QoS-Aware Approximate Task Mapping on Heterogeneous Multicore Platforms with DVFS and Task Migration

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Abstract
Heterogeneous Multicore Platforms (HMPs) have been widely adopted to execute tasks across a range of applications. Under limited system resources and diverse application requirements, allocating and executing dependent Approximate Computing (AC) tasks on these platforms to achieve high Quality-of-Service (QoS) is challenging. Dynamic Voltage and Frequency Scaling (DVFS) and task migration have proven effective for improving QoS while balancing time and energy consumption. However, existing approaches often overlook the migration overhead and the resulting dynamic changes in task dependencies, which can adversely affect mapping outcomes. To address these issues, this article presents a novel AC task mapping method that maximizes system QoS under multiple constraints on HMPs, accounting for task migration overhead, DVFS, and changes in Directed Acyclic Graph (DAG) topology. We first formulate this joint design problem as a complex nonlinear programming problem. Next, we linearize the nonlinear terms without performance loss by introducing auxiliary variables and additional constraints. Building on this formulation, we propose an optimal (OPT) and a low-complexity Heuristic Algorithm (HEU), derived from problem decomposition and a greedy strategy, which divides the Mixed-Integer Non-Linear Programming (MINLP) problem into two smaller subproblems with fewer variables and constraints, solving them sequentially. The simulation results show that the proposed OPT method achieves higher QoS performance, measured at about 2.389 times on average and up to 4.115 times, while its feasibility is increased to about 3.263 times on average and up to 9.667 times, compared to other state-of-the-art methods. In addition, the average QoS of the proposed HEU method is about 0.577 times that of the proposed method, but its computation time is over a thousand times shorter.
Funding Information
  • National Key Research and Development Program of China (2022YFF0902800)
  • National Natural Science Foundation of China (62533019)
  • Natural Science Foundation of Jiangsu Province (BK20242028)

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