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Patterns for High Performance Multiscale Computing

delete2019-02-01
delete17
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OA
AI
S
Saad Alowayyed *
T
Tomasz Piontek
J
James L. Suter
O
O. Hoenen
D
Derek Groen
O
O. O. Luk
B
Bartosz Bosak
P
Piotr Kopta
K
Krzysztof Kurowski
O
O. F. J. Perks
K
Keeran Brabazon
J
Jancauskas, V.
D
D. Coster
P
Peter V. Coveney
A
Alfons G. Hoekstra
DOI:10.1016/j.future.2018.08.045delete
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摘要

摘要

En 中文
We describe our Multiscale Computing Patterns software for High Performance Multiscale Computing. Following a short review of Multiscale Computing Patterns, this paper introduces the Multiscale Computing Patterns Software, which consists of description, optimisation and execution components. First, the description component translates the task graph, representing a multiscale simulation, to a particular type of multiscale computing pattern. Second, the optimisation component selects and applies algorithms to find the most suitable mapping between submodels and available HPC resources. Third, the execution component which a middleware layer maps submodels to the number and type of physical resources based on the suggestions emanating from the optimisation part together with infrastructure-specific metrics such as queueing time and resource availability. The main purpose of the Multiscale Computing Patterns software is to leverage the Multiscale Computing Patterns to simplify and automate the execution of complex multiscale simulations on high performance computers, and to provide both application specific and pattern-specific performance optimisation. We test the performance and the resource usage for three multiscale models, which are expressed in terms of two Multiscale Computing Patterns. In doing so, we demonstrate how the software automates resource selection and load balancing, and delivers performance benefits from both the end-user and the HPC system level perspectives. (C) 2018 The Authors. Published by Elsevier B.V.
Keyword:
Multiscale computing
High performance computing
Modelling methodology
Distributed computing
Model coupling
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期刊

F
Future Generation Computer Systems-The International Journal of eScience
IF:
6.1
论文数:
6.9K
被引数:
2.3W

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P
Polish Academy of Sciences
学者数:
3.0W
论文数: 3.1W
被引数: 3.1W
U
university of amsterdam
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被引数: 94
P
poznan supercomputing & networking center
学者数:
65
论文数: 40
被引数: 0
M
Max Planck Society
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论文数: 7.7W
被引数: 3.3W
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