DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies

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,Christopher Bishop,Max Welling,Tie-Yan Liu,Christian Bodnar,Johannes Brandsetter,Wessel Bruinsma, Chan Cao,Yuan-Jyue Chen, Peggy Dai,Patrick Garvan,Liang He,Elizabeth Heider,Pipi Hu,Peiran Jin,Fusong Ju,Yatao Li,Chang Liu,Renqian Luo,Qi Meng,Frank Noe,Tao Qin,Janwei Zhu,Bin Shao,Yu Shi,Wenlei Shi,Gregor Simm,Megan Stanley,Lixin Sun, Yue Wang,Tong Wang,Zun Wang,Lijun Wu,Yingce Xia,Leo Xia,Shufang Xie,Shuxin Zheng,Jianwei Zhu,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, Thomas Gibbs,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

CoRR(2023)

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摘要
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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sophisticated ai system technologies,discovery
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