NPDP benchmark suite for the evaluation of the effectiveness of automatic optimizing compilers

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Parallel Computing Pub Date : 2023-07-01 DOI:10.1016/j.parco.2023.103016

Marek Palkowski, Wlodzimierz Bielecki

引用次数: 1

Abstract

The paper presents a benchmark suite of ten non-serial polyadic dynamic programming (NPDP) kernels, which are designed to test the efficiency of tiled code generated by polyhedral optimization compilers. These kernels are mainly derived from bioinformatics algorithms, which pose a significant challenge for automatic loop nest tiling transformations. The paper describes algorithms implemented with examined kernels and unifies them in the form of loop nests presented in the C language. The purpose is to reconsider the execution and monitoring of codes, typically used in past and current publications. For carrying out experiments with introduced benchmarks, we applied the two source-to-source compilers, PLuTo and TRACO, to generate cache-efficient codes and analyzed their performance on four multi-core machines. We discuss the limitations of well-known tiling approaches and outline future tiling strategies to generate effective tiled code by means of optimizing compilers for introduced benchmarks.

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NPDP基准套件，用于评估自动优化编译器的有效性

本文提出了一套由十个非串行的多元动态编程（NPDP）内核组成的基准套件，旨在测试多面体优化编译器生成的平铺代码的效率。这些内核主要来自生物信息学算法，这对自动循环嵌套平铺转换提出了重大挑战。本文描述了用检查过的内核实现的算法，并以C语言中的循环嵌套形式将它们统一起来。其目的是重新考虑过去和当前出版物中通常使用的代码的执行和监控。为了用引入的基准测试进行实验，我们应用了两个源到源编译器PLuTo和TRACO来生成高效缓存的代码，并分析了它们在四台多核机器上的性能。我们讨论了众所周知的平铺方法的局限性，并概述了未来的平铺策略，通过为引入的基准优化编译器来生成有效的平铺代码。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Parallel Computing 工程技术-计算机：理论方法

CiteScore

3.50

自引率

7.10%

发文量

审稿时长

4.5 months

期刊介绍： Parallel Computing is an international journal presenting the practical use of parallel computer systems, including high performance architecture, system software, programming systems and tools, and applications. Within this context the journal covers all aspects of high-end parallel computing from single homogeneous or heterogenous computing nodes to large-scale multi-node systems. Parallel Computing features original research work and review articles as well as novel or illustrative accounts of application experience with (and techniques for) the use of parallel computers. We also welcome studies reproducing prior publications that either confirm or disprove prior published results. Particular technical areas of interest include, but are not limited to: -System software for parallel computer systems including programming languages (new languages as well as compilation techniques), operating systems (including middleware), and resource management (scheduling and load-balancing). -Enabling software including debuggers, performance tools, and system and numeric libraries. -General hardware (architecture) concepts, new technologies enabling the realization of such new concepts, and details of commercially available systems -Software engineering and productivity as it relates to parallel computing -Applications (including scientific computing, deep learning, machine learning) or tool case studies demonstrating novel ways to achieve parallelism -Performance measurement results on state-of-the-art systems -Approaches to effectively utilize large-scale parallel computing including new algorithms or algorithm analysis with demonstrated relevance to real applications using existing or next generation parallel computer architectures. -Parallel I/O systems both hardware and software -Networking technology for support of high-speed computing demonstrating the impact of high-speed computation on parallel applications

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