A general data dependence analysis for parallelizing compilers

Authors:
Jing Zhou;Guosun Zeng
Affiliations:
Department of Statistics, University of California at Berkeley, Berkeley, USA 94720 and Department of Computer Science and Technology, Tongji University, Shanghai, China 201804;Department of Computer Science and Technology, Tongji University, Shanghai, China 201804
Venue:
The Journal of Supercomputing
Year:
2008

Citing 15
Cited 1

Efficient and exact data dependence analysis

PLDI '91 Proceedings of the ACM SIGPLAN 1991 conference on Programming language design and implementation
A practical algorithm for exact array dependence analysis

Communications of the ACM
Integer Programming for Array Subscript Analysis

IEEE Transactions on Parallel and Distributed Systems
Static and Dynamic Evaluation of Data Dependence Analysis Techniques

IEEE Transactions on Parallel and Distributed Systems
Data dependence analysis on multi-dimensional array references

ICS '89 Proceedings of the 3rd international conference on Supercomputing
Data dependence analysis for array references

Journal of Systems and Software
High Performance Compilers for Parallel Computing

High Performance Compilers for Parallel Computing
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
The Power Test for Data Dependence

IEEE Transactions on Parallel and Distributed Systems
The Direction Vector I Test

IEEE Transactions on Parallel and Distributed Systems
Data dependence analysis techniques for increased accuracy and extracted parallelism

International Journal of Parallel Programming - Special issue II: The 17th annual international conference on supercomputing (ICS'03)
An Empirical Study of the I Test for Exact Data Dependence

ICPP '94 Proceedings of the 1994 International Conference on Parallel Processing - Volume 03
An experimental evaluation of data dependence analysis techniques

IEEE Transactions on Parallel and Distributed Systems
CMP Support for Large and Dependent Speculative Threads

IEEE Transactions on Parallel and Distributed Systems

An approximate method for filtering out data dependencies with a sufficiently large distance between memory references

The Journal of Supercomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Many dependence tests have been proposed for loop parallelization in the case of arrays with linear subscripts, but little work has been done on the arrays with nonlinear subscripts, which sometimes occur in parallel benchmarks and scientific and engineering applications. This paper focuses on array subscripts coupled integer power index variables. We attempt to use the integer interval theory to solve the above difficult dependence test problem. Some "interval solution" rules for polynomial equations have been proposed in this paper. Furthermore, based on the proposed rules, we present a novel approach to loop dependence analysis, which is termed the Polynomial Variable Interval test or PVI test, and also develop a related algorithm. Some case studies show that the PVI test is effective and efficient. Compared to the I test and Omega test, the PVI test makes significant improvement, and is, therefore, a more general scheme of dependence test.