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dc.contributor.authorJose M., Badia-
dc.contributor.authorAdrian, Amor-Martin-
dc.contributor.authorJose A., Belloch-
dc.date.accessioned2023-03-30T06:45:31Z-
dc.date.available2023-03-30T06:45:31Z-
dc.date.issued2023-
dc.identifier.urihttps://link.springer.com/article/10.1007/s11227-022-04975-6-
dc.identifier.urihttps://dlib.phenikaa-uni.edu.vn/handle/PNK/7332-
dc.descriptionCC BYvi
dc.description.abstractAchieving 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.vi
dc.language.isoenvi
dc.publisherSpringervi
dc.subjectCPUsvi
dc.subjectGPUsvi
dc.titleStrategies to parallelize a finite element mesh truncation technique on multi-core and many-core architecturesvi
dc.typeBookvi
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