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488 results on '"Scott Klasky"'

1. MGARD: A multigrid framework for high-performance, error-controlled data compression and refactoring

Author

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, and Scott Klasky

Subjects
Error-controlled data compression, Data refactoring, I/O acceleration, Derived quantities preservation, Computer software, QA76.75-76.765
Abstract

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 requirements, including storage reduction, high-performance I/O, and in-situ data analysis. It features a unified application programming interface (API) that seamlessly operates across diverse computing architectures. MGARD has been optimized with highly-tuned GPU kernels and efficient memory and device management mechanisms, ensuring scalable and rapid operations.

Published
2023
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2. Error-Bounded Learned Scientific Data Compression with Preservation of Derived Quantities

Author

Jaemoon Lee, Qian Gong, Jong Choi, Tania Banerjee, Scott Klasky, Sanjay Ranka, and Anand Rangarajan

Subjects
data compression, autoencoders, error guarantees, moment preservation, constraint satisfaction, quantization, Technology, Engineering (General). Civil engineering (General), TA1-2040, Biology (General), QH301-705.5, Physics, QC1-999, Chemistry, QD1-999
Abstract

Scientific applications continue to grow and produce extremely large amounts of data, which require efficient compression algorithms for long-term storage. Compression errors in scientific applications can have a deleterious impact on downstream processing. Thus, it is crucial to preserve all the “known” Quantities of Interest (QoI) during compression. To address this issue, most existing approaches guarantee the reconstruction error of the original data or primary data (PD), but cannot directly control the problem of preserving the QoI. In this work, we propose a physics-informed compression technique that is composed of two parts: (i) reduction of the PD with bounded errors and (ii) preservation of the QoI. In the first step, we combine tensor decompositions, autoencoders, product quantizers, and error-bounded lossy compressors to bound the reconstruction error at high levels of compression. In the second step, we use constraint satisfaction post-processing followed by quantization to preserve the QoI. To illustrate the challenges of reducing the reconstruction errors of the PD and QoI, we focus on simulation data generated by a large-scale fusion code, XGC, which can produce tens of petabytes in a single day. The results show that our approach can achieve a high compression amount while accurately preserving the QoI within scientifically acceptable bounds.

Published
2022
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3. ADIOS 2: The Adaptable Input Output System. A framework for high-performance data management

Author

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, and Scott Klasky

Subjects
High-performance computing (HPC), Scalable I/O, Luster GPFS file systems, Staging, RDMA, Data science, Computer software, QA76.75-76.765
Abstract

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 introduces a unified application programming interface (API) that enables seamless data movement through files, wide-area-networks, and direct memory access, as well as high-level APIs for data analysis. The internal architecture provides a set of reusable and extendable components for managing data presentation and transport mechanisms for new applications. ADIOS 2 bindings are available in C++11, C, Fortran, Python, and Matlab and are currently used across different scientific communities. ADIOS 2 provides a communal framework to tackle data management challenges as we approach the exascale era of supercomputing.

Published
2020
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22. Streaming Data in HPC Workflows Using ADIOS.

Author

Greg Eisenhauer, Norbert Podhorszki, Ana Gainaru, Scott Klasky, Philip E. Davis, Manish Parashar, Matthew Wolf, Eric Suchtya, Erick Fredj, Vicente Bolea, Franz Pöschel, Klaus Steiniger, Michael Bussmann, Richard Pausch, and Sunita Chandrasekaran

Published
2024
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24. AI Surrogate Model for Distributed Computing Workloads.

Author

David K. Park, Yihui Ren 0001, Ozgur O. Kilic, Tatiana Korchuganova, Sairam Sri Vatsavai, Joseph Boudreau, Tasnuva Chowdhury, Shengyu Feng, Raees Khan, Jaehyung Kim, Scott Klasky, Tadashi Maeno, Paul Nilsson, Verena Ingrid Martinez Outschoorn, Norbert Podhorszki, Frédéric Suter, Wei Yang, Yiming Yang, Shinjae Yoo, Alexei Klimentov, and Adolfy Hoisie

Published
2024
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41. The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science.

Author

Eric Suchyta, Scott Klasky, Norbert Podhorszki, Matthew Wolf, Abolaji D. Adesoji, Choong-Seock Chang, Jong Choi 0001, Philip E. Davis, Julien Dominski, Stéphane Ethier, Ian T. Foster, Kai Germaschewski, Berk Geveci, Chris Harris 0004, Kevin A. Huck, Qing Liu 0002, Jeremy Logan, Kshitij Mehta, Gabriele Merlo, Shirley V. Moore, Todd S. Munson, Manish Parashar, David Pugmire, Mark S. Shephard, Cameron W. Smith, Pradeep Subedi, Lipeng Wan 0001, Ruonan Wang, and Shuangxi Zhang

Published
2022
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