Search Results for author: Yadu Babuji

Found 5 papers, 3 papers with code

Colmena: Scalable Machine-Learning-Based Steering of Ensemble Simulations for High Performance Computing

1 code implementation6 Oct 2021 Logan Ward, Ganesh Sivaraman, J. Gregory Pauloski, Yadu Babuji, Ryan Chard, Naveen Dandu, Paul C. Redfern, Rajeev S. Assary, Kyle Chard, Larry A. Curtiss, Rajeev Thakur, Ian Foster

Scientific applications that involve simulation ensembles can be accelerated greatly by using experiment design methods to select the best simulations to perform.

Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening

1 code implementation13 Jun 2021 Austin Clyde, Thomas Brettin, Alexander Partin, Hyunseung Yoo, Yadu Babuji, Ben Blaiszik, Andre Merzky, Matteo Turilli, Shantenu Jha, Arvind Ramanathan, Rick Stevens

Our analysis of the speedup explains that to screen more molecules under a docking paradigm, another order of magnitude speedup must come from model accuracy rather than computing speed (which, if increased, will not anymore alter our throughput to screen molecules).

Targeting SARS-CoV-2 with AI- and HPC-enabled Lead Generation: A First Data Release

1 code implementation28 May 2020 Yadu Babuji, Ben Blaiszik, Tom Brettin, Kyle Chard, Ryan Chard, Austin Clyde, Ian Foster, Zhi Hong, Shantenu Jha, Zhuozhao Li, Xuefeng Liu, Arvind Ramanathan, Yi Ren, Nicholaus Saint, Marcus Schwarting, Rick Stevens, Hubertus van Dam, Rick Wagner

Researchers across the globe are seeking to rapidly repurpose existing drugs or discover new drugs to counter the the novel coronavirus disease (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).

funcX: A Federated Function Serving Fabric for Science

no code implementations7 May 2020 Ryan Chard, Yadu Babuji, Zhuozhao Li, Tyler Skluzacek, Anna Woodard, Ben Blaiszik, Ian Foster, Kyle Chard

These new approaches must enable computation to be mobile, so that, for example, it can occur near data, be triggered by events (e. g., arrival of new data), be offloaded to specialized accelerators, or run remotely where resources are available.

Distributed, Parallel, and Cluster Computing

DLHub: Model and Data Serving for Science

no code implementations27 Nov 2018 Ryan Chard, Zhuozhao Li, Kyle Chard, Logan Ward, Yadu Babuji, Anna Woodard, Steve Tuecke, Ben Blaiszik, Michael J. Franklin, Ian Foster

Here we present the Data and Learning Hub for science (DLHub), a multi-tenant system that provides both model repository and serving capabilities with a focus on science applications.

Distributed Computing

Cannot find the paper you are looking for? You can Submit a new open access paper.