Multicore system task scheduling method based on particle swarm optimization

A particle swarm algorithm and task scheduling technology, applied in computing, computers, computing models, etc., can solve problems such as slow convergence speed, and achieve the effect of accelerating iteration speed, reducing communication overhead, and optimizing system performance

Inactive Publication Date: 2017-09-05
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

In fact, the multi-task scheduling problem is an NP problem
However, due to the lack of pheromones in the initial stage, the convergence speed of this algorithm is very slow at the beginning

Method used

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  • Multicore system task scheduling method based on particle swarm optimization
  • Multicore system task scheduling method based on particle swarm optimization
  • Multicore system task scheduling method based on particle swarm optimization

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Embodiment Construction

[0017] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0018] please see figure 1 , a kind of multi-core system task scheduling method based on particle swarm algorithm provided by the present invention comprises the following steps:

[0019] Step 1: Construct a task scheduling model and generate a directed acyclic graph DAG;

[0020] All processor cores in a multi-core processor form a parallel system, and the task scheduling model is converted into a mathematical model using a directed acyclic graph DAG. The directed acyclic graph DAG can be described as G=(V,E,C,W), in:

[0021] V is a collection of vertice...

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Abstract

The invention discloses a multicore system task scheduling method based on particle swarm optimization, comprising the steps of constructing a task scheduling model to generate an DAG (directed acyclic graph), initializing basic parameters and pheromones, updating the pheromones to calculate inter-task communication time and fitness value, and generating a global optimal solution. Ant colony optimization and particle swarm optimization are combined by effectively initializing the pheromones, and a heuristic intelligent optimization algorithm is used to efficiently schedule tasks for a multicore system. The method of the invention has the advantages that task scheduling time is shortened, communications cost is lowered, and defects of a unilateral algorithm are made up.

Description

technical field [0001] The invention belongs to the technical field of multi-core system task scheduling methods, in particular to a multi-core system task scheduling method based on a particle swarm algorithm. Background technique [0002] In order to improve the system performance of computers and deal with energy consumption problems, research on computer processors has been developed from single-core to multi-core. Completing computing tasks through the parallel work of multiple processor cores has become a performance bottleneck in solving processor problems. Task scheduling is one of the factors that determine the performance of a computer system. When multiple processor cores are integrated on a single chip, it is very important to schedule multi-tasks among these processor cores. [0003] In a multi-core system, the execution order of parallel programs depends on the assignment of tasks to each processor core, so a good set of rules and strategies is required to fo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F9/48G06F15/177G06N3/00
CPCG06F9/4881G06F15/177G06N3/006
Inventor 张瑜胡威鲁俊良张鸿刘小明张凯王悦李亚鑫
Owner WUHAN UNIV OF SCI & TECH
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