A methodology for efficient code optimizations and memory management

The key to optimizing software is the correct choice, order as well parameters of optimizations-transformations, which has remained an open problem in compilation research for decades for various reasons. First, most of the compilation subproblems-transformations are interdependent and thus addressing them separately is not effective. Second, it is very hard to couple the transformation parameters to the processor architecture (e.g., cache size and associativity) and algorithm characteristics (e.g. data reuse); therefore compiler designers and researchers either do not take them into account at all or do it partly. Third, the search space (all different transformation parameters) is very large and thus searching is impractical. In this paper, the above problems are addressed for data dominant affine loop kernels, delivering significant contributions. A novel methodology is presented that takes as input the underlying architecture details and algorithm characteristics and outputs the near-optimum parameters of six code optimizations in terms of either L1,L2,DDR accesses, execution time or energy consumption. The proposed methodology has been evaluated to both embedded and general purpose processors and for 6 well known algorithms, achieving high speedup as well energy consumption gain values over gcc compiler, hand written optimized code and Polly