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An iterative expanding and shrinking process for processor allocation in mixed-parallel workflow scheduling.

Kuo-Chan Huang1, Wei-Ya Wu2, Feng-Jian Wang2

  • 1Department of Computer Science, National Taichung University of Education, No. 140, Min-Shen Road, Taichung, Taiwan.

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|August 10, 2016
PubMed
Summary
This summary is machine-generated.

Scheduling mixed-parallel workflows with moldable tasks (M-tasks) is challenging. The proposed Iterative Allocation Expanding and Shrinking (IAES) approach improves processor allocation for reduced workflow execution makespan.

Keywords:
Mixed parallelismMoldable taskProcessor allocationWorkflow scheduling

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Area of Science:

  • Computer Science
  • Computational Science

Background:

  • Parallel computation is crucial for large-scale scientific and engineering problems.
  • Mixed-parallel workflows, combining task and data parallelism, offer greater efficiency than pure parallelism.
  • Scheduling these workflows presents NP-complete challenges, particularly processor allocation for moldable tasks (M-tasks).

Purpose of the Study:

  • To address the processor allocation challenge in scheduling mixed-parallel workflows of M-tasks.
  • To introduce a novel Iterative Allocation Expanding and Shrinking (IAES) approach for improved scheduling.

Main Methods:

  • The IAES approach allocates more processors to tasks on critical paths to reduce makespan.
  • IAES allows processor allocation to shrink iteratively, enhancing flexibility and effectiveness.
  • The approach was evaluated through simulation experiments and compared against CPR, CPA, MCPA, and MCPA2.

Main Results:

  • The IAES approach significantly outperforms previous methods in most tested scenarios.
  • IAES shows particular effectiveness when workflow layers have unequal workloads.
  • The iterative shrinking of processor allocation contributes to finding better solutions.

Conclusions:

  • The IAES approach offers a significant advancement in mixed-parallel workflow scheduling.
  • IAES provides a more effective and flexible method for processor allocation in M-task scheduling.
  • This method is especially beneficial for complex workflows with variable task demands.