Zobrazeno 1 - 10
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pro vyhledávání: '"Underwood, Robert A."'
The performance of the GMRES iterative solver on GPUs is limited by the GPU main memory bandwidth. Compressed Basis GMRES outperforms GMRES by storing the Krylov basis in low precision, thereby reducing the memory access. An open question is whether
Externí odkaz:
http://arxiv.org/abs/2409.15468
Autor:
Agarwal, Tripti, Di, Sheng, Huang, Jiajun, Huang, Yafan, Gopalakrishnan, Ganesh, Underwood, Robert, Zhao, Kai, Liang, Xin, Li, Guanpeng, Cappello, Franck
Error-bounded lossy compression has been a critical technique to significantly reduce the sheer amounts of simulation datasets for high-performance computing (HPC) scientific applications while effectively controlling the data distortion based on use
Externí odkaz:
http://arxiv.org/abs/2408.11971
LLMs have seen rapid adoption in all domains. They need to be trained on high-end high-performance computing (HPC) infrastructures and ingest massive amounts of input data. Unsurprisingly, at such a large scale, unexpected events (e.g., failures of c
Externí odkaz:
http://arxiv.org/abs/2406.10707
Autor:
Di, Sheng, Liu, Jinyang, Zhao, Kai, Liang, Xin, Underwood, Robert, Zhang, Zhaorui, Shah, Milan, Huang, Yafan, Huang, Jiajun, Yu, Xiaodong, Ren, Congrong, Guo, Hanqi, Wilkins, Grant, Tao, Dingwen, Tian, Jiannan, Jin, Sian, Jian, Zizhe, Wang, Daoce, Rahman, MD Hasanur, Zhang, Boyuan, Calhoun, Jon C., Li, Guanpeng, Yoshii, Kazutomo, Alharthi, Khalid Ayed, Cappello, Franck
Error-bounded lossy compression has been effective in significantly reducing the data storage/transfer burden while preserving the reconstructed data fidelity very well. Many error-bounded lossy compressors have been developed for a wide range of par
Externí odkaz:
http://arxiv.org/abs/2404.02840
Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and validation which need methods to sha
Externí odkaz:
http://arxiv.org/abs/2403.15953
Autor:
Wilkins, Grant, Di, Sheng, Calhoun, Jon C., Li, Zilinghan, Kim, Kibaek, Underwood, Robert, Mortier, Richard, Cappello, Franck
With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and servers becomes crucial. FL, though decentralized, often faces communica
Externí odkaz:
http://arxiv.org/abs/2312.13461
Autor:
Liu, Jinyang, Tian, Jiannan, Wu, Shixun, Di, Sheng, Zhang, Boyuan, Underwood, Robert, Huang, Yafan, Huang, Jiajun, Zhao, Kai, Li, Guanpeng, Tao, Dingwen, Chen, Zizhong, Cappello, Franck
Error-bounded lossy compression is a critical technique for significantly reducing scientific data volumes. Compared to CPU-based compressors, GPU-based compressors exhibit substantially higher throughputs, fitting better for today's HPC applications
Externí odkaz:
http://arxiv.org/abs/2312.05492
Network Architecture Search and specifically Regularized Evolution is a common way to refine the structure of a deep learning model.However, little is known about how models empirically evolve over time which has design implications for designing cac
Externí odkaz:
http://arxiv.org/abs/2309.12576
Autor:
Underwood, Robert, Bessac, Julie, Krasowska, David, Calhoun, Jon C., Di, Sheng, Cappello, Franck
Lossy compressors are increasingly adopted in scientific research, tackling volumes of data from experiments or parallel numerical simulations and facilitating data storage and movement. In contrast with the notion of entropy in lossless compression,
Externí odkaz:
http://arxiv.org/abs/2305.08801
Crystallography is the leading technique to study atomic structures of proteins and produces enormous volumes of information that can place strains on the storage and data transfer capabilities of synchrotron and free-electron laser light sources. Lo
Externí odkaz:
http://arxiv.org/abs/2206.11297