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Load Balancing Method for Parallel Computing in Structured Grid Based on Minmax Local Optimization

A structured grid and local optimization technology, applied in genetic models, multi-programming devices, resource allocation, etc., can solve problems such as uncertain load balancing effects, intelligent optimization algorithms that cannot obtain good solutions, and calculation divergence

Active Publication Date: 2020-08-25
NAT UNIV OF DEFENSE TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] (1) Too fine a block will lead to a large increase in additional communication overhead and reduce the efficiency of parallel computing
[0007] (2) Too many sub-regions (blocks) will increase the number of iterative solutions and affect the calculation efficiency, which is determined by the characteristics of the region decomposition algorithm itself
[0008] (3) Engineering calculation is not only a science, but also an art. It is not only related to the numerical calculation method, but also related to the grid. Inappropriate block division may lead to calculation divergence
[0075] (1) The deterministic method is fast, but the load balancing effect is uncertain, sometimes the effect is particularly good, but most of the effect is not good
[0076] (2) The intelligent optimization algorithm is relatively primitive, and the problems of poor robustness and low computational efficiency of the corresponding intelligent optimization algorithm are not considered
[0077] (3) The hybrid algorithm works better, but there are still problems of low computational efficiency and unobvious effect of load balancing optimization
The population generated by the deterministic method often leads to the failure of the intelligent optimization algorithm to obtain a good solution.

Method used

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  • Load Balancing Method for Parallel Computing in Structured Grid Based on Minmax Local Optimization
  • Load Balancing Method for Parallel Computing in Structured Grid Based on Minmax Local Optimization

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Embodiment Construction

[0157] figure 2 Is the overall flow chart of the present invention. like figure 2 Shown, the present invention comprises the following steps:

[0158] The first step, parameter configuration:

[0159] 1.1 Obtain the input file location, population size popNum, maximum iteration number IteMax, balance rate threshold ε, crossover probability Pcross, mutation probability Pvari, and maximum repetition number SameMax from the configuration file.

[0160] 1.2 Make the number of repetitions of the optimal fitness value nSame=0, and make the old optimal fitness value

[0161] The second step is to initialize the population.

[0162] 2.1 Read all grid blocks from the input file, and randomly assign all grid blocks to M processes. A grid block corresponds to a gene, and the number of grids in the grid block is the value of the gene. Generate a population PopA containing popNum chromosomes, PopA={R 1 ,...,R n ..., R popNum}, popNum is the number of chromosomes in PopA, 1≤n≤p...

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Abstract

The invention discloses a structured grid load balancing method based on MINMAX local optimization, and aims to overcome the defects of an existing load balancing method and improve the load balancingrate and the calculation speed. The technical scheme is based on a genetic algorithm on the whole. The method comprises eleven steps of parameter configuration, population initialization, fitness calculation, migration optimization on two maximum and minimum chromosome fragments in each chromosome by adopting a MINMAX method, condition judgment, update judgment, population update, operator selection, cross operators, mutation operators and output of the best load balance mode. According to the invention, migration optimization is carried out on the maximum chromosome segment and the minimum chromosome segment in each chromosome. By means of the method, the chromosome fitness is better, due to population updating, the population is not prone to premature, a program stops too early, a globally optimal solution can be obtained, and the parallel computing load balance rate of the whole structured grid is increased.

Description

technical field [0001] The invention relates to a load balancing method for improving structured grid parallel computing, in particular to a parallel load balancing method based on genetic algorithm and MINMAX (maximum and minimum) local optimization. Background technique [0002] Computation has been juxtaposed with theory and experiment as the three main research methods for human beings to understand the world, and it is mainly used to solve problems that are impossible to conduct experiments or that are too expensive to conduct experiments. In recent decades, with the in-depth understanding of physical laws and the needs of engineering applications, engineering computing has developed into a specialized discipline, which has been widely used in aerospace, automobiles, environmental engineering, materials, physics and ships, etc. . The engineering calculation process is mainly to iteratively calculate the characteristic quantities on the grid, and the number of grids is ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F9/50G06N3/12
Inventor 杨博龚春叶刘杰甘新标李胜国孙泽文李彪朱肖雄谢佩珍张庆阳
Owner NAT UNIV OF DEFENSE TECH
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