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| Manuel Ujaldon, Emilio L. Zapata, Barbara M. Chapman, Hans P. Zima, "Vienna-Fortran/HPF Extensions for Sparse and Irregular Problems and Their Compilation," IEEE Transactions on Parallel and Distributed Systems, vol. 8, no. 10, pp. 1068-1083, October, 1997. | |||
| BibTex | x | ||
| @article{ 10.1109/71.629489, author = {Manuel Ujaldon and Emilio L. Zapata and Barbara M. Chapman and Hans P. Zima}, title = {Vienna-Fortran/HPF Extensions for Sparse and Irregular Problems and Their Compilation}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {8}, number = {10}, issn = {1045-9219}, year = {1997}, pages = {1068-1083}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.629489}, 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 - Vienna-Fortran/HPF Extensions for Sparse and Irregular Problems and Their Compilation IS - 10 SN - 1045-9219 SP1068 EP1083 EPD - 1068-1083 A1 - Manuel Ujaldon, A1 - Emilio L. Zapata, A1 - Barbara M. Chapman, A1 - Hans P. Zima, PY - 1997 KW - Data-parallel language and compiler KW - sparse computation KW - distributed-memory machines KW - runtime support. VL - 8 JA - IEEE Transactions on Parallel and Distributed Systems ER - | |||
Abstract—Vienna Fortran, High Performance Fortran (HPF), and other data parallel languages have been introduced to allow the programming of massively parallel distributed-memory machines (DMMP) at a relatively high level of abstraction, based on the SPMD paradigm. Their main features include directives to express the distribution of data and computations across the processors of a machine. In this paper, we use Vienna-Fortran as a general framework for dealing with sparse data structures. We describe new methods for the representation and distribution of such data on DMMPs, and propose simple language features that permit the user to characterize a matrix as "sparse" and specify the associated representation. Together with the data distribution for the matrix, this enables the compiler and runtime system to translate sequential sparse code into explicitly parallel message-passing code. We develop new compilation and runtime techniques, which focus on achieving storage economy and reducing communication overhead in the target program. The overall result is a powerful mechanism for dealing efficiently with sparse matrices in data parallel languages and their compilers for DMMPs.
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