Resource allocation for task-level speculative scientific applications: A proof of concept using Parallel Trajectory Splicing

IF 2 4区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS

Parallel Computing Pub Date : 2022-09-01 DOI:10.1016/j.parco.2022.102936

Andrew Garmon , Vinay Ramakrishnaiah , Danny Perez

引用次数: 1

Abstract

The constant increase in parallelism available on large-scale distributed computers poses major scalability challenges to many scientific applications. A common strategy to improve scalability is to express algorithms in terms of independent tasks that can be executed concurrently on a runtime system. In this manuscript, we consider a generalization of this approach where task-level speculation is allowed. In this context, a probability is attached to each task which corresponds to the likelihood that the output of the speculative task will be consumed as part of the larger calculation. We consider the problem of optimal resource allocation to each of the possible tasks so as to maximize the total expected computational throughput. The power of this approach is demonstrated by analyzing its application to Parallel Trajectory Splicing, a massively-parallel long-time-dynamics method for atomistic simulations.

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任务级思辨科学应用的资源分配:使用平行轨迹拼接的概念证明

大规模分布式计算机上可用的并行性的不断增加对许多科学应用程序提出了主要的可伸缩性挑战。提高可伸缩性的一种常用策略是用可以在运行时系统上并发执行的独立任务来表示算法。在这份手稿中，我们考虑了这种方法的推广，其中任务级猜测是允许的。在这种情况下，每个任务都附加了一个概率，该概率对应于投机任务的输出将作为较大计算的一部分被消耗的可能性。我们考虑对每个可能的任务进行最优资源分配的问题，以使总期望计算吞吐量最大化。通过分析该方法在并行轨迹拼接(一种用于原子模拟的大规模并行长时间动力学方法)中的应用，证明了该方法的有效性。

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