FireWorks: a Dynamic Workflow System Designed for High-Throughput Applications

Jain, Anubhav; Ong, Shyue Ping; Chen, Wei; Medasani, Bharat; Qu, Xiaohui; Kocher, Michael; Brafman, Miriam; Petretto, Guido; Rignanese, Gian-Marco; Hautier, Geoffroy; Gunter, Daniel; Persson, Kristin A.

doi:10.1002/cpe.3505

Title: FireWorks: a Dynamic Workflow System Designed for High-Throughput Applications

Journal Article · Mon May 18 00:00:00 EDT 2015 · Concurrency and Computation. Practice and Experience

DOI:https://doi.org/10.1002/cpe.3505· OSTI ID:1474899

Jain, Anubhav ^[1]; Ong, Shyue Ping ^[2]; Chen, Wei ^[1]; Medasani, Bharat ^[3]; Qu, Xiaohui ^[1]; Kocher, Michael ^[1]; Brafman, Miriam ^[3]; Petretto, Guido ^[4]; Rignanese, Gian-Marco ^[4]; Hautier, Geoffroy ^[4]; Gunter, Daniel ^[3]; Persson, Kristin A. ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Environmental Energy and Technologies Division
Univ. of California, San Diego, CA (United States). Dept. of Nanoengineering
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Computational Research Division
Catholic Univ. of Louvain, Louvain-la-Neuve (Belgium). Inst. of Condensed Matter and Nanosciences (IMCN) and European Theoretical Spectroscopy Facility (ETSF)

This work 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.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Science (SC), Basic Energy Sciences (BES). Joint Center for Energy Storage Research (JCESR); USDOE Office of Science (SC), Biological and Environmental Research (BER); European Union (EU)

Grant/Contract Number:: AC02-05CH11231; EDCBEE; HTforTCOs PCIG11‐GA‐2012‐321988

OSTI ID:: 1474899

Alternate ID(s):: OSTI ID: 1401389

Journal Information:: Concurrency and Computation. Practice and Experience, Vol. 27, Issue 17; Related Information: © 2015 John Wiley & Sons, Ltd.; ISSN 1532-0626

Publisher:: WileyCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 287 works

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