Comparing Parallel Functional Array Languages: Programming and Performance

TL;DR

This paper systematically compares five parallel functional array languages across diverse benchmarks, demonstrating their expressiveness, portability, and competitive performance on multicore and GPU architectures.

Contribution

It provides a comprehensive comparison of five functional array languages, highlighting their expressiveness, portability, and potential for competitive performance.

Findings

Functional array code is shorter and more comprehensible than hand-optimized implementations.

Languages can generate multicore and GPU executables from a single source.

Performance of languages is competitive with conventional techniques across benchmarks.

Abstract

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.