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Pramod G. Joisha, Prithviraj Banerjee, "The Efficient Computation of Ownership Sets in HPF," IEEE Transactions on Parallel and Distributed Systems, vol. 12, no. 8, pp. 769788, August, 2001.  
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@article{ 10.1109/71.946650, author = {Pramod G. Joisha and Prithviraj Banerjee}, title = {The Efficient Computation of Ownership Sets in HPF}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {12}, number = {8}, issn = {10459219}, year = {2001}, pages = {769788}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.946650}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
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TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  The Efficient Computation of Ownership Sets in HPF IS  8 SN  10459219 SP769 EP788 EPD  769788 A1  Pramod G. Joisha, A1  Prithviraj Banerjee, PY  2001 KW  HPF KW  array alignment KW  array distribution KW  ownership set KW  FourierMotzkin Elimination technique KW  parallelizing compiler. VL  12 JA  IEEE Transactions on Parallel and Distributed Systems ER   
Abstract—Ownership sets are fundamental to the partitioning of program computations across processors by the ownercomputes rule. These sets arise due to the mapping of arrays onto processors. In this paper, we focus on how ownership sets can be efficiently determined in the context of the HPF language and show how the structure of these sets can be symbolically characterized in the presence of arbitrary array alignment and array distribution directives. Our starting point is a system of equalities and inequalities due to Ancourt et al. [1] that captures the array mapping problem in HPF. We arrive at a refined system that enables us to efficiently solve for the ownership set using the FourierMotzkin Elimination technique and that requires the course vector as the only auxiliary vector. The formulation makes it possible to enumerate the elements of the ownership set exactly once, a feature that is very beneficial when such sets are applied to handle DO loops qualified by HPF's INDEPENDENT directive. We develop important and general properties pertaining to HPF alignments and distributions and show how they can be used to eliminate redundant communication due to array replication. Polynomialtime schemes that determine whether the ownership set of a
[1] C. Ancourt, F. Coelho, F. Irigoin, and R. Keryell, “A Linear Algebra Framework for Static HPF Code Distribution,” Technical Report A278CRI, Centre de Recherche en Informatique,École Nationale Supérieure des Mines de Paris, 35, rue SaintHonoroé, F77305 Fontainebleau cedex, France, Nov. 1995.
[2] S.P. Amarasinghe and M.S. Lam, “Communication Optimization and Code Generation for Distributed Memory Machines,” Proc. ACM SIGPLAN Programming Language Design and Implementation, pp. 126138, June 1993.
[3] V.S. Adve and J. MellorCrummey, “Using Integer Sets for DataParallel Program Analysis and Optimization,” Proc. SIGPLAN '98 Conf. Programming Language Design and Implementation, June 1998.
[4] V. Balasundaram, “A Mechanism for Keeping Useful Internal Information in Parallel Programming Tools: The Data Access Descriptor,” J. Parallel and Distributed Computing, vol. 9, pp. 154170, 1990.
[5] U. Banerjee, Loop Transformations for Restructuring Compilers, Kluwer Academic Publishers, Boston, Mass., 1997.
[6] S. Chatterjee, J. Gilbert, F. Long, R. Schreiber, and S. Tseng, “Generating Local Adresses and Communication Sets for Data Parallel Programs,” J. Parallel and Distributed Computing, vol. 26,pp. 72–84, 1995.
[7] D. Callahan and K. Kennedy, “Analysis of Interprocedural Side Effects in a Parallel Programming Environment,” Proc. First ACM Int'l Conf. Supercomputing, pp. 138171, May 1987.
[8] S.K.S. Gupta, S.D. Kaushik, S. Mufti, S. Sharma, C.H. Huang, and P. Sadayappan, “On Compiling Array Expressions for Efficient Execution on DistributedMemory Machines,” Proc. 22nd Int'l Conf. Parallel Processing, vol. II (Software), pp. 301305, Aug. 1993.
[9] P.G. Joisha and P. Banerjee, “PARADIGM (Version 2. 0): A New HPF Compilation System,” Proc. 13th Int'l Parallel Processing Symp. and 10th Symp. Parallel and Distributed Processing, pp. 609615, Apr. 1999.
[10] C. Koelbel, D. Loveman, R. Schreiber, G. Steele Jr., and M. Zosel, The High Performance Fortran Handbook. MIT Press, 1994.
[11] F.M. McMahon, “The Livermore FORTRAN Kernels: A Computer Test of the Numerical Performance Range,” Technical Report UCRL55745, Lawrence Livermore National Laboratory, Livermore, Calif., Dec. 1986.
[12] S.P. Midkiff, “Local Iteration Set Computation for BlockCyclic Distributions,” Proc. 24th Int'l Conf. Parallel Processing, vol. II (Software), pp. 7784, Aug. 1995.
[13] E. Su, D.J. Palermo, and P. Banerjee, “Processor Tagged Descriptors: A Data Structure for Compiling for DistributedMemory Multicomputers,” Proc. 1994 Int’l Conf. Parallel Architectures and Compilation Techniques, Elsevier Science B.V., Amsterdam, The Netherlands, 1994, pp. 123132.
[14] A. Thirumalai and J. Ramanujam, “Efficient Computation of Address Sequences in DataParallel Programs Using Closed Forms for Basis Vectors,” J. Parallel and Distributed Computing, vol, 38, no. 2, pp. 188203, Nov. 1996.
[15] M.J. Wolfe, High Performance Compilers for Parallel Computing, Redwood City, Calif.: Addison Wesley, 1996.