Using Model and Iterative Compilation to Optimize Matrix Multiplication Applications
Yonggang Che · Computer Engineering and Science · 2009
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.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 approaches significantly.But it's quite time consuming because of the huge optimization space.Aiming at matrix multiplication program,this paper presents a combinative method to solve this problem by investigating a constraint model on optimization parameters to limit the optimization space,and then applying genetic algorithms to search the optimal parameters.Experimental results indicate our approach can produce parameter values with better performance and lower cost.