Abstract
We describe, and give experience with, a new method of intraprocedural data flow analysis on reducible flow-graphs[9]. The method is advantageous in imbedded applications where the added value of improved performance justifies substantial optimization effort, but extremely powerful data flow analysis is required due to the code profile. The method is unusual in that (1) various kinds of forward data flow analysis are done simultaneously, so that benefit is derived from informative interactions (e.g. between constant propagation[7] and alias analysis[8]) and (2) we abandon insistence on reaching a least fixed point, allowing us to handle extremely broad classes of information (e.g., inequality of array indices, which implies non-aliasing in array references). We argue that the gain from using a very rich framework more than offsets the loss due to non-minimal fixed points, and justify this with a 'thought experiment' and practical results.
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