Search Results for author: Shantenu Jha

Found 14 papers, 8 papers with code

Optimal Decision Making in High-Throughput Virtual Screening Pipelines

no code implementations23 Sep 2021 Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon

Effective selection of the potential candidates that meet certain conditions in a tremendously large search space has been one of the major concerns in many real-world applications.

Decision Making

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).

Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations

no code implementations10 Apr 2021 Alexander Brace, Igor Yakushin, Heng Ma, Anda Trifan, Todd Munson, Ian Foster, Arvind Ramanathan, Hyungro Lee, Matteo Turilli, Shantenu Jha

The use of ML methods to dynamically steer ensemble-based simulations promises significant improvements in the performance of scientific applications.

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).

Learning Everywhere: A Taxonomy for the Integration of Machine Learning and Simulations

no code implementations29 Sep 2019 Geoffrey Fox, Shantenu Jha

We present a taxonomy of research on Machine Learning (ML) applied to enhance simulations together with a catalog of some activities.

DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding

1 code implementation17 Sep 2019 Hyungro Lee, Heng Ma, Matteo Turilli, Debsindhu Bhowmik, Shantenu Jha, Arvind Ramanathan

Our study provides a quantitative basis to understand how DL driven MD simulations, can lead to effective performance gains and reduced times to solution on supercomputing resources.

Protein Folding

Understanding ML driven HPC: Applications and Infrastructure

no code implementations5 Sep 2019 Geoffrey Fox, Shantenu Jha

We recently outlined the vision of "Learning Everywhere" which captures the possibility and impact of how learning methods and traditional HPC methods can be coupled together.

Parallel Performance of Molecular Dynamics Trajectory Analysis

1 code implementation28 Jun 2019 Mahzad Khoshlessan, Ioannis Paraskevakos, Geoffrey C. Fox, Shantenu Jha, Oliver Beckstein

The performance of biomolecular molecular dynamics simulations has steadily increased on modern high performance computing resources but acceleration of the analysis of the output trajectories has lagged behind so that analyzing simulations is becoming a bottleneck.

Distributed, Parallel, and Cluster Computing Quantitative Methods D.1.3; J.2

Task-parallel Analysis of Molecular Dynamics Trajectories

1 code implementation23 Jan 2018 Ioannis Paraskevakos, Andre Luckow, Mahzad Khoshlessan, George Chantzialexiou, Thomas E. Cheatham, Oliver Beckstein, Geoffrey C. Fox, Shantenu Jha

We also provide a quantitative performance analysis of the different algorithms across the three frameworks.

Distributed, Parallel, and Cluster Computing

Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations

1 code implementation3 Jan 2018 Jumana Dakka, Kristof Farkas-Pall, Matteo Turilli, David W Wright, Peter V Coveney, Shantenu Jha

This paper makes three main contributions: (1) shows the importance of adaptive execution for ensemble-based free energy protocols to improve binding affinity accuracy; (2) presents and characterizes HTBAC -- a software system that enables the scalable and adaptive execution of binding affinity protocols at scale; and (3) for a widely used free-energy protocol (TIES), shows improvements in the accuracy of simulations for a fixed amount of resource, or reduced resource consumption for a fixed accuracy as a consequence of adaptive execution.

Distributed, Parallel, and Cluster Computing

Learning Neural Markers of Schizophrenia Disorder Using Recurrent Neural Networks

no code implementations1 Dec 2017 Jumana Dakka, Pouya Bashivan, Mina Gheiratmand, Irina Rish, Shantenu Jha, Russell Greiner

Smart systems that can accurately diagnose patients with mental disorders and identify effective treatments based on brain functional imaging data are of great applicability and are gaining much attention.

Ensemble Toolkit: Scalable and Flexible Execution of Ensembles of Tasks

1 code implementation1 Feb 2016 Vivekanandan Balasubramanian, Antons Treikalis, Ole Weidner, Shantenu Jha

Motivated by the missing capabilities of these computing systems and the increasing importance of task-level parallelism, we introduce the Ensemble toolkit which has the following application development features: (i) abstractions that enable the expression of ensembles as primary entities, and (ii) support for ensemble-based execution patterns that capture the majority of application scenarios.

Distributed, Parallel, and Cluster Computing

Hadoop on HPC: Integrating Hadoop and Pilot-based Dynamic Resource Management

no code implementations31 Jan 2016 Andre Luckow, Ioannis Paraskevakos, George Chantzialexiou, Shantenu Jha

High-performance computing platforms such as supercomputers have traditionally been designed to meet the compute demands of scientific applications.

Distributed, Parallel, and Cluster Computing

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