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Z. Li, P.C. Yew, C.Q. Zhu, "An Efficient Data Dependence Analysis for Parallelizing Compilers," IEEE Transactions on Parallel and Distributed Systems, vol. 1, no. 1, pp. 2634, January, 1990.  
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@article{ 10.1109/71.80122, author = {Z. Li and P.C. Yew and C.Q. Zhu}, title = {An Efficient Data Dependence Analysis for Parallelizing Compilers}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {1}, number = {1}, issn = {10459219}, year = {1990}, pages = {2634}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.80122}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  An Efficient Data Dependence Analysis for Parallelizing Compilers IS  1 SN  10459219 SP26 EP34 EPD  2634 A1  Z. Li, A1  P.C. Yew, A1  C.Q. Zhu, PY  1990 KW  Index Termsprogram restructuring; array subscripts; convex set; hyperplanes; loop bounds; linear inequalities; data dependence analysis; parallelizing compilers; lambda test; multidimensional array references; numerical methods; Parafrase; Fortran program parallelization restructurer; FORTRAN; parallel programming; program compilers VL  1 JA  IEEE Transactions on Parallel and Distributed Systems ER   
A novel algorithm, called the lambda test, is presented for an efficient and accurate data dependence analysis of multidimensional array references. It extends the numerical methods to allow all dimensions of array references to be tested simultaneously. Hence, it combines the efficiency and the accuracy of both approaches. This algorithm has been implemented in Parafrase, a Fortran program parallelization restructurer developed at the University of Illinois at UrbanaChampaign. Some experimental results are presented to show its effectiveness.
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