论文标题
超级计算机资源经理的三种作业映射算法的比较
Comparison of Three Job Mapping Algorithms for Supercomputer Resource Managers
论文作者
论文摘要
超级计算机的性能取决于资源管理器的质量，其功能之一是将作业分配给集群或MPP计算机的节点。并行程序的一部分相互互动，并以不同的强度相互作用，并且将程序映射到超级计算机节点会影响运行的效率。在每个程序运行图中，代表应用程序程序的图表应映射到代表计算机系统子集的节点的图。这两个图都不是事先知道的，因此在安排资源时必须在合理的时间内完成映射。探索了三种映射算法：模拟退火，遗传和复合算法的平行版本。进行了一组具有不同算法参数的实验运行，对映射质量和运行时进行了比较，并提供了有关资源管理算法的适用性的建议。
Performance of supercomputer depends on the quality of resource manager, one of its functions is assignment of jobs to the nodes of clusters or MPP computers. Parts of parallel programs interact with each other with different intensity, and mapping of program to supercomputer nodes influence efficiency of the run. At each program run graph representing application program is to be mapped onto graph of nodes representing a subset of computer system. The both graphs are not known beforehand, hence the mapping must be done in reasonable time while scheduling resources. Three mapping algorithms were explored: parallel versions of simulated annealing, genetic and composite algorithms. A set of experimental runs with different algorithms parameters was performed, comparison of mapping quality and runtime was made, and suggestions on applicability of algorithms for resource managers were provided.
