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

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

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.

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2310.04610
Document Type :
Working Paper