A Power‐Efficient Scheduling Approach in a CPU–GPU Computing System by Thread‐Based Parallel Programming

Abstract

CPU–GPU heterogeneous computing platforms are increasingly ubiquitous in mobile embedded systems, including smartphones, tablets, and unmanned aerial vehicles, due to their superior computational throughput. However, given the strict energy limitations inherent to battery‐powered devices, the development of power‐efficient, real‐time computing architectures remains a critical design challenge. This paper proposes a power‐efficient scheduling methodology leveraging thread‐based parallel programming to optimize resource utilization across CPU–GPU architectures using the taskflow framework. By explicitly modeling thread‐level runtime behavior across heterogeneous computation units, the scheduling objective is formulated as an integer nonlinear programming problem aimed at minimizing total system power consumption. A state‐transition framework is derived, and a dynamic programming algorithm is introduced to optimize computing task assignment and computational resource allocation. Extensive empirical evaluations demonstrate that the proposed dynamic programming strategy significantly accelerates convergence and yields superior power‐efficient schedules compared to baseline methodologies, including particle swarm optimization. Furthermore, validation on a physical platform confirms the practical efficacy and accuracy of the proposed scheduling architecture.