Zobrazeno 1 - 10
of 87
pro vyhledávání: '"Ana Gainaru"'
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:
2022 IEEE International Conference on Big Data (Big Data).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3f3eac2fee93007934eefe53f7baa9a9
https://doi.org/10.1109/bigdata55660.2022.10020884
https://doi.org/10.1109/bigdata55660.2022.10020884
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:
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).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a89bc334a5f436ff50dd4d1c7aafb1c1
https://doi.org/10.1109/cluster51413.2022.00035
https://doi.org/10.1109/cluster51413.2022.00035
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
Autor:
Xinying Wang, Lipeng Wan, Jieyang Chen, Qian Gong, Ben Whitney, Jinzhen Wang, Ana Gainaru, Qing Liu, Norbert Podhorszki, Dongfang Zhao, Feng Yan, Scott Klasky
Publikováno v:
2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data (DRBSD-7).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7490683b19ad1166fa6dfb190d9db7df
https://doi.org/10.1109/drbsd754563.2021.00008
https://doi.org/10.1109/drbsd754563.2021.00008
Autor:
Franz Poeschel, Juncheng E, William F. Godoy, Norbert Podhorszki, Scott Klasky, Greg Eisenhauer, Philip E. Davis, Lipeng Wan, Ana Gainaru, Junmin Gu, Fabian Koller, René Widera, Michael Bussmann, Axel Huebl
Publikováno v:
Smoky Mountains Computational Sciences & Engineering Conference (SMC2021), 18.-20.10.2021, Oak Ridge, USACommunications in Computer and Information Science, Volume 1512 CCIS, 99-118
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation ISBN: 9783030964979
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation ISBN: 9783030964979
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:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0153efcc9e9ddaedcfe59dc51ab45d84
http://arxiv.org/abs/2107.06108
http://arxiv.org/abs/2107.06108
Publikováno v:
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems, Institute of Electrical and Electronics Engineers, 2021, 32 (5), pp.1178-1190. ⟨10.1109/TPDS.2020.3039728⟩
IEEE Transactions on Parallel and Distributed Systems, 2021, 32 (5), pp.1178-1190. ⟨10.1109/TPDS.2020.3039728⟩
IEEE Transactions on Parallel and Distributed Systems, Institute of Electrical and Electronics Engineers, 2021, 32 (5), pp.1178-1190. ⟨10.1109/TPDS.2020.3039728⟩
IEEE Transactions on Parallel and Distributed Systems, 2021, 32 (5), pp.1178-1190. ⟨10.1109/TPDS.2020.3039728⟩
International audience; With the expected convergence between HPC, BigData and AI, new applications with different profiles are coming to HPC infrastructures. We aim at better understanding the features and needs of these applications in order to be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b204063a47ed246fd2cb73d4b1c160f6
https://hal.inria.fr/hal-03010676/document
https://hal.inria.fr/hal-03010676/document
This book constitutes the refereed post-conference proceedings of the 14th BenchCouncil International Symposium on Benchmarking, Measuring, and Optimization, Bench 2022, held virtually in November 2022.The 10 revised full papers presented were caref