An Empirical Study of the I Test for Exact Data Dependence

Abstract

Parallelizing Compilers rely upon subscript analysis to detect data dependences between pairs of array references inside loop nests. The most widely used approximate subscript analysis tests are the GCD test and the Banerjee test. In an earlier work we proposed the I test, an improved subscript analysis test. The I test extends the accuracy of a combination of the GCD test and the Banerjee test. It is also able to provide exact data dependence information at no additional computation cost. In the present work we perform an empirical study on the Perfect Club benchmarks to demonstrate the effectiveness and practical importance of the I Test. We compare its performance with that of the GCD test and the Banerjee test. We show that the I test is always an exact test in practice.

A general data dependence analysis for parallelizing compilers

“…The I test [7] is a polynomial time test. Generally speaking, the I test is a linear time exact test in most cases for single dimensional array references, but cannot precisely handle multidimensional array references involving coupled subscripts and rely on information known at compile time [8]. The DVI test [9] checks the existence of integer solutions and takes loop constant bounds and direction vector constraints into account for one-dimensional arrays.…”