Abstract

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 …

Metadata

publication

arXiv e-prints, arXiv: 2106.05177, 2021

year

2021

publication date

2021/6

authors

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 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

link

https://ui.adsabs.harvard.edu/abs/2021arXiv210605177F/abstract

journal

arXiv e-prints

pages

arXiv: 2106.05177