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Moldable Parallel Job Scheduling Using Job Efficiency: An Iterative Approach

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Job Scheduling Strategies for Parallel Processing (JSSPP 2006)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4376))

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Abstract

Currently, job schedulers require “rigid” job submissions from users, who must specify a particular number of processors for each parallel job. Most parallel jobs can be run on different processor partition sizes, but there is often a trade-off between wait-time and run-time — asking for many processors reduces run-time but may require a protracted wait. With moldable scheduling, the choice of job partition size is determined by the scheduler, using information about job scalability characteristics.We explore the role of job efficiency in moldable scheduling, through the development of a scheduling scheme that utilizes job efficiency information. The algorithm is able to improve the average turnaround time, but requires tuning of parameters. Using this exploration as motivation, we then develop an iterative scheme that avoids the need for any parameter tuning. The iterative scheme performs an intelligent, heuristic based search for a schedule that minimizes average turnaround time. It is shown to perform better than other recently proposed moldable job scheduling schemes, with good response times for both the small and large jobs, when evaluated with different workloads.

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Authors and Affiliations

  1. Dept. of Computer Science and Engineering, The Ohio State University, Columbus OH 43201, USA

    Gerald Sabin, Matthew Lang & P. Sadayappan

Authors
  1. Gerald Sabin
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  2. Matthew Lang
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  3. P. Sadayappan
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Editor information

Eitan Frachtenberg Uwe Schwiegelshohn

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Sabin, G., Lang, M., Sadayappan, P. (2007). Moldable Parallel Job Scheduling Using Job Efficiency: An Iterative Approach. In: Frachtenberg, E., Schwiegelshohn, U. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2006. Lecture Notes in Computer Science, vol 4376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71035-6_5

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  • DOI: https://doi.org/10.1007/978-3-540-71035-6_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-71034-9

  • Online ISBN: 978-3-540-71035-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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