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
Exploiting superword level parallelism with multimedia instruction sets
PLDI '00 Proceedings of the ACM SIGPLAN 2000 conference on Programming language design and implementation
Loop Transformations for Restructuring Compilers: The Foundations
Loop Transformations for Restructuring Compilers: The Foundations
Vector pascal reference manual
ACM SIGPLAN Notices
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
An overview of methods for dependence analysis of concurrent programs
ACM SIGPLAN Notices
Compilation Techniques for Multimedia Processors
International Journal of Parallel Programming
A Vectorizing Compiler for Multimedia Extensions
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
Vectorization for SIMD architectures with alignment constraints
Proceedings of the ACM SIGPLAN 2004 conference on Programming language design and implementation
An extended ANSI C for processors with a multimedia extension
International Journal of Parallel Programming
SWARP: a retargetable preprocessor for multimedia instructions: Research Articles
Concurrency and Computation: Practice & Experience - Compilers for Parallel Computers
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There are a number of data dependence tests that have been proposed in the literature. 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 the multimedia extensions. 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 this paper we present an accurate and simple method that can filter out data dependences with a sufficiently large distance between memory references for linear array references within a nested loop. The presented method 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.