Data dependence and its application to parallel processing
International Journal of Parallel Programming
Supercompilers for parallel and vector computers
Supercompilers for parallel and vector computers
On the accuracy of the Banerjee test
Journal of Parallel and Distributed Computing - Special issue on shared-memory multiprocessors
A practical algorithm for exact array dependence analysis
Communications of the ACM
The Banerjee-Wolfe and GCD tests on exact data dependence information
Journal of Parallel and Distributed Computing
Compilation techniques for multimedia processors
International Journal of Parallel Programming - Special issue on instruction-level parallelism and parallelizing compilation, Part 1
A vectorizing compiler for multimedia extensions
International Journal of Parallel Programming - Special issue on instruction-level parallelism and parallelizing compilation, Part 1
Dependence Analysis
High Performance Compilers for Parallel Computing
High Performance Compilers for Parallel Computing
Automatic intra-register vectorization for the Intel architecture
International Journal of Parallel Programming
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
IEEE Transactions on Parallel and Distributed Systems
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There are a number of data dependence tests that have been proposed in the literature. In each test there is a different trade-off between accuracy and efficiency. The most widely used approximate data dependence tests are the Banerjee inequality and the GCD test. In this paper we consider parallelization for microprocessors with a multimedia extension (the short SIMD execution model). For the short SIMD parallelism extraction it is essential that, if dependency exists, then the distance between memory references is greater than or equal to the number of data processed in the SIMD register. This implies that some loops that could not be vectorized on traditional vector processors can still be parallelized for the short SIMD execution. In all of these tests the parallelization would be prohibited when actually there is no parallelism restriction relating to the short SIMD execution model. In this paper we present a new, fast and accurate data dependence test (called D-test) for array references with linear subscripts, which is used in a vectorizing compiler for microprocessors with a multimedia extension. The presented test is suitable for use in a dependence analyzer that is organized as a series of tests, progressively increasing in accuracy, as a replacement for the GCD or Banerjee tests.