Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors

• Main Authors: Chih-Hsuae Yang, 楊志學
• Other Authors: Pangfeng Liu
• Format: Others
• Language: en_US
• Published: 2001
• Online Access: http://ndltd.ncl.edu.tw/handle/09157041150496221881

碩士 === 國立中正大學 === 資訊工程研究所 === 89 === Load balancing and data locality are the two most important factors in the performance of parallel programs on distributed-memory multiprocessors. A good balancing scheme should evenly distribute the workload among the available processors, and locate the tasks close to their data to reduce communication and idle time. In this dissertation, we study the load balancing problem of data-parallel loops with predictable neighborhood data references. The loops are characterized by variable and unpredictable execution time due to dynamic external workload. Nevertheless the data referenced by each loop iteration exploits spatial locality of stencil references. We combine an initial static BLOCK scheduling and a dynamic scheduling based on work stealing. Data locality is preserved by careful restrictions on the tasks that can be migrated. Experimental results on a network of workstations are reported.