CAO Qian, LI Huiyong, ZUO Min, DENG Yun, YU Yuan. An Adaptive Task Creation Pruning Strategy for Optimizing Irregular Applications[J]. Chinese Journal of Electronics, 2015, 24(3): 535-541. doi: 10.1049/cje.2015.07.017
Citation: CAO Qian, LI Huiyong, ZUO Min, DENG Yun, YU Yuan. An Adaptive Task Creation Pruning Strategy for Optimizing Irregular Applications[J]. Chinese Journal of Electronics, 2015, 24(3): 535-541. doi: 10.1049/cje.2015.07.017

An Adaptive Task Creation Pruning Strategy for Optimizing Irregular Applications

doi: 10.1049/cje.2015.07.017
Funds:  This work is supported by the Scientific Research Common Program of Beijing Municipal Commission of Education (No.KM201410011005), the National Natural Science Foundation of China (No.61170113), the National Basic Research Program (973) of China (No.2012CB821200, No.2012CB821206), and Project Foundation of Guangxi Experiment Center of Information Science, Guilin University of Electronic Technology (No.20130206).
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  • Corresponding author: LI Huiyong (corresponding author) was born in Hebei province in 1983. He received the Ph.D degree in computer application from Beihang University. He is now a postdoctor at Peking University. His research interests include high-performance video process and embedded system design. (Email:
  • Received Date: 2014-12-26
  • Rev Recd Date: 2015-02-03
  • Publish Date: 2015-07-10
  • 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.
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