IPLS: an intelligent parallel loop scheduling for multiprocessor systems

Abstract

We propose a knowledge based approach for solving loop scheduling problems. A rule based system, called the IPLS, is developed by repertory grid and attribute ordering table to construct the knowledge base. The IPLS chooses an appropriate scheduling algorithm by inferring some features of loops and assigns parallel loops on multiprocessors for achieving high speedup. In addition, the refined system of IPLS can automatically adjust the attributes in a knowledge base according to profile information; therefore IPLS has feedback learning ability.