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pro vyhledávání: '"P. J. Gregory"'
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:
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:
Meilander, Jeff, Caporaso, J. Gregory
Linear waste management systems are unsustainable and contribute to environmental degradation, economic inequity, and health disparities. Among the array of environmental challenges stemming from anthropogenic impacts, the management of human excreme
Externí odkaz:
http://arxiv.org/abs/2409.07376
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:
Pauloski, J. Gregory, Hayot-Sasson, Valerie, Gonthier, Maxime, Hudson, Nathaniel, Pan, Haochen, Zhou, Sicheng, Foster, Ian, Chard, Kyle
Task-based execution frameworks, such as parallel programming libraries, computational workflow systems, and function-as-a-service platforms, enable the composition of distinct tasks into a single, unified application designed to achieve a computatio
Externí odkaz:
http://arxiv.org/abs/2408.07236
Autor:
Raspet, Isaiah, Gehret, Elizabeth, Herman, Chloe, Meilander, Jeff, Manley, Andrew, Simard, Anthony, Bolyen, Evan, Caporaso, J. Gregory
Background: We present q2-boots, a QIIME 2 plugin that facilitates bootstrapped and rarefaction-based microbiome diversity analysis. This plugin provides eight new actions that allow users to apply any of thirty different alpha diversity metrics and
Externí odkaz:
http://arxiv.org/abs/2408.05420
Autor:
Pauloski, J. Gregory, Hayot-Sasson, Valerie, Ward, Logan, Brace, Alexander, Bauer, André, Chard, Kyle, Foster, Ian
Workflow and serverless frameworks have empowered new approaches to distributed application design by abstracting compute resources. However, their typically limited or one-size-fits-all support for advanced data flow patterns leaves optimization to
Externí odkaz:
http://arxiv.org/abs/2407.01764
Autor:
Herman, Chloe, Barker, Bridget M., Bartelli, Thais F., Chandra, Vidhi, Krajmalnik-Brown, Rosa, Jewell, Mary, Li, Le, Liao, Chen, McAllister, Florencia, Nirmalkar, Khemlal, Xavier, Joao B., Caporaso, J. Gregory
Fecal Microbiota Transplant (FMT) is an FDA approved treatment for recurrent Clostridium difficile infections, and is being explored for other clinical applications, from alleviating digestive and neurological disorders, to priming the microbiome for
Externí odkaz:
http://arxiv.org/abs/2404.07325
Autor:
Hudson, Nathaniel, Pauloski, J. Gregory, Baughman, Matt, Kamatar, Alok, Sakarvadia, Mansi, Ward, Logan, Chard, Ryan, Bauer, André, Levental, Maksim, Wang, Wenyi, Engler, Will, Skelly, Owen Price, Blaiszik, Ben, Stevens, Rick, Chard, Kyle, Foster, Ian
Deep learning methods are transforming research, enabling new techniques, and ultimately leading to new discoveries. As the demand for more capable AI models continues to grow, we are now entering an era of Trillion Parameter Models (TPM), or models
Externí odkaz:
http://arxiv.org/abs/2402.03480
Autor:
Song, Shuaiwen Leon, Kruft, Bonnie, Zhang, Minjia, Li, Conglong, Chen, Shiyang, Zhang, Chengming, Tanaka, Masahiro, Wu, Xiaoxia, Rasley, Jeff, Awan, Ammar Ahmad, Holmes, Connor, Cai, Martin, Ghanem, Adam, Zhou, Zhongzhu, He, Yuxiong, Luferenko, Pete, Kumar, Divya, Weyn, Jonathan, Zhang, Ruixiong, Klocek, Sylwester, Vragov, Volodymyr, AlQuraishi, Mohammed, Ahdritz, Gustaf, Floristean, Christina, Negri, Cristina, Kotamarthi, Rao, Vishwanath, Venkatram, Ramanathan, Arvind, Foreman, Sam, Hippe, Kyle, Arcomano, Troy, Maulik, Romit, Zvyagin, Maxim, Brace, Alexander, Zhang, Bin, Bohorquez, Cindy Orozco, Clyde, Austin, Kale, Bharat, Perez-Rivera, Danilo, Ma, Heng, Mann, Carla M., Irvin, Michael, Pauloski, J. Gregory, Ward, Logan, Hayot, Valerie, Emani, Murali, Xie, Zhen, Lin, Diangen, Shukla, Maulik, Foster, Ian, Davis, James J., Papka, Michael E., Brettin, Thomas, Balaprakash, Prasanna, Tourassi, Gina, Gounley, John, Hanson, Heidi, Potok, Thomas E, Pasini, Massimiliano Lupo, Evans, Kate, Lu, Dan, Lunga, Dalton, Yin, Junqi, Dash, Sajal, Wang, Feiyi, Shankar, Mallikarjun, Lyngaas, Isaac, Wang, Xiao, Cong, Guojing, Zhang, Pei, Fan, Ming, Liu, Siyan, Hoisie, Adolfy, Yoo, Shinjae, Ren, Yihui, Tang, William, Felker, Kyle, Svyatkovskiy, Alexey, Liu, Hang, Aji, Ashwin, Dalton, Angela, Schulte, Michael, Schulz, Karl, Deng, Yuntian, Nie, Weili, Romero, Josh, Dallago, Christian, Vahdat, Arash, Xiao, Chaowei, Gibbs, Thomas, Anandkumar, Anima, Stevens, Rick
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scientific exploration, bringing significant advancements across sectors fro
Externí odkaz:
http://arxiv.org/abs/2310.04610