Energy-Efficient Thread Mapping for Heterogeneous Many-Core Systems via Dynamically Adjusting the Thread Count

Tao Ju, Yan Zhang, Xuejun Zhang, Xiaogang Du and Xiaoshe Dong
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Tao Ju: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Yan Zhang: School of Media Engineering, Lanzhou University of Arts and Science, Lanzhou 730000, China
Xuejun Zhang: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Xiaogang Du: School of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China
Xiaoshe Dong: School of Electronics and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China

Energies, 2019, vol. 12, issue 7, 1-20

Abstract: Improving computing performance and reducing energy consumption are a major concern in heterogeneous many-core systems. The thread count directly influences the computing performance and energy consumption for a multithread application running on a heterogeneous many-core system. For this work, we studied the interrelation between the thread count and the performance of applications to improve total energy efficiency. A prediction model of the optimum thread count, hereafter the thread count prediction model (TCPM), was designed by using regression analysis based on the program running behaviors and heterogeneous many-core architecture feature. Subsequently, a dynamic predictive thread mapping (DPTM) framework was proposed. DPTM uses the prediction model to estimate the optimum thread count and dynamically adjusts the number of active hardware threads according to the phase changes of the running program in order to achieve the optimal energy efficiency. Experimental results show that DPTM obtains a nearly 49% improvement in performance and a 59% reduction in energy consumption on average. Moreover, DPTM introduces about 2% additional overhead compared with traditional thread mapping for PARSEC (The Princeton Application Repository for Shared-Memory Computers) benchmark programs running on an Intel MIC (Many integrated core) heterogeneous many-core system.

Keywords: heterogeneous many-core system; heterogeneous computing; optimum thread count; prediction model; performance optimization; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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