Zobrazeno 1 - 10
of 48
pro vyhledávání: '"Gainaru, Ana"'
Autor:
Eisenhauer, Greg, Podhorszki, Norbert, Gainaru, Ana, Klasky, Scott, Davis, Philip E., Parashar, Manish, Wolf, Matthew, Suchtya, Eric, Fredj, Erick, Bolea, Vicente, Pöschel, Franz, Steiniger, Klaus, Bussmann, Michael, Pausch, Richard, Chandrasekaran, Sunita
The "IO Wall" problem, in which the gap between computation rate and data access rate grows continuously, poses significant problems to scientific workflows which have traditionally relied upon using the filesystem for intermediate storage between wo
Externí odkaz:
http://arxiv.org/abs/2410.00178
AI integration is revolutionizing the landscape of HPC simulations, enhancing the importance, use, and performance of AI-driven HPC workflows. This paper surveys the diverse and rapidly evolving field of AI-driven HPC and provides a common conceptual
Externí odkaz:
http://arxiv.org/abs/2406.14315
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Autor:
Gong, Qian, Chen, Jieyang, Whitney, Ben, Liang, Xin, Reshniak, Viktor, Banerjee, Tania, Lee, Jaemoon, Rangarajan, Anand, Wan, Lipeng, Vidal, Nicolas, Liu, Qing, Gainaru, Ana, Podhorszki, Norbert, Archibald, Richard, Ranka, Sanjay, Klasky, Scott
Publikováno v:
SoftwareX, 24(2023), 101590
We describe MGARD, a software providing MultiGrid Adaptive Reduction for floating-point scientific data on structured and unstructured grids. With exceptional data compression capability and precise error control, MGARD addresses a wide range of requ
Externí odkaz:
http://arxiv.org/abs/2401.05994
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
Autor:
Anirudh, Rushil, Archibald, Rick, Asif, M. Salman, Becker, Markus M., Benkadda, Sadruddin, Bremer, Peer-Timo, Budé, Rick H. S., Chang, C. S., Chen, Lei, Churchill, R. M., Citrin, Jonathan, Gaffney, Jim A, Gainaru, Ana, Gekelman, Walter, Gibbs, Tom, Hamaguchi, Satoshi, Hill, Christian, Humbird, Kelli, Jalas, Sören, Kawaguchi, Satoru, Kim, Gon-Ho, Kirchen, Manuel, Klasky, Scott, Kline, John L., Krushelnick, Karl, Kustowski, Bogdan, Lapenta, Giovanni, Li, Wenting, Ma, Tammy, Mason, Nigel J., Mesbah, Ali, Michoski, Craig, Munson, Todd, Murakami, Izumi, Najm, Habib N., Olofsson, K. Erik J., Park, Seolhye, Peterson, J. Luc, Probst, Michael, Pugmire, Dave, Sammuli, Brian, Sawlani, Kapil, Scheinker, Alexander, Schissel, David P., Shalloo, Rob J., Shinagawa, Jun, Seong, Jaegu, Spears, Brian K., Tennyson, Jonathan, Thiagarajan, Jayaraman, Ticoş, Catalin M., Trieschmann, Jan, van Dijk, Jan, Van Essen, Brian, Ventzek, Peter, Wang, Haimin, Wang, Jason T. L., Wang, Zhehui, Wende, Kristian, Xu, Xueqiao, Yamada, Hiroshi, Yokoyama, Tatsuya, Zhang, Xinhua
Publikováno v:
IEEE Transactions on Plasma Science 51, 1750 - 1838 (2023)
Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine lear
Externí odkaz:
http://arxiv.org/abs/2205.15832
Autor:
Wan, Lipeng, Huebl, Axel, Gu, Junmin, Poeschel, Franz, Gainaru, Ana, Wang, Ruonan, Chen, Jieyang, Liang, Xin, Ganyushin, Dmitry, Munson, Todd, Foster, Ian, Vay, Jean-Luc, Podhorszki, Norbert, Wu, Kesheng, Klasky, Scott
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems, 2021
The applications being developed within the U.S. Exascale Computing Project (ECP) to run on imminent Exascale computers will generate scientific results with unprecedented fidelity and record turn-around time. Many of these codes are based on particl
Externí odkaz:
http://arxiv.org/abs/2107.07108
Autor:
Poeschel, Franz, E, Juncheng, Godoy, William F., Podhorszki, Norbert, Klasky, Scott, Eisenhauer, Greg, Davis, Philip E., Wan, Lipeng, Gainaru, Ana, Gu, Junmin, Koller, Fabian, Widera, René, Bussmann, Michael, Huebl, Axel
This paper aims to create a transition path from file-based IO to streaming-based workflows for scientific applications in an HPC environment. By using the openPMP-api, traditional workflows limited by filesystem bottlenecks can be overcome and flexi
Externí odkaz:
http://arxiv.org/abs/2107.06108
With the ever-growing need of data in HPC applications, the congestion at the I/O level becomes critical in super-computers. Architectural enhancement such as burst-buffers and pre-fetching are added to machines, but are not sufficient to prevent con
Externí odkaz:
http://arxiv.org/abs/1702.06900
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