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Multiprocessor task scheduling is an important and computationally difficultproblem. This paper proposes a comparison study of genetic algorithm and listscheduling algorithm. Both algorithms are naturally parallelizable but haveheavy data dependencies. Based on experimental results, this paper presents adetailed analysis of the scalability, advantages and disadvantages of eachalgorithm. Multiprocessors have emerged as a powerful computing means forrunning real-time applications, especially where a uni-processor system wouldnot be sufficient enough to execute all the tasks. The high performance andreliability of multiprocessors have made them a powerful computing resource.Such computing environment requires an efficient algorithm to determine whenand on which processor a given task should execute. In multiprocessor systems,an efficient scheduling of a parallel program onto the processors thatminimizes the entire execution time is vital for achieving a high performance.This scheduling problem is known to be NP- Hard. In multiprocessor schedulingproblem, a given program is to be scheduled in a given multiprocessor systemsuch that the program's execution time is minimized. The last job must becompleted as early as possible. Genetic algorithm (GA) is one of the widelyused techniques for constrained optimization.

A Performance Study of GA and LSH in Multiprocessor Job Scheduling