A structured grid load balancing method based on LPT local optimization

A structured grid and local optimization technology, applied in gene models, resource allocation, multi-programming devices, etc., can solve the problems of intelligent optimization algorithms that cannot obtain good solutions, reduce parallel computing efficiency, and low computing efficiency

Active Publication Date: 2019-06-14
NAT UNIV OF DEFENSE TECH
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  • Abstract
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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
[0081] (1) The deterministic method is fast, but the load

Method used

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  • A structured grid load balancing method based on LPT local optimization
  • A structured grid load balancing method based on LPT local optimization
  • A structured grid load balancing method based on LPT local optimization

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Embodiment Construction

[0166] figure 2 Is the overall flow chart of the present invention. Such as figure 2 Shown, the present invention comprises the following steps:

[0167] The first step, parameter configuration:

[0168] 1.1 Obtain the input file location, population size popNum, maximum iteration number IteMax, balance rate threshold ε, crossover probability Pcross, mutation probability Pvari, maximum repetition SameMax, and LPT number LPTSize from the configuration file.

[0169] 1.2 Make the number of repetitions of the optimal fitness value nSame=0, and make the old optimal fitness value

[0170] The second step is to initialize the population.

[0171] 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 chromosome...

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Abstract

The invention discloses a structured grid load balancing method based on LPT local optimization, and aims to overcome the defects of an existing load balancing method and improve the load balancing rate and the calculation speed. The technical scheme is based on a genetic algorithm on the whole, and includes totally eleven steps of configuring parameters, initializing populations, calculating theadaptability, carrying out the local optimization on the LPTSize/2 chromosome fragments with the maximum fragment value and the LPTSize/2 chromosome fragments with the minimum fragment value in each chromosome by adopting an LPT method, carrying out the condition judgment, carrying out update judgment, performing population updating, selecting operators, crossing the operators and varying the operators, and outputting the best load balancing mode. According to the invention, a plurality of chromosome fragments with maximum and minimum chromosome fragment values in each chromosome are locally optimized; the fitness of the whole chromosome is improved, the population updating is carried out, the population is not prone to premature, a program is not prone to ending too early, a globally optimal solution can be obtained, and the parallel computing load balance rate of the whole structured grid is improved.

Description

technical field [0001] The invention relates to a load balancing method for improving structured grid parallel computing, especially a parallel load balancing method based on genetic algorithm and LPT (Largest Processing Time, maximum processing time) 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...

Claims

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Application Information

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IPC IPC(8): G06F9/50G06N3/12
Inventor 龚春叶刘杰杨博甘新标李胜国徐海坤李润华穆利安吕书邻穆雨桐
Owner NAT UNIV OF DEFENSE TECH
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