基于JavaSpaces技术的并行进化多肽优化中资源利用的模拟
Simulation of Resource Usage in Parallel Evolutionary Peptide Optimization using JavaSpaces Technology
摘要：
多肽优化是一个非常复杂的问题，它需要很长的计算时间。此优化过程使用运行GNU/Linux操作系统的集群中执行特殊任务的许多软件应用程序。组织整个优化过程的应用程序已经开发出来，即SEPP（肽/聚合物进化帕累托优化系统）。单个肽优化需要大量的计算时间来产生一定数量的个体。但是，可以通过增加并行度以及集群中的节点（处理器）数量来加速。在这篇硕士论文中，我建立了一个模型，模拟程序的相互作用，以便可以确定每个资源（处理器）的使用情况，以及整体优化过程所需的近似时间。进化算法主要有两种，即基于代数的进化算法和稳态进化算法。每个进化算法的结果显示的基础上的模拟。此外，还通过使用不同的参数（并行度和处理器的数量）在模拟的结果进行了比较，给出了一个概述的优点和缺点的算法在计算时间和资源使用。该模型使用JavaSpaces技术建立。
英文摘要：
Peptide Optimization is a highly complex problem and it takes very long time of computation. This optimization process uses many software applications in a cluster running GNU/Linux Operating System that perform special tasks. The application to organize the whole optimization process had been already developed, namely SEPP (System for Evolutionary Pareto Optimization of Peptides/Polymers). A single peptide optimization takes a lot of computation time to produce a certain number of individuals. However, it can be accelerated by increasing the degree of parallelism as well as the number of nodes (processors) in the cluster. In this master thesis, I build a model simulating the interplay of the programs so that the usage of each resource (processor) can be determined and also the approximated time needed for the overall optimization process. There are two Evolutionary Algorithms that could be used in the optimization, namely Generation-based and Steady-state Evolutionary Algorithm. The results of each Evolutionary Algorithm are shown based on the simulations. Moreover, the results are also compared by using different parameters (the degree of parallelism and the number of processors) in the simulation to give an overview of the advantages and the disadvantages of the algorithms in terms of computation time and resource usage. The model is built up using JavaSpaces Technology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
