A low-energy task scheduling strategy for cpu-gpu heterogeneity

Abstract

A low-energy task scheduling strategy for CPU-GPU heterogeneity. Aiming at the characteristics of heterogeneous multi-core systems and the problems of traditional ant colony algorithm that only optimizes a single objective and the convergence speed is too slow, a new method that pays attention to real-time constraints is proposed. and ant colony task scheduling algorithm for system energy consumption. The method firstly provides guidance information in the process of pheromone initialization according to the energy consumption of tasks on heterogeneous cores to speed up the algorithm convergence speed, and then filters the cores according to the real-time constraints of tasks, and then according to the computing power of tasks on heterogeneous cores. According to the energy consumption of different tasks, the energy consumption of inter-core communication and the pheromone content of different tasks to select the appropriate execution core, and finally through the multiple iterations of the ant colony algorithm, the scheduling scheme with lower energy consumption is continuously searched, and the pheromone content is adjusted according to the obtained results. Speed ​​up algorithm convergence. After several iterations, the final task scheduling scheme is obtained, which can optimize the energy consumption of the system under the condition of satisfying the real-time constraints of the task.

Description

technical field [0001] The invention belongs to the field of heterogeneous system task allocation and resource scheduling, and specifically relates to a resource allocation and task scheduling strategy oriented to CPU-GPU heterogeneous system structure while considering real-time constraints and system energy consumption. Background technique [0002] Nowadays, the processor architecture is gradually developing towards multi-core and heterogeneous. Multi-core processors have become the current mainstream processors. With multiple processing cores, the application program can deliver parallel threads to multiple cores for processing separately, so the running speed of the program is greatly improved. According to whether there are differences in their core structures, multi-core processors are divided into homogeneous multi-core processors and heterogeneous multi-core processors. A homogeneous multi-core processor mostly means that all processor cores have the same architec...

AI Technical Summary

Problems solved by technology

Zhang Jing et al. proposed a real-time task scheduling algorithm suitable for heterogeneous multi-core systems by remodeling the heterogeneous core according to the processing capability and scope of application, and referring to the main idea of ​​the relatively strict task scheduling algorithm. It has indeed achieved good results in terms of real-time performance, but the scope of application of processing tasks is still limited by high-real-time processing methods, which is not flexible enough

Bai Enci and others also proposed a heterogeneous multi-core task scheduling algorithm based on ant colony algorithm, and also considered real-time constraints when improving ant colony algorithm, and initially combined real-time constraints with performance-oriented ant colony algorithm up, but did not take into account the soft real-time requirements and the lack of information in the initial stage of the ant colony algorithm

[0005] To sum up, although these improved task and resource scheduling strategies can be applied to heterogeneous multi-core, and some real-time considerations are considered, the distinction between heterogeneous cores is still not detailed enough, and most of them are aimed at improving performance. It does not consider that the ant colony algorithm can be used to search for a feasible solution that satisfies the real-time constraints and no longer optimizes the task scheduling length, but optimizes the overall energy consumption.

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