Instruction scheduling using MAX-MIN ant system optimization

Abstract

Instruction scheduling is a fundamental step for mapping an application to a computational device. It takes a behavioral application specification and produces a schedule for the instructions onto a collection of processing units. The objective is to minimize the completion time of the given application while effectively utilizing the computational resources. The instruction scheduling problem is NP-hard, thus effective heuristic methods are necessary to provide a qualitative scheduling solution. In this paper, we present a novel instruction scheduling algorithm using MAX-MIN Ant System Optimization approach. The algorithm utilizes a unique hybrid approach by combining the ant system meta-heuristic with list scheduling, where the local and global heuristics are dynamically adjusted to the input application in an iterative manner. Compared with force-directed scheduling and a number of different list scheduling heuristics, our algorithm generates better results over all the tested benchmarks with better stability. Furthermore, by solving the test samples optimally using ILP formulation, we show that our algorithm consistently achieves a near optimal solution.