Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars

no code implementations • 24 Jun 2024 • Wesley Brewer, Aditya Kashi, Sajal Dash, Aristeidis Tsaris, Junqi Yin, Mallikarjun Shankar, Feiyi Wang

In a post-ChatGPT world, this paper explores the potential of leveraging scalable artificial intelligence for scientific discovery.

Medical Image Analysis scientific discovery

Zero Coordinate Shift: Whetted Automatic Differentiation for Physics-informed Operator Learning

1 code implementation • 1 Nov 2023 • Kuangdai Leng, Mallikarjun Shankar, Jeyan Thiyagalingam

Automatic differentiation (AD) is a critical step in physics-informed machine learning, required for computing the high-order derivatives of network output w. r. t.

Operator learning Physics-informed machine learning

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

no code implementations • 6 Oct 2023 • 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

In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.

scientific discovery

Scientific Machine Learning Benchmarks

no code implementations • 25 Oct 2021 • Jeyan Thiyagalingam, Mallikarjun Shankar, Geoffrey Fox, Tony Hey

In this paper, we describe our approach to the development of scientific machine learning benchmarks and review other approaches to benchmarking scientific machine learning.

Benchmarking BIG-bench Machine Learning

MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems

no code implementations • 21 Oct 2021 • Steven Farrell, Murali Emani, Jacob Balma, Lukas Drescher, Aleksandr Drozd, Andreas Fink, Geoffrey Fox, David Kanter, Thorsten Kurth, Peter Mattson, Dawei Mu, Amit Ruhela, Kento Sato, Koichi Shirahata, Tsuguchika Tabaru, Aristeidis Tsaris, Jan Balewski, Ben Cumming, Takumi Danjo, Jens Domke, Takaaki Fukai, Naoto Fukumoto, Tatsuya Fukushi, Balazs Gerofi, Takumi Honda, Toshiyuki Imamura, Akihiko Kasagi, Kentaro Kawakami, Shuhei Kudo, Akiyoshi Kuroda, Maxime Martinasso, Satoshi Matsuoka, Henrique Mendonça, Kazuki Minami, Prabhat Ram, Takashi Sawada, Mallikarjun Shankar, Tom St. John, Akihiro Tabuchi, Venkatram Vishwanath, Mohamed Wahib, Masafumi Yamazaki, Junqi Yin

Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights.

Benchmarking BIG-bench Machine Learning +1

Data optimization for large batch distributed training of deep neural networks

no code implementations • 16 Dec 2020 • Shubhankar Gahlot, Junqi Yin, Mallikarjun Shankar

Distributed training in deep learning (DL) is common practice as data and models grow.

DataFed: Towards Reproducible Research via Federated Data Management

no code implementations • 7 Apr 2020 • Dale Stansberry, Suhas Somnath, Jessica Breet, Gregory Shutt, Mallikarjun Shankar

The increasingly collaborative, globalized nature of scientific research combined with the need to share data and the explosion in data volumes present an urgent need for a scientific data management system (SDMS).

Databases Computers and Society

USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

1 code implementation • 22 Mar 2019 • Suhas Somnath, Chris R. Smith, Nouamane Laanait, Rama K. Vasudevan, Anton Ievlev, Alex Belianinov, Andrew R. Lupini, Mallikarjun Shankar, Sergei V. Kalinin, Stephen Jesse

The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID.

Data Analysis, Statistics and Probability

Defining Big Data Analytics Benchmarks for Next Generation Supercomputers

2 code implementations • 6 Nov 2018 • Drew Schmidt, Junqi Yin, Michael Matheson, Bronson Messer, Mallikarjun Shankar

The design and construction of high performance computing (HPC) systems relies on exhaustive performance analysis and benchmarking.

Performance