Probabilistic data flow system with two-edge profiling
Eduard Mehofer, Bernhard J. Scholz · 2000
Traditionally optimization is done statistically independent of actual execution environments. For generating highly optimized code, however, runtime information can be used to adapt a program to different environments. In probabilistic data flow systems runtime information on representative input data is exploited to compute the probability with what data flow facts may hold. Probabilistic data flow analysis can guide sophisticated optimizing transformations resulting in better performance. In comparison classical data flow analysis does not take runtime information into account. All paths are equally weighted irrespectively whether they are never, heavily, or rarely executed.