Paper Info
Title
An Adaptive Task Granularity Based Scheduling for Task-centric Parallelism
Abstract
Different from data parallel model, task parallel computing model is very important for complex analysis and data mining. Task granularity is a key factor that significantly affects the performance of task-centric parallel programs. However, current task-granularity based solutions either only work well for regular task-parallel programs or are difficult to use. As a result, for irregular task-parallel programs, these solutions may suffer from inappropriate task granularity. To meet this challenge, in this paper, we propose an adaptive task-granularity based scheduling strategy, called ATG. It not only can adaptively switch between help-first and serialization scheduling policies to control task granularity, but also can prevent fine-grained tasks from being executed in parallel to reduce the task-creation overhead. Experiment results show that compared with manual cut-off strategy, the performance of irregular task parallel applications can be improved by ATG strategy up to 19% with low overhead. Meanwhile, for the regular task-parallel applications ATG strategy can even get almost the same performance of the optimal manual cut-off scheme as well.
Year
DOI
Venue
2014
10.1109/HPCC.2014.32
HPCC/CSS/ICESS
Keywords
Field
DocType
task granularity,scheduling,adaptive,atg,parallel programming,adaptive task granularity based scheduling,multicore,serialization scheduling policies,task analysis,task-centric parallel programs,task parallel computing model,task-centric parallelism,help-first scheduling policies,multicore processing,parallel processing,switches
Load management,Serialization,Task parallelism,Computer science,Scheduling (computing),Parallel processing,Parallel computing,Real-time computing,Data parallelism,Granularity,Multi-core processor,Distributed computing
Conference
ISBN
Citations 
PageRank 
978-1-4799-6122-1
1
0.35
References 
Authors
15
6
Name
Order
Citations
PageRank
Jianmin Bi110.35
Xiaofei Liao21145120.57
Yu Zhang36917.13
Chencheng Ye493.82
Hai Jin56544644.63
Laurence T. Yang66870682.61