降低使用SimGrid开发分布式应用和平台的定制模拟器的入门门槛

IF 2 4区 计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS
Henri Casanova , Arnaud Giersch , Arnaud Legrand , Martin Quinson , Frédéric Suter
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引用次数: 0

摘要

并行和分布式计算(PDC)的研究人员经常求助于仿真,因为使用模拟器进行的实验可以用于任意的实验场景,比现实世界的实验节省资源、劳动力和时间,并且完全可重复和可观察。为了简化PDC模拟器的开发,已经开发了许多框架,这些框架提供了不同级别的准确性、可伸缩性、多功能性、可扩展性和可用性。20多年来,许多PDC研究人员使用SimGrid框架来制作各种各样的模拟器。它的流行是由于它非常强调准确性、可伸缩性和多功能性,尽管它在可扩展性和可用性方面存在缺点。尽管SimGrid为常见情况提供了合理的仿真模型,但用户很难扩展这些模型以满足特定领域的需求。此外,SimGrid只提供了相对低级的模拟抽象,使得复杂系统的模拟器的实现成为一项劳动密集型的工作。在这篇文章中,我们描述了在过去十年中为极大地提高可扩展性和可用性做出贡献的发展,从而降低或消除了用户开发自定义SimGrid模拟器的入门障碍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Lowering entry barriers to developing custom simulators of distributed applications and platforms with SimGrid
Researchers in parallel and distributed computing (PDC) often resort to simulation because experiments conducted using a simulator can be for arbitrary experimental scenarios, are less resource-, labor-, and time-consuming than their real-world counterparts, and are perfectly repeatable and observable. Many frameworks have been developed to ease the development of PDC simulators, and these frameworks provide different levels of accuracy, scalability, versatility, extensibility, and usability. The SimGrid framework has been used by many PDC researchers to produce a wide range of simulators for over two decades. Its popularity is due to a large emphasis placed on accuracy, scalability, and versatility, and is in spite of shortcomings in terms of extensibility and usability. Although SimGrid provides sensible simulation models for the common case, it was difficult for users to extend these models to meet domain-specific needs. Furthermore, SimGrid only provided relatively low-level simulation abstractions, making the implementation of a simulator of a complex system a labor-intensive undertaking. In this work we describe developments in the last decade that have contributed to vastly improving extensibility and usability, thus lowering or removing entry barriers for users to develop custom SimGrid simulators.
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来源期刊
Parallel Computing
Parallel Computing 工程技术-计算机:理论方法
CiteScore
3.50
自引率
7.10%
发文量
49
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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