An Effective Iterative Compilation Search Algorithm for High Performance Computing Applications

The performance gap for high performance applications has been widening over time. High level program transformations are critical to improve the applications´ performance, many of which concern the determination of optimal values for transformation parameters, such as loop unrolling and blocking. Traditional compilers select these parameters based on static analytical models. However, complex computer architectures and code behaviors greatly limit the strength of optimizing compilers. Iterative compilation approach determines these parameter values by executing the program with different parameter values and selects the one with the shortest runtime, outperforming static compilation approaches significantly, which makes it a hot research topic in the high performance computing research community. But itpsilas quite time consuming because of the huge optimization space. Therefore, an effective search strategy is crucial for iterative compilation. This paper investigates the Nelder-Mead simplex algorithm for iterative compilation optimization parameter search. Experimental results indicate Nelder-Mead simplex based search strategy can produce parameter values with better performance and lower cost.