摘要
【目的】将混合蛙跳算法的求解过程转化为CUDA线程,提出并研究基于GPU的并行混合蛙跳算法,加快算法寻优过程,提高混合蛙跳算法的运算速度,以此促进群体智能优化算法的并行研究及应用。【方法】本文采用了CPU+GPU异构形式进行计算,其中GPU负责对大规模的密集型数据进行设计分析以及计算,而对于CPU来讲,负责开展事务管理以及复杂逻辑运算等不适合数据并行的计算模块。【结果】将混合蛙跳算法的求解过程转化为CUDA线程,实现基于GPU的并行混合蛙跳算法。在GPU上加速执行以提高算法运行速度,在保证与串行混合蛙跳算法相同优化性能的同时提高加速比。【结论】(1)对于ISFLA算法它采用了并行调度的形式展开计算分析,对于虚拟机之间的负载起到了很好的平衡作用,减小了负载间的平衡度对于整体的工作时间来讲起到了很好的缩短作用。(2)ISFLA算法产生的初始种群有着更好的质量,这能够将一些表现不好的个体进行排除,加快了整体的收敛速度,减小了进行搜索迭代的时长。
[Objective] Transform the solution process of the hybrid frog leap algorithm into a CUDA thread, propose and study a parallel hybrid frog leap algorithm based on GPU, speed up the algorithm optimization process, increase the operation speed of the hybrid frog leap algorithm, and promote parallel research and application of swarm intelligent optimization. [Method] It adopts the CPU + GPU heterogeneous model. The CPU is responsible for performing complex logic processing and transaction management that are not suitable for data parallel computing. The GPU is mainly responsible for computing-intensive large-scale data parallel computing. [Results] The solution process of the hybrid frog leap algorithm is transformed into a CUDA thread, and a parallel hybrid frog leap algorithm based on GPU is realized. Accelerate the execution on the GPU to increase the speed of the algorithm, and improve the speedup while ensuring the same optimized performance as the serial hybrid frog leap algorithm. [Conclusion](1) The ISFLA algorithm uses a parallel scheduling model to execute tasks, which effectively balances the load between virtual machines, reduces the load balance degree, and shortens the overall completion time of the workflow.(2) The quality of the initial population generated by ISFLA is better, which can effectively exclude some poorly performing individuals, thereby shortening the search iteration time and accelerating the convergence speed.
作者
牛宝童
钱宇浛
NIU Bao-tong;QIAN Yu-han(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,Gansu,China;China Aerospace Science and Technology Corporation,Ninth Research Institute,Beijing 100094,China)
出处
《软件》
2020年第7期152-158,共7页
Software
基金
甘肃农业大学学科建设专项项目(GAU-XKJS-2018-251)。
关键词
混合蛙跳算法
图形处理器
统一计算设备架构
群体智能优化算法
Hybrid frog jumping algorithm
graphics processor
unified computing device architecture
swarm intelligence optimization algorithm
