Optimizing compilers have become an essential component in achieving high levels of performance. Various simple and sophisticated optimizations are implemented at different stages of compilation to yield significant improvements, but little work has been done in characterizing the effectiveness of optimizers, or in understanding where most of this improvement comes from.

In this paper we study the performance impact of optimization in the context of our methodology for CPU performance characterization based on the abstract machine model. The abstract machine model considers all machines to be different implementations of the same high level language machine; in previous research, we have used this model as a basis to analyze machine and benchmark performance. In this paper, we: 1) show that our model can be extended to characterize the performance improvement provided by optimizers and to predict the run time of optimized programs; 2) measure the effectiveness of several optimizing compilers in implementing different optimization techniques; and 3) analyze the optimization opportunities present in the Fortran SPEC benchmarks and other benchmarks.




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