Abstract
The mission of the DOE Argonne Leadership Computing Facility (ALCF) is to accelerate major scientific discoveries and engineering breakthroughs for humanity by designing and providing world-leading computing facilities in partnership with the computational science community. The ALCF operates supercomputers that are generally amongst the Top 5 fastest machines in the world. Specifically, ALCF is looking for the science that is either too big to run anywhere else, or it would take so long as to be impractical (i.e., “capability jobs”). At ALCF, batch scheduling plays a critical role for achieving a set of site goals within a set of constraints. While system utilization is an important goal at ALCF, its largest mission constraint is to enable extreme scale parallel jobs to take precedence. In this paper, we will describe the specific scheduling goals and constraints, analyze the workload traces collected in 2013–2017 from the 48-rack petascale supercomputer Mira, and discuss the upcoming scheduling challenges at ALCF.
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References
Argonne National Laboratory. http://www.anl.gov/
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Acknowledgement
This research used resources of the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357. Zhiling Lan is supported in part by US National Science Foundation grants CNS-1320125 and CCF-1422009.
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Allcock, W., Rich, P., Fan, Y., Lan, Z. (2018). Experience and Practice of Batch Scheduling on Leadership Supercomputers at Argonne. In: Klusáček, D., Cirne, W., Desai, N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2017. Lecture Notes in Computer Science(), vol 10773. Springer, Cham. https://doi.org/10.1007/978-3-319-77398-8_1
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DOI: https://doi.org/10.1007/978-3-319-77398-8_1
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