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Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development
arXiv - CS - Distributed, Parallel, and Cluster Computing Pub Date : 2021-06-09 , DOI: arxiv-2106.05177
Rafael Ferreira da Silva, Henri Casanova, Kyle Chard, Tainã Coleman, Dan Laney, Dong Ahn, Shantenu Jha, Dorran Howell, Stian Soiland-Reys, Ilkay Altintas, Douglas Thain, Rosa Filgueira, Yadu Babuji, Rosa M. Badia, Bartosz Balis, Silvina Caino-Lores, Scott Callaghan, Frederik Coppens, Michael R. Crusoe, Kaushik De, Frank Di Natale, Tu M. A. Do, Bjoern Enders, Thomas Fahringer, Anne Fouilloux, Grigori Fursin, Alban Gaignard, Alex Ganose, Daniel Garijo, Sandra Gesing, Carole Goble, Adil Hasan, Sebastiaan Huber, Daniel S. Katz, Ulf Leser, Douglas Lowe, Bertram Ludaescher, Ketan Maheshwari, Maciej Malawski, Rajiv Mayani, Kshitij Mehta, Andre Merzky, Todd Munson, Jonathan Ozik, Loïc Pottier, Sashko Ristov, Mehdi Roozmeh, Renan Souza, Frédéric Suter, Benjamin Tovar, Matteo Turilli, Karan Vahi, Alvaro Vidal-Torreira, Wendy Whitcup, Michael Wilde, Alan Williams, Matthew Wolf, Justin Wozniak

Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and thus must execute on a wide range of large-scale platforms, from large clouds to upcoming exascale HPC platforms. Workflows will play a crucial role in the data-oriented and post-Moore's computing landscape as they democratize the application of cutting-edge research techniques, computationally intensive methods, and use of new computing platforms. As workflows continue to be adopted by scientific projects and user communities, they are becoming more complex. Workflows are increasingly composed of tasks that perform computations such as short machine learning inference, multi-node simulations, long-running machine learning model training, amongst others, and thus increasingly rely on heterogeneous architectures that include CPUs but also GPUs and accelerators. The workflow management system (WMS) technology landscape is currently segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. Another fundamental problem is that there are conflicting theoretical bases and abstractions for a WMS. Systems that use the same underlying abstractions can likely be translated between, which is not the case for systems that use different abstractions. More information: https://workflowsri.org/summits/technical

中文翻译:

工作流社区峰会:推进最先进的科学工作流管理系统研究与开发

科学工作流是现代科学计算的基石,它们支撑了过去十年中一些最重要的发现。其中许多工作流具有很高的计算、存储和/或通信需求,因此必须在各种大型平台上执行,从大型云到即将推出的百亿亿次 HPC 平台。工作流将在面向数据和后摩尔的计算领域发挥关键作用,因为它们使尖端研究技术的应用、计算密集型方法和新计算平台的使用民主化。随着工作流继续被科学项目和用户社区采用,它们变得越来越复杂。工作流越来越多地由执行计算的任务组成,例如简短的机器学习推理、多节点模拟、长期运行的机器学习模型训练等,因此越来越依赖异构架构,包括 CPU 以及 GPU 和加速器。由于存在数百个看似可比但不兼容的系统,工作流管理系统 (WMS) 技术领域目前是细分的,并且存在巨大的进入壁垒。另一个基本问题是 WMS 存在相互冲突的理论基础和抽象。使用相同底层抽象的系统很可能可以相互转换,而使用不同抽象的系统则不然。更多信息:https://workflowsri.org/summits/technical 由于存在数百个看似可比但不兼容的系统,工作流管理系统 (WMS) 技术领域目前是细分的,并且存在巨大的进入壁垒。另一个基本问题是 WMS 存在相互冲突的理论基础和抽象。使用相同底层抽象的系统很可能可以相互转换,而使用不同抽象的系统则不然。更多信息:https://workflowsri.org/summits/technical 由于存在数百个看似可比但不兼容的系统,工作流管理系统 (WMS) 技术领域目前是细分的,并且存在巨大的进入壁垒。另一个基本问题是 WMS 存在相互冲突的理论基础和抽象。使用相同底层抽象的系统很可能可以相互转换,而使用不同抽象的系统则不然。更多信息:https://workflowsri.org/summits/technical 使用相同底层抽象的系统很可能可以相互转换,而使用不同抽象的系统则不然。更多信息:https://workflowsri.org/summits/technical 使用相同底层抽象的系统很可能可以相互转换,而使用不同抽象的系统则不然。更多信息:https://workflowsri.org/summits/technical
更新日期:2021-06-10
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