Comparison of Three Job Mapping Algorithms for Supercomputer Resource Managers

TL;DR

This paper compares three algorithms—simulated annealing, genetic, and composite—for mapping jobs onto supercomputer nodes, aiming to improve efficiency and resource management in high-performance computing environments.

Contribution

It introduces a comparative analysis of three different job mapping algorithms, providing insights into their performance and suitability for supercomputer resource management.

Findings

Simulated annealing outperforms others in mapping quality.

Genetic algorithm offers faster runtime with moderate quality.

Composite algorithm balances quality and speed effectively.

Abstract

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.