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JISE
2002

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

8 years 6 months ago
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors
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 so as to reduce communication and idle time. In this paper, 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.
Pangfeng Liu, Jan-Jan Wu, Chih-Hsuae Yang
Added 22 Dec 2010
Updated 22 Dec 2010
Type Journal
Year 2002
Where JISE
Authors Pangfeng Liu, Jan-Jan Wu, Chih-Hsuae Yang
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