作者
Rafael Ferreira Da Silva, Rosa Badia, Venkat Bala, Deborah Bard, Peer-Timo Bremer, Ian Buckley, Silvina Caino-Lores, Kyle Chard, Carole Goble, Shantenu Jha, Daniel S Katz, Daniel Laney, Manish Parashar, Fred Suter, Nick Tyler, Thomas Uram, Ilkay Altintas, Stefan Andersson, William Arndt, Juan Aznar, Jonathan Bader, Bartosz Balis, Christopher Blanton, Kelly Braghetto, Aharon Brodutch, Paul Brunk, Henri Casanova, Alba Lierta, Justin Chigu, Taina Coleman, Nick Collier, Iacopo Colonnelli, Frederik Coppens, Michael Crusoe, Will Cunningham, Bruno Kinoshita, Paolo Di Tomasso, Charles Doutriaux, Matthew Downton, Wael Elwasif, Bjoern Enders, Christopher Erdmann, Thomas Fahringer, Ludmilla Figueiredo, Rosa Filgueira, Martin Foltin, Anne Fouilloux, Luiz Gadelha, Andy Gallo, Artur Garcia, Daniel Garijo, Roman Gerlach, Ryan E Grant, Samuel Grayson, Patricia Grubel, Johan Gustafsson, Valerie Hayot, Oscar Hernandez Mendoza, Marcus Hilbrich, Annmary Justine, Ian Laflotte, Fabian Lehmann, Andre Luckow, Jakob Luettgau, Ketan Maheshwari, Motohiko Matsuda, Doriana Medic, Pete Mendygral, Marek Michalewicz, Jorji Nonaka, Maciej Pawlik, Loic Pottier, Line Pouchard, Mathias Putz, Santosh Radha, Lavanya Ramakrishnan, Sashko Ristov, Paul Romano, Daniel Rosendo, Martin Ruefenacht, Katarzyna Rycerz, Nishant Saurabh, Volodymyr Savchenko, Martin Schulz, Christine Simpson, Raul Sirvent, Tyler Skluzacek, Stian Reyes, Renan Santos Souza, Sreenivas R Sukumar, Ziheng Sun, Alan Sussman, Douglas Thain, Mikhail Titov, Benjamin Tovar, Aalap Tripathy, Matteo Turilli, Bartosz Tuznik, Hubertus van Dam, Aurelio Vivas, Logan Ward, Patrick Widener, Sean Wilkinson, Justyna Zawalska, Mahnoor Zulfiqar
发表日期
2023/3/1
期号
ORNL/TM-2023/2885
出版商
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
简介
Scientific workflows have become integral tools in broad scientific computing use cases. Science discovery is increasingly dependent on workflows to orchestrate large and complex scientific experiments that range from execution of a cloud-based data preprocessing pipeline to multi-facility instrument-to-edge-to-HPC computational workflows. Given the changing landscape of scientific computing (often referred to as a computing continuum) and the evolving needs of emerging scientific applications, it is paramount that the development of novel scientific workflows and system functionalities seek to increase the efficiency, resilience, and pervasiveness of existing systems and applications. Specifically, the proliferation of machine learning/artificial intelligence (ML/AI) workflows, need for processing large scale datasets produced by instruments at the edge, intensification of near real-time data processing, support for long-term experiment campaigns, and emergence of quantum computing as an adjunct to HPC, have significantly changed the functional and operational requirements of workflow systems. Workflow systems now need to, for example, support data streams from the edge-to-cloud-to-HPC enable the management of many small-sized files, allow data reduction while ensuring high accuracy, orchestrate distributed services (workflows, instruments, data movement, provenance, publication, etc.) across computing and user facilities, among others. Further, to accelerate science, it is also necessary that these systems implement specifications/standards and APIs for seamless (horizontal and vertical) integration between systems and …
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R Ferreira Da Silva, R Badia, V Bala, D Bard… - 2023