Software Pipelining via Stochastic Search Algorithms

The scheduling of loops for architectures which support instruction level parallelism is an important area of research. Many polynomial time, heuristic algorithms for software pipelining have been proposed for this NP-complete problem. In this research, genetic algorithms and simulated annealing are used to test the feasibility of applying artiicial intelligence techniques to the problem of software pipelining. Both algorithms are iterative search algorithms which adjust their response based on feedback from the tness function. Results indicate these techniques are superior to deterministic polynomial time algorithms.