|Title||FireWorks: a dynamic workflow system designed for high-throughput applications|
|Publication Type||Journal Article|
|Year of Publication||2015|
|Authors||Anubhav Jain, Shyue Ping Ong, Wei Chen, Bharat Medasan, Xiaohui Qu, Michael Kocher, Miriam Brafman, Guido Petretto, Gian-Marco Rignanese, Geoffroy Hautier, Daniel Gunter, Kristin A Persson|
|Journal||Concurrency and Computation: Practice and Experience|
|Pagination||5037 - 5059|
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.
|Short Title||Concurrency Computat.: Pract. Exper.|