Zobrazeno 1 - 10
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pro vyhledávání: '"da Silva, Rafael"'
Autor:
Beck, Thomas, Baroni, Alessandro, Bennink, Ryan, Buchs, Gilles, Perez, Eduardo Antonio Coello, Eisenbach, Markus, da Silva, Rafael Ferreira, Meena, Muralikrishnan Gopalakrishnan, Gottiparthi, Kalyan, Groszkowski, Peter, Humble, Travis S., Landfield, Ryan, Maheshwari, Ketan, Oral, Sarp, Sandoval, Michael A., Shehata, Amir, Suh, In-Saeng, Zimmer, Christopher
Quantum Computing (QC) offers significant potential to enhance scientific discovery in fields such as quantum chemistry, optimization, and artificial intelligence. Yet QC faces challenges due to the noisy intermediate-scale quantum era's inherent ext
Externí odkaz:
http://arxiv.org/abs/2408.16159
Autor:
Turilli, Matteo, Hategan-Marandiuc, Mihael, Titov, Mikhail, Maheshwari, Ketan, Alsaadi, Aymen, Merzky, Andre, Arambula, Ramon, Zakharchanka, Mikhail, Cowan, Matt, Wozniak, Justin M., Wilke, Andreas, Kilic, Ozgur Ozan, Chard, Kyle, da Silva, Rafael Ferreira, Jha, Shantenu, Laney, Daniel
Scientific discovery increasingly requires executing heterogeneous scientific workflows on high-performance computing (HPC) platforms. Heterogeneous workflows contain different types of tasks (e.g., simulation, analysis, and learning) that need to be
Externí odkaz:
http://arxiv.org/abs/2407.16646
Autor:
Bieberich, Samuel T., Maheshwari, Ketan C., Wilkinson, Sean R., Date, Prasanna, Suh, In-Saeng, da Silva, Rafael Ferreira
Quantum Computers offer an intriguing challenge in modern Computer Science. With the inevitable physical limitations to Moore's Law, quantum hardware provides avenues to solve grander problems faster by utilizing Quantum Mechanical properties at suba
Externí odkaz:
http://arxiv.org/abs/2310.03286
Autor:
Godoy, William F., Valero-Lara, Pedro, Anderson, Caira, Lee, Katrina W., Gainaru, Ana, da Silva, Rafael Ferreira, Vetter, Jeffrey S.
We evaluate Julia as a single language and ecosystem paradigm powered by LLVM to develop workflow components for high-performance computing. We run a Gray-Scott, 2-variable diffusion-reaction application using a memory-bound, 7-point stencil kernel o
Externí odkaz:
http://arxiv.org/abs/2309.10292
Distributed learning on edge devices has attracted increased attention with the advent of federated learning (FL). Notably, edge devices often have limited battery and heterogeneous energy availability, while multiple rounds are required in FL for co
Externí odkaz:
http://arxiv.org/abs/2309.06033
Autor:
Souza, Renan, Skluzacek, Tyler J., Wilkinson, Sean R., Ziatdinov, Maxim, da Silva, Rafael Ferreira
Publikováno v:
19th IEEE International Conference on e-Science (eScience) 2023 - Limassol, Cyprus
Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role i
Externí odkaz:
http://arxiv.org/abs/2308.09004
Autor:
Lage, Viviane M. A., Rodríguez-Fernández, Carlos, Vieira, Felipe S., da Silva, Rafael T., Bernardi, Maria Inês B., Lima Jr., Maurício M de, Cantarero, Andrés, de Carvalho, Hugo B.
We present a comprehensive study on the structure and optical properties of Mn-and Co-doped ZnO samples prepared via solid-state reaction method with different dopant concentrations and atmospheres. The samples were structural and chemically characte
Externí odkaz:
http://arxiv.org/abs/2308.00684
Autor:
Hategan-Marandiuc, Mihael, Merzky, Andre, Collier, Nicholson, Maheshwari, Ketan, Ozik, Jonathan, Turilli, Matteo, Wilke, Andreas, Wozniak, Justin M., Chard, Kyle, Foster, Ian, da Silva, Rafael Ferreira, Jha, Shantenu, Laney, Daniel
It is generally desirable for high-performance computing (HPC) applications to be portable between HPC systems, for example to make use of more performant hardware, make effective use of allocations, and to co-locate compute jobs with large datasets.
Externí odkaz:
http://arxiv.org/abs/2307.07895
Models derived from other models are extremely common in machine learning (ML) today. For example, transfer learning is used to create task-specific models from "pre-trained" models through finetuning. This has led to an ecosystem where models are re
Externí odkaz:
http://arxiv.org/abs/2307.07507
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