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
An overview of the PTRAN analysis system for multiprocessing
Proceedings of the 1st 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
PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
Eliminating false data dependences using the Omega test
PLDI '92 Proceedings of the ACM SIGPLAN 1992 conference on Programming language design and implementation
A general algorithm for data dependence analysis
ICS '92 Proceedings of the 6th international conference on Supercomputing
On exact data dependence analysis
ICS '92 Proceedings of the 6th international conference on Supercomputing
Going Beyond Integer Programming with the Omega Test to Eliminate False Data Dependences
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
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The Banerjee test is commonly considered to be the more accurate of the two major approximate data dependence tests used in automatic vectorization/parallelization of loops, the other being the GCD test. From its derivation, however, there is no simple explanation of why the Banerjee test should be nearly as accurate as it is given credit for. We present a set of sufficient conditions for the Banerjee test's accuracy, and explain its perceived accuracy in actual practice by proving that under circumstances which occur extremely frequently in actual code, the Banerjee test is, in fact, not approximate, but perfectly accurate.