Zobrazeno 1 - 10
of 690
pro vyhledávání: '"Parashar Manish"'
Autor:
Parashar, Manish
Emerging data-driven scientific workflows are seeking to leverage distributed data sources to understand end-to-end phenomena, drive experimentation, and facilitate important decision-making. Despite the exponential growth of available digital data s
Externí odkaz:
http://arxiv.org/abs/2406.04480
Autor:
Hoefler, Torsten, Copik, Marcin, Beckman, Pete, Jones, Andrew, Foster, Ian, Parashar, Manish, Reed, Daniel, Troyer, Matthias, Schulthess, Thomas, Ernst, Dan, Dongarra, Jack
HPC and Cloud have evolved independently, specializing their innovations into performance or productivity. Acceleration as a Service (XaaS) is a recipe to empower both fields with a shared execution platform that provides transparent access to comput
Externí odkaz:
http://arxiv.org/abs/2401.04552
Autor:
da Silva, Rafael Ferreira, Badia, Rosa M., Bala, Venkat, Bard, Debbie, Bremer, Peer-Timo, Buckley, Ian, Caino-Lores, Silvina, Chard, Kyle, Goble, Carole, Jha, Shantenu, Katz, Daniel S., Laney, Daniel, Parashar, Manish, Suter, Frederic, Tyler, Nick, Uram, Thomas, Altintas, Ilkay, Andersson, Stefan, Arndt, William, Aznar, Juan, Bader, Jonathan, Balis, Bartosz, Blanton, Chris, Braghetto, Kelly Rosa, Brodutch, Aharon, Brunk, Paul, Casanova, Henri, Lierta, Alba Cervera, Chigu, Justin, Coleman, Taina, Collier, Nick, Colonnelli, Iacopo, Coppens, Frederik, Crusoe, Michael, Cunningham, Will, Kinoshita, Bruno de Paula, Di Tommaso, Paolo, Doutriaux, Charles, Downton, Matthew, Elwasif, Wael, Enders, Bjoern, Erdmann, Chris, Fahringer, Thomas, Figueiredo, Ludmilla, Filgueira, Rosa, Foltin, Martin, Fouilloux, Anne, Gadelha, Luiz, Gallo, Andy, Saez, Artur Garcia, Garijo, Daniel, Gerlach, Roman, Grant, Ryan, Grayson, Samuel, Grubel, Patricia, Gustafsson, Johan, Hayot-Sasson, Valerie, Hernandez, Oscar, Hilbrich, Marcus, Justine, AnnMary, Laflotte, Ian, Lehmann, Fabian, Luckow, Andre, Luettgau, Jakob, Maheshwari, Ketan, Matsuda, Motohiko, Medic, Doriana, Mendygral, Pete, Michalewicz, Marek, Nonaka, Jorji, Pawlik, Maciej, Pottier, Loic, Pouchard, Line, Putz, Mathias, Radha, Santosh Kumar, Ramakrishnan, Lavanya, Ristov, Sashko, Romano, Paul, Rosendo, Daniel, Ruefenacht, Martin, Rycerz, Katarzyna, Saurabh, Nishant, Savchenko, Volodymyr, Schulz, Martin, Simpson, Christine, Sirvent, Raul, Skluzacek, Tyler, Soiland-Reyes, Stian, Souza, Renan, Sukumar, Sreenivas Rangan, Sun, Ziheng, Sussman, Alan, Thain, Douglas, Titov, Mikhail, Tovar, Benjamin, Tripathy, Aalap, Turilli, Matteo, Tuznik, Bartosz, van Dam, Hubertus, Vivas, Aurelio, Ward, Logan, Widener, Patrick, Wilkinson, Sean, Zawalska, Justyna, Zulfiqar, Mahnoor
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
Externí odkaz:
http://arxiv.org/abs/2304.00019
Data collected by large-scale instruments, observatories, and sensor networks are key enablers of scientific discoveries in many disciplines. However, ensuring that these data can be accessed, integrated, and analyzed in a democratized and timely man
Externí odkaz:
http://arxiv.org/abs/2112.06479
In this survey, we discuss the challenges of executing scientific workflows as well as existing Machine Learning (ML) techniques to alleviate those challenges. We provide the context and motivation for applying ML to each step of the execution of the
Externí odkaz:
http://arxiv.org/abs/2110.13999
Graph embedding techniques have attracted growing interest since they convert the graph data into continuous and low-dimensional space. Effective graph analytic provides users a deeper understanding of what is behind the data and thus can benefit a v
Externí odkaz:
http://arxiv.org/abs/2110.06991
Autor:
Qin, Yubo, Rodero, Ivan, Simonet, Anthony, Meertens, Charles, Reiner, Daniel, Riley, James, Parashar, Manish
With the growing number and increasing availability of shared-use instruments and observatories, observational data is becoming an essential part of application workflows and contributor to scientific discoveries in a range of disciplines. However, t
Externí odkaz:
http://arxiv.org/abs/2012.15321
Autor:
Wang, Zhe, Subedi, Pradeep, Duan, Shaohua, Qin, Yubo, Davis, Philip, Simonet, Anthony, Rodero, Ivan, Parashar, Manish
In order to achieve near-time insights, scientific workflows tend to be organized in a flexible and dynamic way. Data-driven triggering of tasks has been explored as a way to support workflows that evolve based on the data. However, the overhead intr
Externí odkaz:
http://arxiv.org/abs/2004.10381
The Internet of Things paradigm connects edge devices via the Internet enabling them to be seamlessly integrated with a wide variety of applications. In recent years, the number of connected devices has grown significantly, along with the volume and
Externí odkaz:
http://arxiv.org/abs/1912.06567