55 citations · 59 across the 7 of their papers we have counts for
7 papers
Robustness of deep learning classification to adversarial input on GPUs: asynchronous parallel accumulation is a source of vulnerability
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver +3
The ability of machine learning (ML) classification models to resist small, targeted input perturbations -- known as adversarial attacks -- is a key measure of their safety and rel…
Impacts of floating-point non-associativity on reproducibility for HPC and deep learning applications
Sanjif Shanmugavelu, Mathieu Taillefumier, Christopher Culver +3
Run to run variability in parallel programs caused by floating-point non-associativity has been known to significantly affect reproducibility in iterative algorithms, due to accumu…
Deep Learning Interatomic Potential Connects Molecular Structural Ordering to Macroscale Properties of Polyacrylonitrile (PAN) Polymer
Rajni Chahal, Michael D. Toomey, Logan T. Kearney +4
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental un…
Portability for GPU-accelerated molecular docking applications for cloud and HPC: can portable compiler directives provide performance across all platforms?
Mathialakan Thavappiragasam, Wael Elwasif, Ada Sedova
High-throughput structure-based screening of drug-like molecules has become a common tool in biomedical research. Recently, acceleration with graphics processing units (GPUs) has p…
Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer
Mu Gao, Mark Coletti, Russell B. Davidson +4
Deep learning has contributed to major advances in the prediction of protein structure from sequence, a fundamental problem in structural bioinformatics. With predictions now appro…
DIPS-Plus: The Enhanced Database of Interacting Protein Structures for Interface Prediction
Alex Morehead, Chen Chen, Ada Sedova +1
How and where proteins interface with one another can ultimately impact the proteins' functions along with a range of other biological processes. As such, precise computational met…
GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research
Scott LeGrand, Aaron Scheinberg, Andreas F. Tillack +12
Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experime…