An approach toward profit-driven optimization

Although optimizations have been applied for a number of years to improve the performance of software, problems with respect to the application of optimizations have not been adequately addressed. For example, in certain circumstances, optimizations may degrade performance. However, there is no efficient way to know when a degradation will occur. In this research, we investigate the profitability of optimizations, which is useful for determining the benefit of applying optimizations. We develop a framework that enables us to predict profitability using analytic models. The profitability of an optimization depends on code context, the particular optimization, and machine resources. Thus, our framework has analytic models for each of these components. As part of the framework, there is also a profitability engine that uses models to predict the profit. In this paper, we target scalar optimizations and, in particular, describe the models for partial redundancy elimination (PRE), loop invariant code motion (LICM), and value numbering (VN). We implemented the framework for predicting the profitability of these optimizations. Based on the predictions, we can selectively apply profitable optimizations. We compared the profit-driven approach with an approach that uses a heuristic in deciding when optimizations should be applied. Our experiments demonstrate that the profitability of scalar optimizations can be accurately predicted by using models. That is, without actually applying a scalar optimization, we can determine if an optimization is beneficial and should be applied.