Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors
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...
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| Other Authors: | |
| Format: | Others |
| Language: | en_US |
| Published: |
2001
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| Online Access: | http://ndltd.ncl.edu.tw/handle/09157041150496221881 |
| Summary: | 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.
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