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Found 7 papers, 4 papers with code
AI-coupled HPC Workflows
Increasingly, scientific discovery requires sophisticated and scalable workflows.
Asynchronous Execution of Heterogeneous Tasks in ML-driven HPC Workflows
Heterogeneous scientific workflows consist of numerous types of tasks that require executing on heterogeneous resources.
Protein-Ligand Docking Surrogate Models: A SARS-CoV-2 Benchmark for Deep Learning Accelerated Virtual Screening
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
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.
Pandemic Drugs at Pandemic Speed: Infrastructure for Accelerating COVID-19 Drug Discovery with Hybrid Machine Learning- and Physics-based Simulations on High Performance Computers
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.
DeepDriveMD: Deep-Learning Driven Adaptive Molecular Simulations for Protein Folding
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.
Concurrent and Adaptive Extreme Scale Binding Free Energy Calculations
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
