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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ndltd-TW-089CCU003920272016-07-06T04:09:53Z http://ndltd.ncl.edu.tw/handle/09157041150496221881 Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors 分散式環境下對於資料平行計算可保持其資料連續性之動態負載平衡方法 Chih-Hsuae Yang 楊志學 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. Pangfeng Liu 劉邦鋒 2001 學位論文 ; thesis 33 en_US |
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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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author2 |
Pangfeng Liu |
author_facet |
Pangfeng Liu Chih-Hsuae Yang 楊志學 |
author |
Chih-Hsuae Yang 楊志學 |
spellingShingle |
Chih-Hsuae Yang 楊志學 Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
author_sort |
Chih-Hsuae Yang |
title |
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
title_short |
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
title_full |
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
title_fullStr |
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
title_full_unstemmed |
Locality-Preserving Dynamic Load Balancing for Data-Parallel Applications on Distributed-Memory Multiprocessors |
title_sort |
locality-preserving dynamic load balancing for data-parallel applications on distributed-memory multiprocessors |
publishDate |
2001 |
url |
http://ndltd.ncl.edu.tw/handle/09157041150496221881 |
work_keys_str_mv |
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