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Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows

Fuente: arXiv

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Autori principali:	da Silva, Rafael Ferreira, Bard, Deborah, Chard, Kyle, de Witt, Shaun, Foster, Ian T., Gibbs, Tom, Goble, Carole, Godoy, William, Gustafsson, Johan, Haus, Utz-Uwe, Hudson, Stephen, Jha, Shantenu, Los, Laila, Paine, Drew, Suter, Frédéric, Ward, Logan, Wilkinson, Sean, Amaris, Marcos, Babuji, Yadu, Bader, Jonathan, Balin, Riccardo, Balouek, Daniel, Beecroft, Sarah, Belhajjame, Khalid, Bhattarai, Rajat, Brewer, Wes, Brunk, Paul, Caino-Lores, Silvina, Casanova, Henri, Cassol, Daniela, Coleman, Jared, Coleman, Taina, Colonnelli, Iacopo, Da Silva, Anderson Andrei, de Oliveira, Daniel, Elahi, Pascal, Elfaramawy, Nour, Elwasif, Wael, Etz, Brian, Fahringer, Thomas, Ferreira, Wesley, Filgueira, Rosa, Tande, Jacob Fosso, Gadelha, Luiz, Gallo, Andy, Garijo, Daniel, Georgiou, Yiannis, Gritsch, Philipp, Grubel, Patricia, Gueroudji, Amal, Guilloteau, Quentin, Hamalainen, Carlo, Enriquez, Rolando Hong, Huet, Lauren, Kesling, Kevin Hunter, Iborra, Paula, Jahangiri, Shiva, Janssen, Jan, Jordan, Joe, Kanwal, Sehrish, Kunstmann, Liliane, Lehmann, Fabian, Leser, Ulf, Li, Chen, Liu, Peini, Luettgau, Jakob, Lupat, Richard, Fernandez, Jose M., Maheshwari, Ketan, Malik, Tanu, Marquez, Jack, Matsuda, Motohiko, Medic, Doriana, Mohammadi, Somayeh, Mulone, Alberto, Navarro, John-Luke, Ng, Kin Wai, Noelp, Klaus, Kinoshita, Bruno P., Prout, Ryan, Crusoe, Michael R., Ristov, Sashko, Robila, Stefan, Rosendo, Daniel, Rowell, Billy, Rybicki, Jedrzej, Sanchez, Hector, Saurabh, Nishant, Saurav, Sumit Kumar, Scogland, Tom, Senanayake, Dinindu, Shin, Woong, Sirvent, Raul, Skluzacek, Tyler, Sly-Delgado, Barry, Soiland-Reyes, Stian, Souza, Abel, Souza, Renan, Talia, Domenico, Tallent, Nathan, Thamsen, Lauritz, Titov, Mikhail, Tovar, Benjamin, Vahi, Karan, Vardar-Irrgang, Eric, Vartina, Edite, Wang, Yuandou, Wouters, Merridee, Yu, Qi, Bkhetan, Ziad Al, Zulfiqar, Mahnoor
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Distributed, Parallel, and Cluster Computing
Accesso online:	Acceder al recurso
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_version_	1866916445822124032
author	da Silva, Rafael Ferreira Bard, Deborah Chard, Kyle de Witt, Shaun Foster, Ian T. Gibbs, Tom Goble, Carole Godoy, William Gustafsson, Johan Haus, Utz-Uwe Hudson, Stephen Jha, Shantenu Los, Laila Paine, Drew Suter, Frédéric Ward, Logan Wilkinson, Sean Amaris, Marcos Babuji, Yadu Bader, Jonathan Balin, Riccardo Balouek, Daniel Beecroft, Sarah Belhajjame, Khalid Bhattarai, Rajat Brewer, Wes Brunk, Paul Caino-Lores, Silvina Casanova, Henri Cassol, Daniela Coleman, Jared Coleman, Taina Colonnelli, Iacopo Da Silva, Anderson Andrei de Oliveira, Daniel Elahi, Pascal Elfaramawy, Nour Elwasif, Wael Etz, Brian Fahringer, Thomas Ferreira, Wesley Filgueira, Rosa Tande, Jacob Fosso Gadelha, Luiz Gallo, Andy Garijo, Daniel Georgiou, Yiannis Gritsch, Philipp Grubel, Patricia Gueroudji, Amal Guilloteau, Quentin Hamalainen, Carlo Enriquez, Rolando Hong Huet, Lauren Kesling, Kevin Hunter Iborra, Paula Jahangiri, Shiva Janssen, Jan Jordan, Joe Kanwal, Sehrish Kunstmann, Liliane Lehmann, Fabian Leser, Ulf Li, Chen Liu, Peini Luettgau, Jakob Lupat, Richard Fernandez, Jose M. Maheshwari, Ketan Malik, Tanu Marquez, Jack Matsuda, Motohiko Medic, Doriana Mohammadi, Somayeh Mulone, Alberto Navarro, John-Luke Ng, Kin Wai Noelp, Klaus Kinoshita, Bruno P. Prout, Ryan Crusoe, Michael R. Ristov, Sashko Robila, Stefan Rosendo, Daniel Rowell, Billy Rybicki, Jedrzej Sanchez, Hector Saurabh, Nishant Saurav, Sumit Kumar Scogland, Tom Senanayake, Dinindu Shin, Woong Sirvent, Raul Skluzacek, Tyler Sly-Delgado, Barry Soiland-Reyes, Stian Souza, Abel Souza, Renan Talia, Domenico Tallent, Nathan Thamsen, Lauritz Titov, Mikhail Tovar, Benjamin Vahi, Karan Vardar-Irrgang, Eric Vartina, Edite Wang, Yuandou Wouters, Merridee Yu, Qi Bkhetan, Ziad Al Zulfiqar, Mahnoor
author_facet	da Silva, Rafael Ferreira Bard, Deborah Chard, Kyle de Witt, Shaun Foster, Ian T. Gibbs, Tom Goble, Carole Godoy, William Gustafsson, Johan Haus, Utz-Uwe Hudson, Stephen Jha, Shantenu Los, Laila Paine, Drew Suter, Frédéric Ward, Logan Wilkinson, Sean Amaris, Marcos Babuji, Yadu Bader, Jonathan Balin, Riccardo Balouek, Daniel Beecroft, Sarah Belhajjame, Khalid Bhattarai, Rajat Brewer, Wes Brunk, Paul Caino-Lores, Silvina Casanova, Henri Cassol, Daniela Coleman, Jared Coleman, Taina Colonnelli, Iacopo Da Silva, Anderson Andrei de Oliveira, Daniel Elahi, Pascal Elfaramawy, Nour Elwasif, Wael Etz, Brian Fahringer, Thomas Ferreira, Wesley Filgueira, Rosa Tande, Jacob Fosso Gadelha, Luiz Gallo, Andy Garijo, Daniel Georgiou, Yiannis Gritsch, Philipp Grubel, Patricia Gueroudji, Amal Guilloteau, Quentin Hamalainen, Carlo Enriquez, Rolando Hong Huet, Lauren Kesling, Kevin Hunter Iborra, Paula Jahangiri, Shiva Janssen, Jan Jordan, Joe Kanwal, Sehrish Kunstmann, Liliane Lehmann, Fabian Leser, Ulf Li, Chen Liu, Peini Luettgau, Jakob Lupat, Richard Fernandez, Jose M. Maheshwari, Ketan Malik, Tanu Marquez, Jack Matsuda, Motohiko Medic, Doriana Mohammadi, Somayeh Mulone, Alberto Navarro, John-Luke Ng, Kin Wai Noelp, Klaus Kinoshita, Bruno P. Prout, Ryan Crusoe, Michael R. Ristov, Sashko Robila, Stefan Rosendo, Daniel Rowell, Billy Rybicki, Jedrzej Sanchez, Hector Saurabh, Nishant Saurav, Sumit Kumar Scogland, Tom Senanayake, Dinindu Shin, Woong Sirvent, Raul Skluzacek, Tyler Sly-Delgado, Barry Soiland-Reyes, Stian Souza, Abel Souza, Renan Talia, Domenico Tallent, Nathan Thamsen, Lauritz Titov, Mikhail Tovar, Benjamin Vahi, Karan Vardar-Irrgang, Eric Vartina, Edite Wang, Yuandou Wouters, Merridee Yu, Qi Bkhetan, Ziad Al Zulfiqar, Mahnoor
contents	The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.
format	Preprint
id	arxiv_https___arxiv_org_abs_2410_14943
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows da Silva, Rafael Ferreira Bard, Deborah Chard, Kyle de Witt, Shaun Foster, Ian T. Gibbs, Tom Goble, Carole Godoy, William Gustafsson, Johan Haus, Utz-Uwe Hudson, Stephen Jha, Shantenu Los, Laila Paine, Drew Suter, Frédéric Ward, Logan Wilkinson, Sean Amaris, Marcos Babuji, Yadu Bader, Jonathan Balin, Riccardo Balouek, Daniel Beecroft, Sarah Belhajjame, Khalid Bhattarai, Rajat Brewer, Wes Brunk, Paul Caino-Lores, Silvina Casanova, Henri Cassol, Daniela Coleman, Jared Coleman, Taina Colonnelli, Iacopo Da Silva, Anderson Andrei de Oliveira, Daniel Elahi, Pascal Elfaramawy, Nour Elwasif, Wael Etz, Brian Fahringer, Thomas Ferreira, Wesley Filgueira, Rosa Tande, Jacob Fosso Gadelha, Luiz Gallo, Andy Garijo, Daniel Georgiou, Yiannis Gritsch, Philipp Grubel, Patricia Gueroudji, Amal Guilloteau, Quentin Hamalainen, Carlo Enriquez, Rolando Hong Huet, Lauren Kesling, Kevin Hunter Iborra, Paula Jahangiri, Shiva Janssen, Jan Jordan, Joe Kanwal, Sehrish Kunstmann, Liliane Lehmann, Fabian Leser, Ulf Li, Chen Liu, Peini Luettgau, Jakob Lupat, Richard Fernandez, Jose M. Maheshwari, Ketan Malik, Tanu Marquez, Jack Matsuda, Motohiko Medic, Doriana Mohammadi, Somayeh Mulone, Alberto Navarro, John-Luke Ng, Kin Wai Noelp, Klaus Kinoshita, Bruno P. Prout, Ryan Crusoe, Michael R. Ristov, Sashko Robila, Stefan Rosendo, Daniel Rowell, Billy Rybicki, Jedrzej Sanchez, Hector Saurabh, Nishant Saurav, Sumit Kumar Scogland, Tom Senanayake, Dinindu Shin, Woong Sirvent, Raul Skluzacek, Tyler Sly-Delgado, Barry Soiland-Reyes, Stian Souza, Abel Souza, Renan Talia, Domenico Tallent, Nathan Thamsen, Lauritz Titov, Mikhail Tovar, Benjamin Vahi, Karan Vardar-Irrgang, Eric Vartina, Edite Wang, Yuandou Wouters, Merridee Yu, Qi Bkhetan, Ziad Al Zulfiqar, Mahnoor Distributed, Parallel, and Cluster Computing The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.
title	Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows
topic	Distributed, Parallel, and Cluster Computing
url	https://arxiv.org/abs/2410.14943