An Implementation of the Linear Scheduling Algorithm in Multiprocessor Systems using Genetic Algorithms
Sung-Hwan Bae, Sang-Bang Choi · Jeongbo gwahaghoe nonmunji. si'seu'tem mich i'lon · 2000
In this paper, we present a linear scheduling method for homogeneous multiprocessor systems using genetic algorithms. In general, genetic algorithms randomly generate initial strings, which leads to long operation time and slow convergence due to an inappropriate initialization. The proposed algorithm considers communication costs among processors and generates initial strings such that successive nodes are grouped into the same cluster. In the crossover and mutation operations, the algorithm maintains linearity in scheduling by associating a node with its immediate successor or predecessor. Linear scheduling can fully utilize the inherent parallelism of a given program and has been proven to be superior to nonlinear scheduling on a coarse grain DAG (directed acyclic graph). This paper emphasizes the usability of the genetic algorithm for real-time applications. Simulation results show that the proposed algorithm rapidly converges within 50 generations in most DAGs.