Zobrazeno 1 - 10
of 73
pro vyhledávání: '"Jieyang Chen"'
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:
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8c2f8d428bbfff82843674710b7b24c9
https://doi.org/10.1109/tpds.2022.3179989
https://doi.org/10.1109/tpds.2022.3179989
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems. 33:3702-3717
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1ccf3449ea15fecf2c969f67db193f61
https://doi.org/10.1109/tpds.2022.3168386
https://doi.org/10.1109/tpds.2022.3168386
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
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e440ad469795ca4a74f95555ee52b76d
https://doi.org/10.1109/tc.2021.3092201
https://doi.org/10.1109/tc.2021.3092201
Publikováno v:
ACM Transactions on Parallel Computing.
The current trend of performance growth in HPC systems is accompanied by a massive increase in energy consumption. In this paper, we introduce GreenMD, an energy-efficient framework for heterogeneous systems for LU factorization utilizing multi-GPUs.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b8af5ef768091beb0585d002fd17e8ef
https://doi.org/10.1145/3583590
https://doi.org/10.1145/3583590
Publikováno v:
2022 IEEE/ACM 8th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52afb5697710773356014cd5e548e585
https://doi.org/10.1109/drbsd56682.2022.00004
https://doi.org/10.1109/drbsd56682.2022.00004
Autor:
Qian Gong, Ben Whitney, Chengzhu Zhang, Xin Liang, Anand Rangarajan, Jieyang Chen, Lipeng Wan, Paul Ullrich, Qing Liu, Robert Jacob, Sanjay Ranka, Scott Klasky
Publikováno v:
34th International Conference on Scientific and Statistical Database Management.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f9c849e00a50ff5c3459fdc32950795f
https://doi.org/10.1145/3538712.3538717
https://doi.org/10.1145/3538712.3538717
Publikováno v:
Journal of Parallel and Distributed Computing. 151:70-85
Linear algebra operations have been widely used in big data analytics and scientific computations. Many works have been done on optimizing linear algebra operations on GPUs with regular-shaped input. However, few works focus on fully utilizing GPU re
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::208df264225400d97be61b1da001aad3
https://doi.org/10.1016/j.jpdc.2021.02.013
https://doi.org/10.1016/j.jpdc.2021.02.013
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
