Direct parallelization of call statements
SIGPLAN '86 Proceedings of the 1986 SIGPLAN symposium on Compiler construction
A practical algorithm for exact array dependence analysis
Communications of the ACM
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
A Guidebook to FORTRAN on Supercomputers
A Guidebook to FORTRAN on Supercomputers
The range test: a dependence test for symbolic, non-linear expressions
Proceedings of the 1994 ACM/IEEE conference on Supercomputing
An Empirical Study of Fortran Programs for Parallelizing Compilers
IEEE Transactions on Parallel and Distributed Systems
The I Test: An Improved Dependence Test for Automatic Parallelization and Vectorization
IEEE Transactions on Parallel and Distributed Systems
The Power Test for Data Dependence
IEEE Transactions on Parallel and Distributed Systems
Performance Analysis of Parallelizing Compilers on the Perfect Benchmarks Programs
IEEE Transactions on Parallel and Distributed Systems
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The I test is an efficient and precise data dependence method to ascertain whether integer solutions exist for one-dimensional arrays with constant bounds. For one-dimensional arrays with variable limits, the I test assumes that there may exist integer solutions. In this paper, we propose the I+ test|an extended version of the I test. The I+ test can be applied towards determining whether integer solutions exist for onedimensional arrays with either variable or constant limits, improving the applicable range of the I test. Experiments with benchmark cited from EISPACK, LINPACK, Parallel loops, Livermore loops and Vector loops showed that among 1189 pairs of one-dimensional arrays tested, 183 had their data dependence analysis amended by the I+ test. That is, the I+ test increases the success rate of the I test by approximately 15:4 percent. Comparing with the Power test and the Omega test, the I+ test has higher accuracy than the Power test and shares the same accuracy with the Omega test when determining integer solutions for these 1189 pairs of one-dimensional arrays, but has much better efficiency over them.