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Computer Science > Mathematical Software

arXiv:2204.12120 (cs)
[Submitted on 26 Apr 2022 (v1), last revised 27 Apr 2022 (this version, v2)]

Title:Automated Generation of High-Performance Computational Fluid Dynamics Codes

Authors:Sandra Macià, Pedro J. Martıínez-Ferrer, Eduard Ayguadé, Vicenç Beltran
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Abstract:Domain-Specific Languages (DSLs) improve programmers productivity by decoupling problem descriptions from algorithmic implementations. However, DSLs for High-Performance Computing (HPC) have two additional critical requirements: performance and scalability. This paper presents the automated process of generating, from abstract mathematical specifications of Computational Fluid Dynamics (CFD) problems, optimised parallel codes that perform and scale as manually optimised ones. We consciously combine within Saiph, a DSL for solving CFD problems, low-level optimisations and parallelisation strategies, enabling high-performance single-core executions which effectively scale to multi-core and distributed environments. Our results demonstrate how high-level DSLs can offer competitive performance by transparently leveraging state-of-the-art HPC techniques.
Comments: 30 pages, 18 figures. Postprint submitted to the Journal of Computational Science (Elsevier). Article updated with reviewers' comments, additional material in section 4.3 including figures and correction of typos
Subjects: Mathematical Software (cs.MS); Computational Engineering, Finance, and Science (cs.CE); Distributed, Parallel, and Cluster Computing (cs.DC)
ACM classes: D.1.3; J.2
Cite as: arXiv:2204.12120 [cs.MS]
  (or arXiv:2204.12120v2 [cs.MS] for this version)
  https://doi.org/10.48550/arXiv.2204.12120
Journal reference: Journal of Computational Science Volume 61, May 2022, 101664
Related DOI: https://doi.org/10.1016/j.jocs.2022.101664

Submission history

From: Sandra Macià [view email]
[v1] Tue, 26 Apr 2022 07:26:36 UTC (1,123 KB)
[v2] Wed, 27 Apr 2022 06:50:57 UTC (1,095 KB)
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