Comparing Parallel Functional Array Languages: Programming and Performance
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arXiv
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| Autori principali: | , , , , , , , , , , , , , , |
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| Natura: | Preprint |
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2025
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| author | van Balen, David De Matteis, Tiziano Grelck, Clemens Henriksen, Troels Hsu, Aaron W. Keller, Gabriele K. Koopman, Thomas McDonell, Trevor L. Oancea, Cosmin Scholz, Sven-Bodo Sinkarovs, Artjoms Smeding, Tom Trinder, Phil de Wolff, Ivo Gabe Ziogas, Alexandros Nikolaos |
| author_facet | van Balen, David De Matteis, Tiziano Grelck, Clemens Henriksen, Troels Hsu, Aaron W. Keller, Gabriele K. Koopman, Thomas McDonell, Trevor L. Oancea, Cosmin Scholz, Sven-Bodo Sinkarovs, Artjoms Smeding, Tom Trinder, Phil de Wolff, Ivo Gabe Ziogas, Alexandros Nikolaos |
| contents | Parallel functional array languages are an emerging class of programming languages that promise to combine low-effort parallel programming with good performance and performance portability. We systematically compare the designs and implementations of five different functional array languages: Accelerate, APL, DaCe, Futhark, and SaC. We demonstrate the expressiveness of functional array programming by means of four challenging benchmarks, namely N-body simulation, MultiGrid, Quickhull, and Flash Attention. These benchmarks represent a range of application domains and parallel computational models. We argue that the functional array code is much shorter and more comprehensible than the hand-optimized baseline implementations because it omits architecture-specific aspects. Instead, the language implementations generate both multicore and GPU executables from a single source code base. Hence, we further argue that functional array code could more easily be ported to, and optimized for, new parallel architectures than conventional implementations of numerical kernels. We demonstrate this potential by reporting the performance of the five parallel functional array languages on a total of 39 instances of the four benchmarks on both a 32-core AMD EPYC 7313 multicore system and on an NVIDIA A30 GPU. We explore in-depth why each language performs well or not so well on each benchmark and architecture. We argue that the results demonstrate that mature functional array languages have the potential to deliver performance competitive with the best available conventional techniques. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08906 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Comparing Parallel Functional Array Languages: Programming and Performance van Balen, David De Matteis, Tiziano Grelck, Clemens Henriksen, Troels Hsu, Aaron W. Keller, Gabriele K. Koopman, Thomas McDonell, Trevor L. Oancea, Cosmin Scholz, Sven-Bodo Sinkarovs, Artjoms Smeding, Tom Trinder, Phil de Wolff, Ivo Gabe Ziogas, Alexandros Nikolaos Programming Languages Distributed, Parallel, and Cluster Computing Performance Parallel functional array languages are an emerging class of programming languages that promise to combine low-effort parallel programming with good performance and performance portability. We systematically compare the designs and implementations of five different functional array languages: Accelerate, APL, DaCe, Futhark, and SaC. We demonstrate the expressiveness of functional array programming by means of four challenging benchmarks, namely N-body simulation, MultiGrid, Quickhull, and Flash Attention. These benchmarks represent a range of application domains and parallel computational models. We argue that the functional array code is much shorter and more comprehensible than the hand-optimized baseline implementations because it omits architecture-specific aspects. Instead, the language implementations generate both multicore and GPU executables from a single source code base. Hence, we further argue that functional array code could more easily be ported to, and optimized for, new parallel architectures than conventional implementations of numerical kernels. We demonstrate this potential by reporting the performance of the five parallel functional array languages on a total of 39 instances of the four benchmarks on both a 32-core AMD EPYC 7313 multicore system and on an NVIDIA A30 GPU. We explore in-depth why each language performs well or not so well on each benchmark and architecture. We argue that the results demonstrate that mature functional array languages have the potential to deliver performance competitive with the best available conventional techniques. |
| title | Comparing Parallel Functional Array Languages: Programming and Performance |
| topic | Programming Languages Distributed, Parallel, and Cluster Computing Performance |
| url | https://arxiv.org/abs/2505.08906 |