Flexible CoScheduling : mitigating load imbalance and improving utilization of heterogeneous resources

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Fine-grained parallel applications require all their processes to run simultaneously on distinct processors to achieve good efficiency. This is typically accomplished by space slicing, wherein nodes are dedicated for the duration of the run, or by gang scheduling, wherein time slicing is coordinated across processors. Both schemes suffer from fragmentation, where processors are left idle because jobs cannot be packed with perfect efficiency. Obviously, this leads to reduced utilization and sub-optimal performance. Flexible coscheduling (FCS) solves this problem by monitoring each job's granularity and communication activity, and using gang scheduling only for those jobs that require it. Processes from other ... continued below

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13 p.

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Frachtenberg, E. (Eitan); Feitelson, Dror G.; Petrini, F. (Fabrizio) & Fernández, J. C. (Juan C.) January 1, 2002.

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Description

Fine-grained parallel applications require all their processes to run simultaneously on distinct processors to achieve good efficiency. This is typically accomplished by space slicing, wherein nodes are dedicated for the duration of the run, or by gang scheduling, wherein time slicing is coordinated across processors. Both schemes suffer from fragmentation, where processors are left idle because jobs cannot be packed with perfect efficiency. Obviously, this leads to reduced utilization and sub-optimal performance. Flexible coscheduling (FCS) solves this problem by monitoring each job's granularity and communication activity, and using gang scheduling only for those jobs that require it. Processes from other jobs, which can be scheduled without any constraints, are used as filler to reduce fragmentation. In addition, inefficiencies due to load imbalance and hardware heterogeneity are also reduced because the classification is done on a per-process basis. FCS has been fully implemented as part of the STORM resource manager, and shown to be competitive with gang scheduling and implicit coscheduling. Keywords: Cluster computing, load balancing, job scheduling, gang scheduling, parallel architectures, heterogeneous clusters, STORM

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13 p.

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  • Submitted to: International Parallel and Distributed Processing Symposium, Nice, France, April 2003

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  • Report No.: LA-UR-02-6505
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 976391
  • Archival Resource Key: ark:/67531/metadc931350

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  • January 1, 2002

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 3:55 p.m.

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Frachtenberg, E. (Eitan); Feitelson, Dror G.; Petrini, F. (Fabrizio) & Fernández, J. C. (Juan C.). Flexible CoScheduling : mitigating load imbalance and improving utilization of heterogeneous resources, article, January 1, 2002; United States. (digital.library.unt.edu/ark:/67531/metadc931350/: accessed November 18, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.