Multiple‐GPU parallelization of three‐dimensional material point method based on single‐root complex
As one of the arbitrary Lagrangian–Eulerian methods, the material point method (MPM) owns intrinsic advantages in simulation of large deformation problems by combining the merits of the Lagrangian and Eulerian approaches. Significant computational intensity is involved in the calculations of the MPM...
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| Vydáno v: | International journal for numerical methods in engineering Ročník 123; číslo 6; s. 1481 - 1504 |
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| Hlavní autoři: | , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Hoboken, USA
John Wiley & Sons, Inc
30.03.2022
Wiley Subscription Services, Inc |
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| ISSN: | 0029-5981, 1097-0207 |
| On-line přístup: | Získat plný text |
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| Abstract | As one of the arbitrary Lagrangian–Eulerian methods, the material point method (MPM) owns intrinsic advantages in simulation of large deformation problems by combining the merits of the Lagrangian and Eulerian approaches. Significant computational intensity is involved in the calculations of the MPM due to its very fine mesh needed to achieve a sufficiently high accuracy. A new multiple‐GPU parallel strategy is developed based on a single‐root complex architecture of the computer purely within a CUDA environment. Peer‐to‐Peer (P2P) communication between the GPUs is performed to exchange the information of the crossing particles and ghost element nodes, which is faster than the heavy send/receive operations between different computers through the infiniBand network. Domain decomposition is performed to split the whole computational task over the GPUs with a number of subdomains. The computations within each subdomain are allocated on a corresponding GPU using an enhanced “Particle‐List” scheme to tackle the data race during the interpolation from associated particles to common nodes. The acceleration effect of the parallelization is evaluated with two benchmarks cases, mini‐slump test after a dam break and cone penetration test in clay, where the maximum speedups with 1 and 8 GPUs are 88 and 604, respectively. |
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| AbstractList | As one of the arbitrary Lagrangian–Eulerian methods, the material point method (MPM) owns intrinsic advantages in simulation of large deformation problems by combining the merits of the Lagrangian and Eulerian approaches. Significant computational intensity is involved in the calculations of the MPM due to its very fine mesh needed to achieve a sufficiently high accuracy. A new multiple‐GPU parallel strategy is developed based on a single‐root complex architecture of the computer purely within a CUDA environment. Peer‐to‐Peer (P2P) communication between the GPUs is performed to exchange the information of the crossing particles and ghost element nodes, which is faster than the heavy send/receive operations between different computers through the infiniBand network. Domain decomposition is performed to split the whole computational task over the GPUs with a number of subdomains. The computations within each subdomain are allocated on a corresponding GPU using an enhanced “Particle‐List” scheme to tackle the data race during the interpolation from associated particles to common nodes. The acceleration effect of the parallelization is evaluated with two benchmarks cases, mini‐slump test after a dam break and cone penetration test in clay, where the maximum speedups with 1 and 8 GPUs are 88 and 604, respectively. |
| Author | Dong, Youkou Zhang, Xue Cui, Lan |
| Author_xml | – sequence: 1 givenname: Youkou orcidid: 0000-0002-7354-6464 surname: Dong fullname: Dong, Youkou organization: Dalian University of Technology – sequence: 2 givenname: Lan surname: Cui fullname: Cui, Lan email: lcui@whrsm.ac.cn organization: Institute of Rock and Soil Mechanics, Chinese Academy of Sciences – sequence: 3 givenname: Xue orcidid: 0000-0002-0892-3665 surname: Zhang fullname: Zhang, Xue organization: University of Liverpool |
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| SubjectTerms | cone penetration test Cone penetration tests Finite element method Interpolation material point method mini slump test Nodes parallel computation |
| Title | Multiple‐GPU parallelization of three‐dimensional material point method based on single‐root complex |
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