DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
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.
Keywords
Cite
@article{arxiv.2310.04610,
title = {DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies},
author = {Shuaiwen Leon Song and Bonnie Kruft and Minjia Zhang and Conglong Li and Shiyang Chen and Chengming Zhang and Masahiro Tanaka and Xiaoxia Wu and Jeff Rasley and Ammar Ahmad Awan and Connor Holmes and Martin Cai and Adam Ghanem and Zhongzhu Zhou and Yuxiong He and Pete Luferenko and Divya Kumar and Jonathan Weyn and Ruixiong Zhang and Sylwester Klocek and Volodymyr Vragov and Mohammed AlQuraishi and Gustaf Ahdritz and Christina Floristean and Cristina Negri and Rao Kotamarthi and Venkatram Vishwanath and Arvind Ramanathan and Sam Foreman and Kyle Hippe and Troy Arcomano and Romit Maulik and Maxim Zvyagin and Alexander Brace and Bin Zhang and Cindy Orozco Bohorquez and Austin Clyde and Bharat Kale and Danilo Perez-Rivera and Heng Ma and Carla M. Mann and Michael Irvin and J. Gregory Pauloski and Logan Ward and Valerie Hayot and Murali Emani and Zhen Xie and Diangen Lin and Maulik Shukla and Ian Foster and James J. Davis and Michael E. Papka and Thomas Brettin and Prasanna Balaprakash and Gina Tourassi and John Gounley and Heidi Hanson and Thomas E Potok and Massimiliano Lupo Pasini and Kate Evans and Dan Lu and Dalton Lunga and Junqi Yin and Sajal Dash and Feiyi Wang and Mallikarjun Shankar and Isaac Lyngaas and Xiao Wang and Guojing Cong and Pei Zhang and Ming Fan and Siyan Liu and Adolfy Hoisie and Shinjae Yoo and Yihui Ren and William Tang and Kyle Felker and Alexey Svyatkovskiy and Hang Liu and Ashwin Aji and Angela Dalton and Michael Schulte and Karl Schulz and Yuntian Deng and Weili Nie and Josh Romero and Christian Dallago and Arash Vahdat and Chaowei Xiao and Thomas Gibbs and Anima Anandkumar and Rick Stevens},
journal= {arXiv preprint arXiv:2310.04610},
year = {2023}
}