Source code transformation based on software cost analysis

This paper presents a model and a strategy for source-code transformation applied to software application programs to reduce their energy cost. We propose a flexible performance and energy model for a processor-memory system. The benefit of the model is generality (it is not tied to a single memory and processor architecture) and effectiveness of evaluation. With this model, we first estimate the effects of source-code transformations (called transformation cost), representing the improvement ratios of processor cycles, I-cache misses, and D-cache misses. Next, we combine the transformation cost model with hardware parameters to estimate the actual effect of a transformation on performance and energy. The model can be used to guide software transformation selection for power and performance. The experimental results show that the proposed approach finds the optimal transformation in 95% of the cases, and that the penalty when the non-optimal transformation is selected is within 5%.