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arXiv 2021年

作者： Silva, Rafael Ferreira Da Casanova, Henri Chard, Kyle Laney, Dan Ahn, Dong Jha, Shantenu Goble, Carole Ramakrishnan, Lavanya Peterson, Luc Enders, Bjoern Thain, Douglas Altintas, Ilkay Babuji, Yadu Badia, Rosa M. Bonazzi, Vivien Coleman, Taina Crusoe, Michael Deelman, Ewa Natale, Frank Di Tommaso, Paolo Di Fahringer, Thomas Filgueira, Rosa Fursin, Grigori Ganose, Alex Gruning, Bjorn Katz, Daniel S. Kuchar, Olga Kupresanin, Ana Ludascher, Bertram Maheshwari, Ketan Mattoso, Marta Mehta, Kshitij Munson, Todd Ozik, Jonathan Peterka, Tom Pottier, Loic Randles, Tim Soiland-Reyes, Stian Tovar, Benjamin Turilli, Matteo Uram, Thomas Vahi, Karan Wilde, Michael Wolf, Matthew Wozniak, Justin University of Southern California United States University of Hawaii at Manoa United States University of Chicago United States Lawrence Livermore National Laboratory United States Rutgers University United States University of Manchester United Kingdom Lawrence Berkeley National Laboratory United States NERSC United States VU Amsterdam Netherlands University of Illinois at Urbana-Champaign United States Argonne National Laboratory United States Seqera Labs Spain University of Innsbruck Austria cTuning Foundation and cKnowledge Sas JPMorgan Applied Research Uni-Freiburg Germany Parallel Works Inc. University of Chicago United States University of Notre Dame United States UC San Diego United States Barcelona Supercomputing Center Spain Deloitte United States Oak Ridge National Laboratory United States Argonne Leadership Computing Facility Federal University of Rio de Janeiro Brazil Los Alamos National Lab United States

Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. 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 high-performance computing (HPC) platforms. These executions must be managed using some software infrastructure. Due to the popularity of workflows, workflow management systems (WMSs) have been developed to provide abstractions for creating and executing workflows conveniently, efficiently, and portably. While these efforts are all worthwhile, there are now hundreds of independent WMSs, many of which are moribund. As a result, the WMS landscape is segmented and presents significant barriers to entry due to the hundreds of seemingly comparable, yet incompatible, systems that exist. As a result, many teams, small and large, still elect to build their own custom workflow solution rather than adopt, or build upon, existing WMSs. This current state of the WMS landscape negatively impacts workflow users, developers, and researchers. The "Workflows Community Summit" was held online on January 13, 2021. The overarching goal of the summit was to develop a view of the state of the art and identify crucial research challenges in the workflow community. Prior to the summit, a survey sent to stakeholders in the workflow community (including both developers of WMSs and users of workflows) helped to identify key challenges in this community that were translated into 6 broad themes for the summit, each of them being the object of a focused discussion led by a volunteer member of the community. This report documents and organizes the wealth of information provided by the participants before, during, and after the summit. © 2021, CC BY-SA.

关键词： Work simplification

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