A model-based framework: an approach for profit-driven optimization

Although optimizations have been applied for a number of years to improve the performance of software, problems that have been long-standing remain, which include knowing what optimizations to apply and how to apply them. To systematically tackle these problems, we need to understand the properties of optimizations. In our current research, we are investigating the profitability property, which is useful for determining the benefit of applying an optimization. Due to the high cost of applying optimizations and then experimentally evaluating their profitability, we use an analytic model framework for predicting the profitability of optimizations. In this paper, we target scalar optimizations, and in particular, describe framework instances for partial redundancy elimination (PRE) and loop invariant code motion (LICM). We implemented the framework for both optimizations and compare profit-driven PRE and LICM with a heuristic-driven approach. Our experiments demonstrate that a model-based approach is effective and efficient in that it can accurately predict the profitability of optimizations with low overhead. By predicting the profitability using models, we can selectively apply optimizations. The model-based approach does not require tuning of parameters used in heuristic approaches and works well across different code contexts and optimizations.