A Comprehensive Perspective on Pilot-Job Systems
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Abstract (arXiv)
Pilot-Job systems play an important role in supporting distributed scientific computing. They are used to consume more than 700 million CPU hours a year by the Open Science Grid communities, and by processing up to 1 million jobs a day for the ATLAS experiment on the Worldwide LHC Computing Grid. With the increasing importance of task-level parallelism in high-performance computing, Pilot-Job systems are also witnessing an adoption beyond traditional domains. Notwithstanding the growing impact on scientific research, there is no agreement upon a definition of Pilot-Job system and no clear understanding of the underlying abstraction and paradigm. Pilot-Job implementations have proliferated with no shared best practices or open interfaces and little interoperability. Ultimately, this is hindering the realization of the full impact of Pilot-Jobs by limiting their robustness, portability, and maintainability. This paper offers a comprehensive analysis of Pilot-Job systems critically assessing their motivations, evolution, properties, and implementation. The three main contributions of this paper are: (i) an analysis of the motivations and evolution of Pilot-Job systems; (ii) an outline of the Pilot abstraction, its distinguishing logical components and functionalities, its terminology, and its architecture pattern; and (iii) the description of core and auxiliary properties of Pilot-Jobs systems and the analysis of seven exemplar Pilot-Job implementations. Together, these contributions illustrate the Pilot paradigm, its generality, and how it helps to address some challenges in distributed scientific computing.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 17 Aug 2015.
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Citing papers per year: 2021: 2; 2022: 0; 2023: 0; 2024: 0; 2025: 1.
3 papers.
- 2021Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulationscited by 8
- 2021BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodescited by 3
- 2025Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applicationscited by 0
- 2025Scalable Runtime Architecture for Data-driven, Hybrid HPC and ML Workflow Applications17 Mar 2025
- 2021BFTrainer: Low-Cost Training of Neural Networks on Unfillable Supercomputer Nodes22 Jun 2021
- 2021Coupling streaming AI and HPC ensembles to achieve 100-1000x faster biomolecular simulations10 Apr 2021