Using Performance Bounds to Guide Pre-scheduling Code Optimizations

We advocate using performance bounds to guide code optimizations. Accurate performance bounds establish an efficient way to evaluate benefits as well as overheads of code transformations without actually performing instruction scheduling. In this paper, we introduce a novel bound-guided approach to systematically regulate code size related instruction level parallelism (ILP) optimizations including tail duplication, loop unrolling and if-conversion. Our approach is based on the notion of code size efficiency, which is defined as the ratio of ILP improvement over code size increase. With such a notion, we can 1) develop a general approach to selectively perform optimizations to maximize the ILP improvement while minimizing the cost in code size; and 2) define the optimal tradeoff between ILP improvement and code size overhead and develop a simple heuristic to achieve this optimal tradeoff. Experimental results using SPEC CINT 2000 benchmarks show that the performance improves significantly with very little code size increase using our systematic way to regulate code transformations and the simple heuristic is effective in achieving the optimal tradeoff.