Result: TS3:Energy-Efficiency-First Optimal Thread Number Search Algorithm Based on Specific Starting Point Classification

Optimal thread number setting is one of the key factors affecting the performance and power consumption of multi-threaded programs.However,current algorithms for finding the optimal number of threads usually start the search from a single fixed point,which cause the problem of low precision and large search overhead.The distribution and location of the optimal number of threads are related to various,factors,including types of programs,optimization objectives(performance,power consumption,and EDP),parallel multi-threaded areas,and software-hardware configuration parameters.This paper focuses on the problem of searching for the optimal number of threads with an emphasis on energy efficiency and proposes an energy-efficiency-first optimal thread number search algorithm based on specific starting point classification(abbreviated as TS3 method).By designing a multi-threaded program classifier to optimize the setting of search starting points,and applying heuristic and binary search algorithms to enhance search efficiency,the method effectively improves the accuracy of the optimal number of threads search under energy efficiency priorities(optimal performance,optimal powerconsumption,optimal EDP) and reduces search costs.The effectiveness of the algorithm is experimentally validated using eight benchmarks on two x86 platforms and one ARM platform.Compared to the baseline,the TS3method achieves an average performance improvement of 0.29%(Platform A),0.17%(Platform B),and 10.77%(Platform C);average power consumption reduction of 2.35%(Platform A),1.87%(PlatformB),and 15.97%(Platform C);and average EDP reduction of 6.36%(Platform A),5.07%(Platform B),and 46.94%(Platform C).Across the three platforms,compared to current classical search methods,the TS3 method demonstrates an average performance improvement of 10.16%,an average reduction in power consumption of 13.45%,and an average reduction in EDP of 23.77%,the search overhead is reduced by 86.8%.