract

Heterogeneous cluster systems consisting of CPUs and different kinds of accelerators have become mainstream in HPC. Programming such systems is a difficult task and requires addressing manifold challenges that stem from the intricate composition of such systems and peculiarities of scientific applications. A broad range of obstacles preventing efficient execution have to be considered and dealt with properly. In this paper, we propose a systematic approach and a framework that is capable of providing comprehensive support for running data-parallel applications in heterogeneous asymmetric clusters. Our implementation provides work partitioning and distribution by ensuring workload balance in the cluster while handling of partitioning-induced communication and synchronization in a transparent way. In our experimental section, we choose 11 representative scientific applications from different domains to evaluate our approach. Experimental results show a strong speedup and workload balance for different cluster configurations.
Vorheriger Artikel Opportunistic scheduling and resources consolidation system based on a new economic model
Nächster Artikel Efficient data aggregation with node clustering and extreme learning machine for WSN

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Metadaten
Titel
clusterCL: comprehensive support for multi-kernel data-parallel applications in heterogeneous asymmetric clusters
Publikationsdatum
09.03.2020
Erschienen in
The Journal of Supercomputing / Ausgabe 12/2020
Print ISSN: 0920-8542
Elektronische ISSN: 1573-0484
DOI
https://doi.org/10.1007/s11227-020-03234-w

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