A Performance Predication Model for Parallel Compilers
Ko‐Yang Wang · Purdue e-Pubs (Purdue University System) · 1990
In this paper. we introduce a performance prediction framework for parallel compilers based on the refined combilled characterization model (CCM). The CCM model characterizes the perfonnance of a user program on a target machine by combining a set of characteristic functions called perfonnance factors. Each perfonnance factor represents a particular program aspect that relates program pat~ terns to features of a target architecture and is quantified by an evaluation function. The evaluation functions arc usually inexpensive to compute. so they can be evaluated repeatedly during the parallelism optimization process. The performance prediction framework is highly flexible and can be adjusted with a knOWledge base to fit different needs at different stages of parallel compiling and to accommodate different classes of parallel architectures. The performance prediction model is utilized in an intelligent parallel compiler to guide the program optimization process. Its versatility allows the compiler to offer an adjustable optimization degree and can optimize code for a wide range of target machines.