Title :
An Empirical Study of the I Test for Exact Data Dependence
Author :
Psarris, Kleanthis ; Pande, Santosh
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.
Conference_Titel :
Parallel Processing, 1994. ICPP 1994 Volume 3. International Conference on
Conference_Location :
North Carolina, USA
Print_ISBN :
0-8493-2493-9
DOI :
10.1109/ICPP.1994.55