Interprocedural dependence analysis and parallelization
SIGPLAN '86 Proceedings of the 1986 SIGPLAN symposium on Compiler construction
Automatic translation of FORTRAN programs to vector form
ACM Transactions on Programming Languages and Systems (TOPLAS)
Data dependence and its application to parallel processing
International Journal of Parallel Programming
Compiling issues for supercomputers
Proceedings of the 1988 ACM/IEEE conference on Supercomputing
An overview of the PTRAN analysis system for multiprocessing
Proceedings of the 1st International Conference on Supercomputing
International Journal of High Speed Computing
On the accuracy of the Banerjee test
Journal of Parallel and Distributed Computing - Special issue on shared-memory multiprocessors
A formal study of data dependence analysis for parallelizing compilers
A formal study of data dependence analysis for parallelizing compilers
On the perfect accuracy of an approximate subscript analysis test
ICS '90 Proceedings of the 4th international conference on Supercomputing
The parallel execution of DO loops
Communications of the ACM
Optimizing Supercompilers for Supercomputers
Optimizing Supercompilers for Supercomputers
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
Control and data dependence for program transformations.
Control and data dependence for program transformations.
Speedup of ordinary programs
Dependence analysis for subscripted variables and its application to program transformations
Dependence analysis for subscripted variables and its application to program transformations
Optimizing supercompilers for supercomputers
Optimizing supercompilers for supercomputers
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The GCD test and the Banerjee-Wolfe test are the two tests traditionally used to determine statement data dependence, subject to direction vectors, in automatic vectorization / parallelization of loops. In an earlier study [14] a sufficient condition for the accuracy of the Banerjee-Wolfe test was stated and proved. In the original presentation only the case of general data dependence was considered, i.e., the case of data dependence without direction vector information. In this paper we extend the previous work to the case of data dependence subject to an arbitrary direction vector. We also state and prove a sufficient condition for the accuracy of a combination of the GCD and the Banerjee-Wolfe test. Finally, we demonstrate how these results can be used in actual practice to obtain exact data dependence information.