Sensitivity theorems in integer linear programming
Mathematical Programming: Series A and B
Interprocedural dependence analysis and parallelization
SIGPLAN '86 Proceedings of the 1986 SIGPLAN symposium on Compiler construction
Theory of linear and integer programming
Theory of linear and integer programming
Automatic translation of FORTRAN programs to vector form
ACM Transactions on Programming Languages and Systems (TOPLAS)
A Fast Algorithm for the Two-Variable Integer Programming Problem
Journal of the ACM (JACM)
SIGPLAN '84 Proceedings of the 1984 SIGPLAN symposium on Compiler construction
Optimizing Supercompilers for Supercomputers
Optimizing Supercompilers for Supercomputers
Dependence Analysis for Supercomputing
Dependence Analysis for Supercomputing
An Efficient Data Dependence Analysis for Parallelizing Compilers
IEEE Transactions on Parallel and Distributed Systems
An Empirical Study of Fortran Programs for Parallelizing Compilers
IEEE Transactions on Parallel and Distributed Systems
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
The Omega test: a fast and practical integer programming algorithm for dependence analysis
Proceedings of the 1991 ACM/IEEE conference on Supercomputing
A practical algorithm for exact array dependence analysis
Communications of the ACM
Static analysis of upper and lower bounds on dependences and parallelism
ACM Transactions on Programming Languages and Systems (TOPLAS)
An Interleaving Transformation for Parallelizing Reductions for Distributed-Memory Parallel Machines
The Journal of Supercomputing
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The dependence analysis and testing problem has been well studied in parallelizing compilers. All of the existing dependence decision algorithms, however, are conservative in considering data dependence between statements occurring in conditional branches. We present a new dependence test which can detect disjoint regions of an iteration space, and therefore reports accurate dependencies for a class of conditional statements.