DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery
through Sophisticated AI System Technologies
- URL: http://arxiv.org/abs/2310.04610v2
- Date: Wed, 11 Oct 2023 23:15:43 GMT
- Title: DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery
through Sophisticated AI System Technologies
- Authors: Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang
Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad
Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete
Luferenko, Divya Kumar, Jonathan Weyn, Ruixiong Zhang, Sylwester Klocek,
Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean,
Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam
Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander
Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo
Perez-Rivera, Heng Ma, Carla M. Mann, Michael Irvin, J. Gregory Pauloski,
Logan Ward, Valerie Hayot, Murali Emani, Zhen Xie, Diangen Lin, Maulik
Shukla, Ian Foster, James J. Davis, Michael E. Papka, Thomas Brettin,
Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi Hanson, Thomas E
Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton Lunga, Junqi Yin,
Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang,
Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo,
Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin
Aji, Angela Dalton, Michael Schulte, Karl Schulz, Yuntian Deng, Weili Nie,
Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs,
Anima Anandkumar, Rick Stevens
- Abstract summary: DeepSpeed4Science aims to build unique capabilities through AI system technology innovations.
We showcase the early progress we made with DeepSpeed4Science in addressing two of the critical system challenges in structural biology research.
- Score: 116.09762105379241
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the upcoming decade, deep learning may revolutionize the natural sciences,
enhancing our capacity to model and predict natural occurrences. This could
herald a new era of scientific exploration, bringing significant advancements
across sectors from drug development to renewable energy. To answer this call,
we present DeepSpeed4Science initiative (deepspeed4science.ai) which aims to
build unique capabilities through AI system technology innovations to help
domain experts to unlock today's biggest science mysteries. By leveraging
DeepSpeed's current technology pillars (training, inference and compression) as
base technology enablers, DeepSpeed4Science will create a new set of AI system
technologies tailored for accelerating scientific discoveries by addressing
their unique complexity beyond the common technical approaches used for
accelerating generic large language models (LLMs). In this paper, we showcase
the early progress we made with DeepSpeed4Science in addressing two of the
critical system challenges in structural biology research.
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