Performance-based parallel loop self-scheduling on heterogeneous multicore PC clusters

  • Authors:

  • Chao-Tung Yang;Jen-Hsiang Chang;Chao-Chin Wu

  • Affiliations:

  • High-Performance Computing Laboratory, Department of Computer Science, Tunghai University, Taichung, Taiwan ROC;High-Performance Computing Laboratory, Department of Computer Science, Tunghai University, Taichung, Taiwan ROC;Department of Computer Science and Information Engineering, National Changhua University of Education, Changhua, Taiwan

  • Venue:
  • HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
  • Year:
  • 2009

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Abstract

In recent years, Multicore computers have been widely included in cluster systems. They adopt shared memory architectures. However, previous researches on parallel loop self-scheduling did not consider the feature of multicore computers. It is more suitable for shared-memory multiprocessors to adopt OpenMP for parallel programming. In this paper, we propose a performance-based approach that partitions loop iterations according to the performance weighting of cluster nodes. Because the iterations assigned to one MPI process will be processed in parallel by OpenMP threads running by the processor cores in the same computational node, the number of loop iterations to be allocated to one computational node at each scheduling step also depends on the number of processor cores in that node. Experimental results show that the proposed approach performs better than previous schemes.