GAPS: Iterative Feedback Directed Parallelisation Using Genetic Algorithms

The compilation of FORTRAN programs for SPMD execution on parallel architectures often requires the application of program restructuring transformations such as loop interchange, loop distribution, loop fusion, loop skewing and statement reordering. Determining the optimal transformation sequence that minimises execution time for a given program is an NP-complete problem. The hypothesis of the research described here is that genetic algorithm (GA) techniques can be used to determine the sequence of restructuring transformations which are better, or, as good as, those produced by more conventional compiler search techniques. The Genetic Algorithm Parallelisation System (GAPS) compiler framework is presented. GAPS uses a novel iterative feedback directed approach to autoparallelisation that is based upon genetic algorithm optimisation. Traditional restructuring transformations are represented as mappings that are applied to each statement and its associated iteration space. The hypothesis of GAPS is tested with a comparison of the performance of SPMD code produced by PFA, petit from the Omega Project at the University of Maryland and GAPS for an ADI 1024 1024 benchmark on an SGI Origin 2000. Encouraging initial results show that GAPS delivers performance improvements of up to 44% when the execution times of code produced by GAPS, PFA, PETIT from the Omega Project are compared for an ADI 1024 1024 benchmark on an SGI Origin2000. On this benchmark, GAPS produces code having 21 25% improvements in parallel execution time.