Abstract
The performance of supercomputer schedulers is greatly affected by the characteristics of the workload it serves. A good understanding of workload characteristics is always important to develop and evaluate different scheduling strategies for an HPC system. In this paper, we present a comprehensive analysis of the workload characteristics of Kraken, the world’s fastest academic supercomputer and 11th on the latest Top500 list, with 112,896 compute cores and peak performance of 1.17 petaflops. In this study, we use twelve-month workload traces gathered on the system, which include around 700 thousand jobs submitted by more than one thousand users from 25 research areas. We investigate three categories of the workload characteristics: 1) general characteristics, including distribution of jobs over research fields and different queues, distribution of job size for an individual user, job cancellation rate, job termination rate, and walltime request accuracy; 2) temporal characteristics, including monthly machine utilization, job temporal distributions for different time periods, job inter-arrival time between temporally adjacent jobs and jobs submitted by the same user; 3) execution characteristics, including distributions of each job attribute, such as job queuing time, job actual runtime, job size, and memory usage, and the correlations between these job attributes. This work provides a realistic basis for scheduler design and comparison by studying the supercomputer’s workload with new approaches such as using Gaussian mixture model, and new viewpoints such as from the perspective of user community. To the best of our knowledge, it’s the first research to systematically investigate the workload characteristics of a petascale supercomputer that is dedicated to open scientific research.
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You, H., Zhang, H. (2013). Comprehensive Workload Analysis and Modeling of a Petascale Supercomputer. In: Cirne, W., Desai, N., Frachtenberg, E., Schwiegelshohn, U. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2012. Lecture Notes in Computer Science, vol 7698. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35867-8_14
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DOI: https://doi.org/10.1007/978-3-642-35867-8_14
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