A Performance-based Parallel Loop Self-Scheduling Using Hybrid OpenMP and MPI Programming on Multicore SMP Clusters

• Main Authors: Jen-Hsiang Chang, 張壬相
• Other Authors: Chao-Tung Yang
• Format: Others
• Language: en_US
• Published: 2009
• Online Access: http://ndltd.ncl.edu.tw/handle/34506415836160772354

碩士 === 東海大學 === 資訊工程與科學系碩士在職專班 === 97 === Parallel loop self-scheduling on parallel and distributed systems has been a critical problem and it is becoming more difficult to deal with in the emerging heterogeneous cluster computing environments. In the past, some self-scheduling schemes have been proposed as applicable to heterogeneous cluster computing environments. In recent years, multicore computers have been widely included in cluster systems. However, previous researches on parallel loop self-scheduling did not consider the features 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, by using hybrid OpenMP and MPI parallel programming, that partitions loop iterations according to the performance weighting of multicore nodes in a cluster. 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.