Zobrazeno 1 - 10
of 7 816
pro vyhledávání: '"Chard A"'
The Common Workflow Language (CWL) is a widely adopted language for defining and sharing computational workflows. It is designed to be independent of the execution engine on which workflows are executed. In this paper, we describe our experiences int
Externí odkaz:
http://arxiv.org/abs/2412.08062
Autor:
Alsaadi, Aymen, Hategan-Marandiuc, Mihael, Maheshwari, Ketan, Merzky, Andre, Titov, Mikhail, Turilli, Matteo, Wilke, Andreas, Wozniak, Justin M., Chard, Kyle, da Silva, Rafael Ferreira, Jha, Shantenu, Laney, Daniel
Exascale computers offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. However, these software combinations and integrations a
Externí odkaz:
http://arxiv.org/abs/2411.10637
Autor:
Khan, Arham, Underwood, Robert, Siebenschuh, Carlo, Babuji, Yadu, Ajith, Aswathy, Hippe, Kyle, Gokdemir, Ozan, Brace, Alexander, Chard, Kyle, Foster, Ian
Deduplication is a major focus for assembling and curating training datasets for large language models (LLM) -- detecting and eliminating additional instances of the same content -- in large collections of technical documents. Unrestrained, duplicate
Externí odkaz:
http://arxiv.org/abs/2411.04257
Autor:
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
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, heterogen
Externí odkaz:
http://arxiv.org/abs/2410.14943
Autor:
Khan, Arham, Nief, Todd, Hudson, Nathaniel, Sakarvadia, Mansi, Grzenda, Daniel, Ajith, Aswathy, Pettyjohn, Jordan, Chard, Kyle, Foster, Ian
Understanding neural networks is crucial to creating reliable and trustworthy deep learning models. Most contemporary research in interpretability analyzes just one model at a time via causal intervention or activation analysis. Yet despite successes
Externí odkaz:
http://arxiv.org/abs/2410.12927
Applications are increasingly written as dynamic workflows underpinned by an execution framework that manages asynchronous computations across distributed hardware. However, execution frameworks typically offer one-size-fits-all solutions for data fl
Externí odkaz:
http://arxiv.org/abs/2410.12092
Autor:
Sakarvadia, Mansi, Ajith, Aswathy, Khan, Arham, Hudson, Nathaniel, Geniesse, Caleb, Chard, Kyle, Yang, Yaoqing, Foster, Ian, Mahoney, Michael W.
Language models (LMs) can "memorize" information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for exa
Externí odkaz:
http://arxiv.org/abs/2410.02159
Autor:
Hudson, Nathaniel, Hayot-Sasson, Valerie, Babuji, Yadu, Baughman, Matt, Pauloski, J. Gregory, Chard, Ryan, Foster, Ian, Chard, Kyle
Federated Learning (FL) is a decentralized machine learning paradigm where models are trained on distributed devices and are aggregated at a central server. Existing FL frameworks assume simple two-tier network topologies where end devices are direct
Externí odkaz:
http://arxiv.org/abs/2409.16495
Autor:
Ward, Logan, Pauloski, J. Gregory, Hayot-Sasson, Valerie, Babuji, Yadu, Brace, Alexander, Chard, Ryan, Chard, Kyle, Thakur, Rajeev, Foster, Ian
Computational workflows are a common class of application on supercomputers, yet the loosely coupled and heterogeneous nature of workflows often fails to take full advantage of their capabilities. We created Colmena to leverage the massive parallelis
Externí odkaz:
http://arxiv.org/abs/2408.14434
Autor:
Pan, Haochen, Chard, Ryan, Zhou, Sicheng, Kamatar, Alok, Vescovi, Rafael, Hayot-Sasson, Valérie, Bauer, André, Gonthier, Maxime, Chard, Kyle, Foster, Ian
Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distribute
Externí odkaz:
http://arxiv.org/abs/2407.11432