Utilizing the Kokkos Framework for Scalable Application Parallelization

New, efficient parallel paradigms and models have been one of the main focuses of the research community in the domain of high-performance computing and multiprocessing systems. It is especially emphasized with the rise of many-core processors, such as graphics processing units. The development of parallel applications for different multi-core and many-core architectures becomes even harder, as it is usually needed to maintain different codebases. In this paper, we evaluate Kokkos, a new high-level parallel programming model and framework aimed at developing portable parallel programs for different architectures. We evaluated it using five different benchmark applications on the CPU and compared the results with OpenMP counterparts. The results and experiences are discussed in the paper.

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Publication Details

Published
2024-11-26
DOI
https://doi.org/10.1109/telfor63250.2024.10819073
Citations
2
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
0.70
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article

Utilizing the Kokkos Framework for Scalable Application Parallelization

Matija Dodović, A Paunovic, Marko Mišić
2 citations
Distributed and Parallel Computing Systems
0.70
article

Utilizing the Kokkos Framework for Scalable Application Parallelization

Matija Dodović, A Paunovic, Marko Mišić
article en
2 citations

Abstract

New, efficient parallel paradigms and models have been one of the main focuses of the research community in the domain of high-performance computing and multiprocessing systems. It is especially emphasized with the rise of many-core processors, such as graphics processing units. The development of parallel applications for different multi-core and many-core architectures becomes even harder, as it is usually needed to maintain different codebases. In this paper, we evaluate Kokkos, a new high-level parallel programming model and framework aimed at developing portable parallel programs for different architectures. We evaluated it using five different benchmark applications on the CPU and compared the results with OpenMP counterparts. The results and experiences are discussed in the paper.

University of Belgrade (RS)
Openalex Percentile: Top 25%
Distributed and Parallel Computing Systems
0.70
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