Sajal Dash

According to our database, Sajal Dash authored at least 23 papers between 2011 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Scalable Artificial Intelligence for Science: Perspectives, Methods and Exemplars.

Wesley Brewer, Aditya Kashi, Sajal Dash, Aristeidis Tsaris, Junqi Yin, Mallikarjun Shankar, Feiyi Wang

CoRR, 2024

Optimizing Distributed Training on Frontier for Large Language Models.

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, J. Austin Ellis, Matthias Maiterth, Guojing Cong, Feiyi Wang, Prasanna Balaprakash

Proceedings of the ISC High Performance 2024 Research Paper Proceedings (39th International Conference), 2024

Exploring Vision Transformers on the Frontier Supercomputer for Remote Sensing and Geoscientific Applications.

Valentine Anantharaj, Takuya Kurihana, Sajal Dash, Gabriele Padovani, Sandro Fiore

Proceedings of the IGARSS 2024, 2024

2023

Evaluation of pre-training large language models on leadership-class supercomputers.

Junqi Yin, Sajal Dash, John Gounley, Feiyi Wang, Georgia D. Tourassi

J. Supercomput., December, 2023

Optimizing Distributed Training on Frontier for Large Language Models.

Sajal Dash, Isaac Lyngaas, Junqi Yin, Xiao Wang, Romain Egele, Guojing Cong, Feiyi Wang, Prasanna Balaprakash

CoRR, 2023

Ultra-Long Sequence Distributed Transformer.

Xiao Wang, Isaac Lyngaas, Aristeidis Tsaris, Peng Chen, Sajal Dash, Mayanka Chandra Shekar, Tao Luo, Hong-Jun Yoon, Mohamed Wahib, John Gounley

CoRR, 2023

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, Pete Luferenko, Divya Kumar, Jonathan A. 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 W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. 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 M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

FORGE: Pre-Training Open Foundation Models for Science.

Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar

Proceedings of the International Conference for High Performance Computing, 2023

Scaling Resolution of Gigapixel Whole Slide Images Using Spatial Decomposition on Convolutional Neural Networks.

Aristeidis Tsaris, Joshua Romero, Thorsten Kurth, Jacob D. Hinkle, Hong-Jun Yoon, Feiyi Wang, Sajal Dash, Georgia D. Tourassi

Proceedings of the Platform for Advanced Scientific Computing Conference, 2023

Distributing Simplex-Shaped Nested for-Loops to Identify Carcinogenic Gene Combinations.

Sajal Dash, Mohammad Alaul Haque Monil, Junqi Yin, Ramu Anandakrishnan, Feiyi Wang

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2023

2022

Toward an Autonomous Workflow for Single Crystal Neutron Diffraction.

Junqi Yin, Guannan Zhang, Huibo Cao, Sajal Dash, Bryan C. Chakoumakos, Feiyi Wang

Proceedings of the Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, 2022

Image transformers for classifying acute lymphoblastic leukemia.

Priscilla Cho, Sajal Dash, Aristeidis Tsaris, Hong-Jun Yoon

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, 2022

A Scalable Pipeline for Gigapixel Whole Slide Imaging Analysis on Leadership Class HPC Systems.

Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, Folami T. Alamudun, Hong-Jun Yoon, Feiyi Wang

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2022

Hvac: Removing I/O Bottleneck for Large-Scale Deep Learning Applications.

Awais Khan, Arnab K. Paul, Christopher Zimmer, Sarp Oral, Sajal Dash, Scott Atchley, Feiyi Wang

Proceedings of the IEEE International Conference on Cluster Computing, 2022

Distilling Knowledge from Ensembles of Cluster-Constrained-Attention Multiple-Instance Learners for Whole Slide Image Classification.

Folami Alamudun, Jacob D. Hinkle, Sajal Dash, Benjamín Hernández, Aristeidis Tsaris, Hong-Jun Yoon

Proceedings of the IEEE International Conference on Big Data, 2022

2021

Smoky Mountain Data Challenge 2021: An Open Call to Solve Scientific Data Challenges Using Advanced Data Analytics and Edge Computing.

Pravallika Devineni, Panchapakesan Ganesh, Nikhil Sivadas, Abhijeet Dhakane, Ketan Maheshwari, Drahomira Herrmannova, Ramakrishnan Kannan, Seung-Hwan Lim, Thomas E. Potok, Jordan B. Chipka, Priyantha Mudalige, Mark Coletti, Sajal Dash, Arnab Kumar Paul, Sarp Oral, Feiyi Wang, Bill Kay, Melissa R. Allen-Dumas, Christa Brelsford, Joshua R. New, Andy Berres, Kuldeep R. Kurte, Jibonananda Sanyal, Levi Sweet, Chathika Gunaratne, Maxim A. Ziatdinov, Rama K. Vasudevan, Sergei V. Kalinin, Olivera Kotevska, Jean C. Bilheux, Hassina Z. Bilheux, Garrett E. Granroth, Thomas Proffen, Rick Riedel, Peter F. Peterson, Shruti R. Kulkarni, Kyle P. Kelley, Stephen Jesse, Maryam Parsa

Proceedings of the Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, 2021

Mitigating Catastrophic Forgetting in Deep Learning in a Streaming Setting Using Historical Summary.

Sajal Dash, Junqi Yin, Mallikarjun Shankar, Feiyi Wang, Wu-chun Feng

Proceedings of the 2021 7th International Workshop on Data Analysis and Reduction for Big Scientific Data, 2021

Scaling Out a Combinatorial Algorithm for Discovering Carcinogenic Gene Combinations to Thousands of GPUs.

Sajal Dash, Qais Al-Hajri, Wu-chun Feng, Harold R. Garner, Ramu Anandakrishnan

Proceedings of the 35th IEEE International Parallel and Distributed Processing Symposium, 2021

2020

Exploring the Landscape of Big Data Analytics Through Domain-Aware Algorithm Design.

Sajal Dash

PhD thesis, 2020

Towards a Universal Classifier for Crystallographic Space Groups: A Trickle-Down Approach to Handle Data Imbalance.

Sajal Dash, Archi Dasgupta

Proceedings of the Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI, 2020

2019

Strategies to Deploy and Scale Deep Learning on the Summit Supercomputer.

Junqi Yin, Shubhankar Gahlot, Nouamane Laanait, Ketan Maheshwari, Jack Morrison, Sajal Dash, Mallikarjun Shankar

Proceedings of the Third IEEE/ACM Workshop on Deep Learning on Supercomputers, 2019

2017

Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis.

Sajal Dash, Anshuman Verma, Chris North, Wu-chun Feng

Proceedings of the 19th IEEE International Conference on High Performance Computing and Communications; 15th IEEE International Conference on Smart City; 3rd IEEE International Conference on Data Science and Systems, 2017

2011

On the energy of bifurcated hydrogen bonds for protein structure prediction.

Sajal Dash, Jack Snoeyink

Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops, 2011