Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Chen, Jieyang"'
Large Language Models (LLMs) have demonstrated remarkable performance in various natural language processing tasks. However, the training of these models is computationally intensive and susceptible to faults, particularly in the attention mechanism,
Externí odkaz:
http://arxiv.org/abs/2410.11720
Autor:
Li, Yanliang, Chen, Jieyang
Publikováno v:
2024 IEEE 20th International Conference on e-Science (e-Science)
Data visualization through isosurface generation is critical in various scientific fields, including computational fluid dynamics, medical imaging, and geophysics. However, the high cost of data sharing between simulation sources and visualization re
Externí odkaz:
http://arxiv.org/abs/2408.05462
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:
Gong, Qian, Zhang, Chengzhu, Liang, Xin, Reshniak, Viktor, Chen, Jieyang, Rangarajan, Anand, Ranka, Sanjay, Vidal, Nicolas, Wan, Lipeng, Ullrich, Paul, Podhorszki, Norbert, Jacob, Robert, Klasky, Scott
Publikováno v:
2023 IEEE 19th International Conference on e-Science, Limassol, Cyprus, 2023, pp. 1-10
Scientific discoveries are increasingly constrained by limited storage space and I/O capacities. For time-series simulations and experiments, their data often need to be decimated over timesteps to accommodate storage and I/O limitations. In this pap
Externí odkaz:
http://arxiv.org/abs/2401.03317
One-sided dense matrix decompositions (e.g., Cholesky, LU, and QR) are the key components in scientific computing in many different fields. Although their design has been highly optimized for modern processors, they still consume a considerable amoun
Externí odkaz:
http://arxiv.org/abs/2301.03166
Autor:
Banerjee, Tania, Choi, Jong, Lee, Jaemoon, Gong, Qian, Chen, Jieyang, Klasky, Scott, Rangarajan, Anand, Ranka, Sanjay
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compression algorithms that limit e
Externí odkaz:
http://arxiv.org/abs/2212.10733
Autor:
Chen, Jieyang, Xie, Chenhao, Firoz, Jesun S, Li, Jiajia, Song, Shuaiwen Leon, Barker, Kevin, Raugas, Mark, Li, Ang
Sparse linear algebra kernels play a critical role in numerous applications, covering from exascale scientific simulation to large-scale data analytics. Offloading linear algebra kernels on one GPU will no longer be viable in these applications, simp
Externí odkaz:
http://arxiv.org/abs/2209.07552
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:
Chen, Jieyang, Wan, Lipeng, Liang, Xin, Whitney, Ben, Liu, Qing, Gong, Qian, Pugmire, David, Thompson, Nicholas, Choi, Jong Youl, Wolf, Matthew, Munson, Todd, Foster, Ian, Klasky, Scott
Rapid growth in scientific data and a widening gap between computational speed and I/O bandwidth makes it increasingly infeasible to store and share all data produced by scientific simulations. Instead, we need methods for reducing data volumes: idea
Externí odkaz:
http://arxiv.org/abs/2105.12764
Autor:
Xie, Chenhao, Chen, Jieyang, Firoz, Jesun S, Li, Jiajia, Song, Shuaiwen Leon, Barker, Kevin, Raugas, Mark, Li, Ang
Designing efficient and scalable sparse linear algebra kernels on modern multi-GPU based HPC systems is a daunting task due to significant irregular memory references and workload imbalance across the GPUs. This is particularly the case for Sparse Tr
Externí odkaz:
http://arxiv.org/abs/2012.06959