Parallel scripting using cloud resources


We will develop and improve techniques for using the Swift parallel scripting language to leverage distributed cloud resources to execute scientific applications at a high degree of parallelism. We will test this on 4 applications: 1) protein structure prediction; 2) protein-RNA docking; 3) quantum-level simulation of glassy materials; 4) analysis of fMRI data

Intellectual Merit

This project will address the systems issues of dynamic resource aggregation, efficient data management without shared cluster filesystems, and scheduling across fluctuating application demand levels and resource availability levels.

Broader Impact

The project will advance the ease-of-use of distributed cloud resources and make them more available to more scientists with less programming effort.

Use of FutureGrid

To test applications in biochemistry and neuroscience using parallel scripting techniques to leverage and federate cloud resources.

Scale Of Use

We would like to test on 4+ locations aggregating about 1000-2000 compute cores.We would run tests sporadically over several months, phasing in about 4 applications as they become ready.


Michael Wilde
Argonne National Laboratory

Project Members

David Kelly
Eugene Yan
Justin Wozniak
Ketan Maheshwari
Thomas Uram
Yonas Demissie

FutureGrid Experts

Andrew Younge
Zhenhua Guo