Computer Science > Programming Languages

arXiv:2505.08906 (cs) [Submitted on 13 May 2025]

Title:Comparing Parallel Functional Array Languages: Programming and Performance

Authors:David van Balen, Tiziano De Matteis, Clemens Grelck, Troels Henriksen, Aaron W. Hsu, Gabriele K. Keller, Thomas Koopman, Trevor L. McDonell, Cosmin Oancea, Sven-Bodo Scholz, Artjoms Sinkarovs, Tom Smeding, Phil Trinder, Ivo Gabe de Wolff, Alexandros Nikolaos Ziogas View a PDF of the paper titled Comparing Parallel Functional Array Languages: Programming and Performance, by David van Balen and 13 other authors View PDF

Abstract:并行函数式数组语言是一类新兴的编程语言，有望将低成本的并行编程与良好的性能和性能可移植性结合起来。我们系统地比较了五种不同的函数式数组语言的设计和实现：Accelerate, APL, DaCe, Futhark, 和 SaC。我们通过四个具有挑战性的基准测试来展示函数式数组编程的表达能力，即 N-body simulation, MultiGrid, Quickhull, 和 Flash Attention。这些基准测试代表了一系列应用领域和并行计算模型。我们认为，函数式数组代码比手工优化的基线实现更短、更易于理解，因为它省略了特定于架构的方面。相反，语言实现从单个源代码库生成多核和 GPU 可执行文件。因此，我们进一步认为，与数值内核的传统实现相比，函数式数组代码可以更容易地移植到新的并行架构并针对其进行优化。我们通过报告五种并行函数式数组语言在32核 AMD EPYC 7313 多核系统和 NVIDIA A30 GPU 上，四个基准测试的共 39 个实例上的性能来证明这种潜力。我们深入探讨了每种语言在每个基准测试和架构上表现良好或不佳的原因。我们认为，结果表明，成熟的函数式数组语言有潜力提供与最佳可用传统技术相媲美的性能。 Subjects: | Programming Languages (cs.PL); Distributed, Parallel, and Cluster Computing (cs.DC); Performance (cs.PF) ---|--- Cite as: | arXiv:2505.08906 [cs.PL] (or arXiv:2505.08906v1 [cs.PL] for this version) https://doi.org/10.48550/arXiv.2505.08906 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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From: Cosmin Oancea [view email] [v1] Tue, 13 May 2025 18:54:36 UTC (1,285 KB) Full-text links:

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