Computer Science ›› 2016, Vol. 43 ›› Issue (5): 22-26.doi: 10.11896/j.issn.1002-137X.2016.05.004

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Branch Divergence Optimization for Performance and Power Consumption on GPU Platform

YU Qi, WANG Bo-qian, SHEN Li, WANG Zhi-ying and CHEN Wei   

  • Online:2018-12-01 Published:2018-12-01

Abstract: Because of the tremendous computing power,general purpose graphics processing units(GPGPUs) have been widely accepted in general purpose computing area.However,as GPGPUs using an execution model called SIMT(Single Instruction Multiple Threads),their efficiency is subject to the presence of branch divergence in a GPU application.People have proposed a method based on thread swapping to reduce the performance loss brought by branch divergence,but these methods always bring extra memory accesses in return,which not only decrease the performance gains to a certain degree,but also increase power consumption.Firstly,an example was used to explain the influence thread swapping range has on performance and power consumption of a program.Secondly,a method was proposed to reduce the extra memory accesses brought by thread swapping.Experiments show that,for Reduction,this method reduces power consumption by 7% with average performance loss by 4% when swapping range is 256.While for Bitonic,this method improves performance by 6.4% and 5.3% when swapping range is 256 and 512 with no power consumption overheads,respectively.

Key words: Branch divergence,Memory access,Thread swapping

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