Effectiveness of data dependence analysis

Data dependence testing is the basic step in detecting loop level parallelism in numerical programs. The problem is undecidable in the general case. Therefore, work has been concentrated on a simplified problem, affine memory disambiguation. In this simpler domain, array references and loops bounds are assumed to be linear integer functions of loop variables. Dataflow information is ignored. For this domain, we have shown that in practice the problem can be solved accurately and efficiently.(1) This paper studies empirically the effectiveness of this domain restriction, how many real references are affine and flow insensitive. We use Larus's llpp system(2) to find all the data dependences dynamically. We compare these to the results given by our affine memory disambiguation system. This system is exact for all the cases we see in practice. We show that while the affine approximation is reasonable, memory disambiguation is not a sufficient approximation for data dependence analysis. We propose extensions to improve the analysis.