1 code implementation • 17 Jul 2023 • Gilchan Park, Byung-Jun Yoon, Xihaier Luo, Vanessa López-Marrero, Shinjae Yoo, Shantenu Jha
Understanding protein interactions and pathway knowledge is crucial for unraveling the complexities of living systems and investigating the underlying mechanisms of biological functions and complex diseases.
no code implementations • 30 Sep 2022 • E. A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian Foster, Geoffrey Fox, Philip Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu
A foundational set of findable, accessible, interoperable, and reusable (FAIR) principles were proposed in 2016 as prerequisites for proper data management and stewardship, with the goal of enabling the reusability of scholarly data.
no code implementations • 24 Aug 2022 • Shantenu Jha, Vincent R. Pascuzzi, Matteo Turilli
Increasingly, scientific discovery requires sophisticated and scalable workflows.
no code implementations • 23 Aug 2022 • Vincent R. Pascuzzi, Ozgur O. Kilic, Matteo Turilli, Shantenu Jha
Heterogeneous scientific workflows consist of numerous types of tasks that require executing on heterogeneous resources.
no code implementations • 23 Sep 2021 • Hyun-Myung Woo, Xiaoning Qian, Li Tan, Shantenu Jha, Francis J. Alexander, Edward R. Dougherty, Byung-Jun Yoon
The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design.
1 code implementation • 13 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).
no code implementations • 10 Apr 2021 • Alexander Brace, Igor Yakushin, Heng Ma, Anda Trifan, Todd Munson, Ian Foster, Arvind Ramanathan, Hyungro Lee, Matteo Turilli, Shantenu Jha
The results establish DeepDriveMD as a high-performance framework for ML-driven HPC simulation scenarios, that supports diverse MD simulation and ML back-ends, and which enables new scientific insights by improving the length and time scales accessible with current computing capacity.
1 code implementation • 4 Mar 2021 • Agastya P. Bhati, Shunzhou Wan, Dario Alfè, Austin R. Clyde, Mathis Bode, Li Tan, Mikhail Titov, Andre Merzky, Matteo Turilli, Shantenu Jha, Roger R. Highfield, Walter Rocchia, Nicola Scafuri, Sauro Succi, Dieter Kranzlmüller, Gerald Mathias, David Wifling, Yann Donon, Alberto Di Meglio, Sofia Vallecorsa, Heng Ma, Anda Trifan, Arvind Ramanathan, Tom Brettin, Alexander Partin, Fangfang Xia, Xiaotan Duan, Rick Stevens, Peter V. Coveney
The race to meet the challenges of the global pandemic has served as a reminder that the existing drug discovery process is expensive, inefficient and slow.
1 code implementation • 28 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).
no code implementations • 29 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.
1 code implementation • 17 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.
no code implementations • 5 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.
1 code implementation • 28 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
1 code implementation • 23 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
1 code implementation • 3 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
no code implementations • 1 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.
1 code implementation • 1 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
no code implementations • 31 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