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pro vyhledávání: '"Mehta, Kshitij"'
Autor:
Pasini, Massimiliano Lupo, Choi, Jong Youl, Mehta, Kshitij, Zhang, Pei, Rogers, David, Bae, Jonghyun, Ibrahim, Khaled Z., Aji, Ashwin M., Schulz, Karl W., Polo, Jorda, Balaprakash, Prasanna
We present our work on developing and training scalable, trustworthy, and energy-efficient predictive graph foundation models (GFMs) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of gr
Externí odkaz:
http://arxiv.org/abs/2406.12909
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate
Externí odkaz:
http://arxiv.org/abs/2207.11333
In the landscape of exascale computing collaborative research campaigns are conducted as co-design activities of loosely coordinated experiments. But the higher level context and the knowledge of individual experimental activity is lost over time. We
Externí odkaz:
http://arxiv.org/abs/2112.03435
Autor:
da Silva, Rafael Ferreira, Casanova, Henri, Chard, Kyle, Altintas, Ilkay, Badia, Rosa M, Balis, Bartosz, Coleman, Tainã, Coppens, Frederik, Di Natale, Frank, Enders, Bjoern, Fahringer, Thomas, Filgueira, Rosa, Fursin, Grigori, Garijo, Daniel, Goble, Carole, Howell, Dorran, Jha, Shantenu, Katz, Daniel S., Laney, Daniel, Leser, Ulf, Malawski, Maciej, Mehta, Kshitij, Pottier, Loïc, Ozik, Jonathan, Peterson, J. Luc, Ramakrishnan, Lavanya, Soiland-Reyes, Stian, Thain, Douglas, Wolf, Matthew
The landscape of workflow systems for scientific applications is notoriously convoluted with hundreds of seemingly equivalent workflow systems, many isolated research claims, and a steep learning curve. To address some of these challenges and lay the
Externí odkaz:
http://arxiv.org/abs/2110.02168
Autor:
da Silva, Rafael Ferreira, Casanova, Henri, Chard, Kyle, Coleman, Tainã, Laney, Dan, Ahn, Dong, Jha, Shantenu, Howell, Dorran, Soiland-Reys, Stian, Altintas, Ilkay, Thain, Douglas, Filgueira, Rosa, Babuji, Yadu, Badia, Rosa M., Balis, Bartosz, Caino-Lores, Silvina, Callaghan, Scott, Coppens, Frederik, Crusoe, Michael R., De, Kaushik, Di Natale, Frank, Do, Tu M. A., Enders, Bjoern, Fahringer, Thomas, Fouilloux, Anne, Fursin, Grigori, Gaignard, Alban, Ganose, Alex, Garijo, Daniel, Gesing, Sandra, Goble, Carole, Hasan, Adil, Huber, Sebastiaan, Katz, Daniel S., Leser, Ulf, Lowe, Douglas, Ludaescher, Bertram, Maheshwari, Ketan, Malawski, Maciej, Mayani, Rajiv, Mehta, Kshitij, Merzky, Andre, Munson, Todd, Ozik, Jonathan, Pottier, Loïc, Ristov, Sashko, Roozmeh, Mehdi, Souza, Renan, Suter, Frédéric, Tovar, Benjamin, Turilli, Matteo, Vahi, Karan, Vidal-Torreira, Alvaro, Whitcup, Wendy, Wilde, Michael, Williams, Alan, Wolf, Matthew, Wozniak, Justin
Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and
Externí odkaz:
http://arxiv.org/abs/2106.05177
Autor:
da Silva, Rafael Ferreira, Casanova, Henri, Chard, Kyle, Laney, Dan, Ahn, Dong, Jha, Shantenu, Goble, Carole, Ramakrishnan, Lavanya, Peterson, Luc, Enders, Bjoern, Thain, Douglas, Altintas, Ilkay, Babuji, Yadu, Badia, Rosa M., Bonazzi, Vivien, Coleman, Taina, Crusoe, Michael, Deelman, Ewa, Di Natale, Frank, Di Tommaso, Paolo, Fahringer, Thomas, Filgueira, Rosa, Fursin, Grigori, Ganose, Alex, Gruning, Bjorn, Katz, Daniel S., Kuchar, Olga, Kupresanin, Ana, Ludascher, Bertram, Maheshwari, Ketan, Mattoso, Marta, Mehta, Kshitij, Munson, Todd, Ozik, Jonathan, Peterka, Tom, Pottier, Loic, Randles, Tim, Soiland-Reyes, Stian, Tovar, Benjamin, Turilli, Matteo, Uram, Thomas, Vahi, Karan, Wilde, Michael, Wolf, Matthew, Wozniak, Justin
Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. Many of these workflows have high computational, storage, and/or communicatio
Externí odkaz:
http://arxiv.org/abs/2103.09181
Akademický článek
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Akademický článek
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Autor:
Mehta, Kshitij V, Cliff, Ashley, Suter, Frédéric, Walker, Angelica M, Wolf, Matthew, Jacobson, Daniel, Klasky, Scott
The ever-increasing volumes of scientific data com- bined with sophisticated techniques for extracting information from them have led to the increasing popularity of ensemble workflows which are a collection of runs of individual workflows. A traditi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a90bcd0d5f0eece69ef214173b5524e9
Autor:
Ferreira da Silva, Rafael, Casanova, Henri, Chard, Kyle, Coleman, Tainã, Laney, Dan, Ahn, Dong, Jha, Shantenu, Howell, Dorran, Soiland-Reys, Stian, Altintas, Ilkay, Thain, Douglas, Filgueira, Rosa, Babuji, Yadu, Badia, Rosa M., Balis, Bartosz, Caino-Lores, Silvina, Callaghan, Scott, Coppens, Frederik, Crusoe, Michael R., De, Kaushik, Di Natale, Frank, Do, Tu M. A., Enders, Bjoern, Fahringer, Thomas, Fouilloux, Anne, Fursin, Grigori, Gaignard, Alban, Ganose, Alex, Garijo, Daniel, Gesing, Sandra, Goble, Carole, Hasan, Adil, Huber, Sebastiaan, Katz, Daniel S., Leser, Ulf, Lowe, Douglas, Ludaescher, Bertram, Maheshwari, Ketan, Malawski, Maciej, Mayani, Rajiv, Mehta, Kshitij, Merzky, Andre, Munson, Todd, Ozik, Jonathan, Pottier, Loïc, Ristov, Sashko, Roozmeh, Mehdi, Souza, Renan, Suter, Frédéric, Tovar, Benjamin, Turilli, Matteo, Vahi, Karan, Vidal-Torreira, Alvaro, Whitcup, Wendy, Wilde, Michael, Williams, Alan, Wolf, Matthew, Wozniak, Justin
Scientific workflows are a cornerstone of modern scientific computing, and they have underpinned some of the most significant discoveries of the last decade. Many of these workflows have high computational, storage, and/or communication demands, and
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e9e084b7cb00a1685208961ff24654e