Strategies to parallelize a finite element mesh truncation technique on multi-core and many-core architectures
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Other documents of the author: Badia, Jose M.; Amor-Martin, Adrian; BELLOCH, JOSE A.; Garcia-Castillo, Luis Emilio
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Show full item recordcomunitat-uji-handle:10234/9
comunitat-uji-handle2:10234/7036
comunitat-uji-handle3:10234/8620
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Title
Strategies to parallelize a finite element mesh truncation technique on multi-core and many-core architecturesDate
2022-12-02Publisher
SpringerISSN
0920-8542; 1573-0484Bibliographic citation
BADIA, Jose M., et al. Strategies to parallelize a finite element mesh truncation technique on multi-core and many-core architectures. The Journal of Supercomputing, 79, 7648–7664 (2023).Type
info:eu-repo/semantics/articleVersion
info:eu-repo/semantics/publishedVersionSubject
Abstract
Achieving maximum parallel performance on multi-core CPUs and many-core GPUs is a challenging task depending on multiple factors. These include, for example, the number and granularity of the computations or the use ... [+]
Achieving maximum parallel performance on multi-core CPUs and many-core GPUs is a challenging task depending on multiple factors. These include, for example, the number and granularity of the computations or the use of the memories of the devices. In this paper, we assess those factors by evaluating and comparing different parallelizations of the same problem on a multiprocessor containing a CPU with 40 cores and four P100 GPUs with Pascal architecture. We use, as study case, the convolutional operation behind a non-standard finite element mesh truncation technique in the context of open region electromagnetic wave propagation problems. A total of six parallel algorithms implemented using OpenMP and CUDA have been used to carry out the comparison by leveraging the same levels of parallelism on both types of platforms. Three of the algorithms are presented for the first time in this paper, including a multi-GPU method, and two others are improved versions of algorithms previously developed by some of the authors. This paper presents a thorough experimental evaluation of the parallel algorithms on a radar cross-sectional prediction problem. Results show that performance obtained on the GPU clearly overcomes those obtained in the CPU, much more so if we use multiple GPUs to distribute both data and computations. Accelerations close to 30 have been obtained on the CPU, while with the multi-GPU version accelerations larger than 250 have been achieved. [-]
Funder Name
Gobierno de España | Generalitat Valenciana | Gobierno de la Comunidad de Madrid
Project code
PID2020-113656RB-C21 | PID2019-106455GB-C21 | PROMETEO/2019/109 | MIMACUHSPACE-CM-UC3M
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© The Author(s) 2022
info:eu-repo/semantics/openAccess
info:eu-repo/semantics/openAccess
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