Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Norbert Podhorszki"'
MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring
Autor:
Qian Gong, Jieyang Chen, Ben Whitney, Xin Liang, Viktor Reshniak, Tania Banerjee, Jaemoon Lee, Anand Rangarajan, Lipeng Wan, Nicolas Vidal, Qing Liu, Ana Gainaru, Norbert Podhorszki, Richard Archibald, Sanjay Ranka, Scott Klasky
Publikováno v:
SoftwareX, Vol 24, Iss , Pp 101590- (2023)
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:
https://doaj.org/article/e7ccffb856204f6982e53466473b0e3c
Autor:
William F. Godoy, Norbert Podhorszki, Ruonan Wang, Chuck Atkins, Greg Eisenhauer, Junmin Gu, Philip Davis, Jong Choi, Kai Germaschewski, Kevin Huck, Axel Huebl, Mark Kim, James Kress, Tahsin Kurc, Qing Liu, Jeremy Logan, Kshitij Mehta, George Ostrouchov, Manish Parashar, Franz Poeschel, David Pugmire, Eric Suchyta, Keichi Takahashi, Nick Thompson, Seiji Tsutsumi, Lipeng Wan, Matthew Wolf, Kesheng Wu, Scott Klasky
Publikováno v:
SoftwareX, Vol 12, Iss , Pp 100561- (2020)
We present ADIOS 2, the latest version of the Adaptable Input Output (I/O) System. ADIOS 2 addresses scientific data management needs ranging from scalable I/O in supercomputers, to data analysis in personal computer and cloud systems. Version 2 intr
Externí odkaz:
https://doaj.org/article/1b10ffd8ae92432b9967bc2e0905e38d
Autor:
Ana Gainaru, Lipeng Wan, Ruonan Wang, Eric Suchyta, Jieyang Chen, Norbert Podhorszki, James Kress, David Pugmire, Scott Klasky
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:4134-4147
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:3702-3717
Autor:
Lipeng Wan, David Pugmire, Xin Liang, Matthew Wolf, Dingwen Tao, Jieyang Chen, James Kress, Scott Klasky, Qing Liu, Norbert Podhorszki, Ben Whitney
Publikováno v:
IEEE Transactions on Computers. 71:1522-1536
Data management is becoming increasingly important in dealing with the large amounts of data produced by large-scale scientific simulations and instruments. Existing multilevel compression algorithms offer a promising way to manage scientific data at
Publikováno v:
Journal of Parallel and Distributed Computing. 164:106-122
Autor:
Eric Suchyta, Jong Youl Choi, Seung-Hoe Ku, David Pugmire, Ana Gainaru, Kevin Huck, Ralph Kube, Aaron Scheinberg, Frederic Suter, Choongseock Chang, Todd Munson, Norbert Podhorszki, Scott Klasky
Publikováno v:
2022 IEEE International Conference on Cluster Computing (CLUSTER).
Publikováno v:
34th International Conference on Scientific and Statistical Database Management.
Autor:
Shuangxi Zhang, Berk Geveci, Matthew Wolf, Kevin Huck, E. Suchyta, Cameron W. Smith, Ruonan Wang, Stephane Ethier, Philip E. Davis, Manish Parashar, Pradeep Subedi, Gabriele Merlo, Abolaji Adesoji, Norbert Podhorszki, Qing Liu, Todd Munson, Shirley Moore, Mark S. Shephard, C.S. Chang, Jeremy Logan, Jong Choi, Lipeng Wan, Kai Germaschewski, David Pugmire, Ian Foster, Scott Klasky, Kshitij Mehta, Chris Harris, Julien Dominski
Publikováno v:
The International Journal of High Performance Computing Applications. 36:106-128
We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of the Whole Device Modeling Application (WDMApp) in the Exascale Computing Project. EFFIS consists of a library
Autor:
Ruonan Wang, Lipeng Wan, Jean-Luc Vay, Scott Klasky, Jieyang Chen, Ian Foster, Todd Munson, Dmitry Ganyushin, Axel Huebl, Ana Gainaru, Xin Liang, Kesheng Wu, Junmin Gu, Norbert Podhorszki, Franz Poeschel
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems, vol 33, iss 4
IEEE Transactions on Parallel and Distributed Systems 33(2022), 878-890
IEEE Transactions on Parallel and Distributed Systems 33(2022), 878-890
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3745e2c84e30d9d142f8715e69e53f4a
https://escholarship.org/uc/item/0s74p189
https://escholarship.org/uc/item/0s74p189