FireWorks: a dynamic workflow system designed for high-throughput applications

Date Published
12/2015
Publication Type
Journal Article
Authors
DOI
10.1002/cpe.3505
Abstract

This paper introduces FireWorks, a workflow software for running high-throughput calculation workflows at supercomputing centers. FireWorks has been used to complete over 50 million CPU-hours worth of computational chemistry and materials science calculations at the National Energy Research Supercomputing Center. It has been designed to serve the demanding high-throughput computing needs of these applications, with extensive support for (i) concurrent execution through job packing, (ii) failure detection and correction, (iii) provenance and reporting for long-running projects, (iv) automated duplicate detection, and (v) dynamic workflows (i.e., modifying the workflow graph during runtime). We have found that these features are highly relevant to enabling modern data-driven and high-throughput science applications, and we discuss our implementation strategy that rests on Python and NoSQL databases (MongoDB). Finally, we present performance data and limitations of our approach along with planned future work. 

Journal
Concurrency and Computation: Practice and Experience
Volume
27
Year of Publication
2015
Issue
17
Pagination
5037 - 5059
Short Title
Concurrency Computat.: Pract. Exper.
Refereed Designation
Refereed
Organizations
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