GURPR - a method for Global Software pipelining
ACM SIGMICRO Newsletter
Static and Dynamic Evaluation of Data Dependence Analysis Techniques
IEEE Transactions on Parallel and Distributed Systems
Dependence Analysis
An Efficient Data Dependence Analysis for Parallelizing Compilers
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
IEEE Transactions on Parallel and Distributed Systems
Single-Dimension Software Pipelining for Multi-Dimensional Loops
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
A dynamic data dependence analysis approach for software pipelining
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Coping with data dependencies of multi-dimensional array references
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
A dynamic data dependence analysis approach for software pipelining
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Coping with data dependencies of multi-dimensional array references
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
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This paper introduces a new static data dependence constraint, called dependence difference inequality, which can deal with coupled subscripts for multi-dimensional array references. Unlike direction vectors, dependence difference inequalities are related to not only the iteration space for a loop program but also the operation distance between two operations. They are more strict than other methods, and can act as additional constraints to each variable in a linear system on their own or with others. As a result, the solution space for a linear system can be compressed heavily. So long as dependence difference inequalities do not satisfy simultaneously, the loop can be software-pipelined with any initiation interval even if there exists a data dependence between two operations. Meanwhile, by replacing direction vectors with dependence difference inequalities some conservative estimations made by other traditional data dependence analysis approaches can be eliminated.