Search NASA⌕ Search

DOE OSTI · 2474744

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

Ferreira Da Silva, Rafael·Bard, Deborah·Chard, Kyle·De witt, Shaun·Foster, Ian·Gibbs, Tom·Goble, Carole·Godoy, William·Gustafsson, Johan·Uwe haus, Utz-·Hudson, Stephen·Jha, Shantenu·Los, Laila·Paine, Drew·Suter, Fred·Ward, Logan·Wilkinson, Sean·Amaris, Marcos·Andrei da silva, Anderson·Babuji, Yadu·Bader, Jonathan·Balin, Riccardo·Balouek, Daniel·Beecroft, Sarah·Belhajjame, Khalid·Bhattarai, Rajat·Brewer, Wes·Brunk, Paul·Caino-Lores, Silvina·Cassol, Daniela·Coleman, Jared·Coleman, Taina·Colonnelli, Iacopo·De oliveira, Daniel·Elahi, Pascal·Elfaramawy, Nour·Elwasif, Wael·Etz, Brian·Fahringer, Thomas·Ferreira, Wesley·Filgueira, Rosa·Fosso-Tande, Jacob·Gadelha, Luiz·Gallo, Andy·Garijo, Daniel·Georgiou, Yiannis·Gritsch, Philipp·Grubel, Patricia·Gueroudji, Amal·Guilloteau, Quentin·Hamalainen, Carlo·Huet, Lauren·Hunter kesling, Kevin·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·Prout, Ryan·Crusoe, Michael·Ristov, Sashko·Robila, Stefan·Rosendo, Daniel·Rowell, Billy·Rybicki, Jedrzej·Sanchez, Hector·Saurabh, Nishant·Saurav, Sumit·Scogland, Tom·Senanayake, Dinindu·Shin, Woong·Sirvent, Raul·Skluzacek, Tyler·Sly-Delgado, Barry·Souza, Abel·Santos Souza, Renan·Talia, Domenico·Tallent, Nathan·Thamsen, Lauritz·Titov, Mikhail·Tovar, Benjamin

Abstract

The 2024 Workflows Community Summit report presents the outcomes of a three-day international gathering that brought together 109 experts from 18 countries to discuss future trends and challenges in scientific workflows. The summit focused on six key areas: time-sensitive workflows, convergence of AI and HPC workflows, multi-facility workflows, heterogeneous HPC environments, user experience and interfaces, and FAIR computational workflows. Discussions highlighted emerging challenges such as integrating AI with traditional HPC, managing workflows across diverse facilities, addressing heterogeneity in computing environments, and ensuring workflows are findable, accessible, interoperable, and reusable (FAIR). The report outlines recent advances, ongoing challenges, and provides recommendations for each topic area, emphasizing the need for standardization, improved interoperability, and the development of more sophisticated tools and frameworks to support the evolving landscape of scientific workflows in the era of exascale computing and AI integration.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ferreira Da Silva, Rafael, Bard, Deborah, Chard, Kyle, De witt, Shaun, Foster, Ian, Gibbs, Tom, Goble, Carole, Godoy, William, Gustafsson, Johan, Uwe haus, Utz-, Hudson, Stephen, Jha, Shantenu, Los, Laila, Paine, Drew, Suter, Fred, Ward, Logan, Wilkinson, Sean, Amaris, Marcos, Andrei da silva, Anderson, Babuji, Yadu, Bader, Jonathan, Balin, Riccardo, Balouek, Daniel, Beecroft, Sarah, Belhajjame, Khalid, Bhattarai, Rajat, Brewer, Wes, Brunk, Paul, Caino-Lores, Silvina, Cassol, Daniela, Coleman, Jared, Coleman, Taina, Colonnelli, Iacopo, De oliveira, Daniel, Elahi, Pascal, Elfaramawy, Nour, Elwasif, Wael, Etz, Brian, Fahringer, Thomas, Ferreira, Wesley, Filgueira, Rosa, Fosso-Tande, Jacob, Gadelha, Luiz, Gallo, Andy, Garijo, Daniel, Georgiou, Yiannis, Gritsch, Philipp, Grubel, Patricia, Gueroudji, Amal, Guilloteau, Quentin, Hamalainen, Carlo, Huet, Lauren, Hunter kesling, Kevin, 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, Prout, Ryan, Crusoe, Michael, Ristov, Sashko, Robila, Stefan, Rosendo, Daniel, Rowell, Billy, Rybicki, Jedrzej, Sanchez, Hector, Saurabh, Nishant, Saurav, Sumit, Scogland, Tom, Senanayake, Dinindu, Shin, Woong, Sirvent, Raul, Skluzacek, Tyler, Sly-Delgado, Barry, Souza, Abel, Santos Souza, Renan, Talia, Domenico, Tallent, Nathan, Thamsen, Lauritz, Titov, Mikhail, Tovar, Benjamin. 2024-10-01. Workflows Community Summit 2024: Future Trends and Challenges in Scientific Workflows. https://doi.org/10.2172/2474744

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

TANTE: Time-adaptive operator learning via neural Taylor expansion

Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynamics. However, most existing methods rely on fixed time step sizes during rollout, which limits their ability to adapt to varying temporal complexity and often leads to error accumulation. In this work, we propose the Time-Adaptive Transformer with Neural Taylor Expansion (TANTE), a novel operator-learning framework that produces continuous-time predictions with adaptive step sizes. TANTE predicts future states by performing a Taylor expansion at the current state, where neural networks learn both the higher-order temporal derivatives and the local radius of convergence. This allows the model to dynamically adjust its rollout based on the local behavior of the solution, thereby reducing cumulative error and improving computational efficiency. We demonstrate the effectiveness of TANTE across a wide range of PDE benchmarks, achieving superior accuracy and adaptability compared to fixed-step baselines, delivering accuracy gains of 60-80 % and speed-ups of 30-40 % at inference time.

97 MATHEMATICS AND COMPUTING↗

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗