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Computing
Erschienen in: Computing 12/2018

31.03.2018

High performance iterative elemental product strategy in assembly-free FEM on GPU with improved occupancy

verfasst von: Nileshchandra K. Pikle, Shailesh R. Sathe, Arvind Y. Vyavahare

Erschienen in: Computing | Ausgabe 12/2018

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Abstract

The Matrix-vector products (MvPs) are computed either at element level or Degree-of-freedom (DoF) level in assembly-free Finite Element Method. The MvPs are mapped on GPU at element level or DoF level on per thread basis. Both strategies exploit the computing power of the GPU with cogent improvement in performance. However, these strategies suffer from poor global memory load/store efficiency. This paper proposes an efficient implementation of DoF based MvPs strategy using faster on-chip shared memory to store elemental matrices on GPU. Since the GPU has smaller shared memory size, MvPs are carried out iteratively in chunks to alleviate the poor occupancy issue. Performance of the iterative method is improved by two factors, first by coalesced access to global memory and second by improving the occupancy. Numerical experiments have shown that proposed iterative method outperforms the DoF based strategy approximately by factor 3.
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Metadaten
Titel
High performance iterative elemental product strategy in assembly-free FEM on GPU with improved occupancy
verfasst von
Nileshchandra K. Pikle
Shailesh R. Sathe
Arvind Y. Vyavahare
Publikationsdatum
31.03.2018
Verlag
Springer Vienna
Erschienen in
Computing / Ausgabe 12/2018
Print ISSN: 0010-485X
Elektronische ISSN: 1436-5057
DOI
https://doi.org/10.1007/s00607-018-0613-x

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