NDLI: An Adaptive Task Creation Pruning Strategy for Optimizing Irregular Applications

Content Provider	IET Digital Library
Author	Cao, Qian Li, Huiyong Zuo, Min Deng, Yun Yu, Yuan
Abstract	The OpenMP task directive makes it possible to efficiently parallelize irregular applications, with task granularity as one of the most critical issues. To implement OpenMP specification on multi-core architecture, a model is presented specializing in the execution of irregular applications. The model captures computation and communication within a node with host cores and accelerator cores. Based on this model, we propose an adaptive task creation pruning strategy including two stages to adjust dynamically task granularity. The first stage is task creation in breadth-first manner until getting to a threshold, which utilizes potential parallelism of multi-core processor. The second stage is starvation-triggered task regeneration once some worker thread becomes starved, which ensures work-stealing and thus achieves load balance. The evaluation is conducted with a series of typical irregular benchmarks, and the results indicate that our approach offers more effective performance in parallel execution of irregular benchmarks.
Starting Page	535
Ending Page	541
Page Count	7
ISSN	10224653
Volume Number	24
e-ISSN	20755597
Issue Number	Issue 3, Jul (2015)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/cje/24/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/cje.2015.07.017
Journal	Chinese Journal of Electronics
Publisher Date	2015-07-01
Access Restriction	Open
Rights Holder	© Chinese Institute of Electronics
Subject Keyword	Accelerator Cores Adaptive Task Creation Pruning Strategy Combinatorial Mathematics Host Cores Multicore Architecture Multiprocessing System OpenMP Task Directive Optimisation Optimisation Technique Starvation-triggered Task Regeneration Tree Searching
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Electrical and Electronic Engineering

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