Rong-Guey Chang
- 1.A. J. C. Bik and Harry A. G. Wijshoff. Automatic data structure selection and transformation for sparse matrix computations, iEEE Transactions on Parallel and Distributed Systems, 7(2):109-126, February 1996. Google Scholar
Digital Library
- 2.R.-G. Chang, C.-W. Chen, T.-R. Chuang, and J. K. Lee. Towards automatic supports of parallel sparse computation in Java with continuous compilation. Concurrency: Practice and Experience, 9(11):1101-1111, November 1997.Google ScholarCross Ref
- 3.W.-M. Ching and A. Katz. An experimental APL compiler for a distributed memory parallel machine. In Proceedings of Supercomputing '9~, pages 59-68. November 1994. Google ScholarDigital Library
- 4.T.-R. Chuang, R.-G. Chang, and J. K. Lee. Sampling and analytical techniques for data distribution of parallel sparse computation. In Eighth SIAM Conference on Parallel Processing for Scientific Computing. 8 pages. March 1997.Google Scholar
- 5.L. De Rose and D. Padua. A MATLAB to Fortran 90 translator and its effectiveness. In Proceedings of the 1996 International Conference on Supercomputing, pages 309-316. May 1996. Google ScholarDigital Library
- 6.J. R. Gilbert, C. Moler, and R. Schreiber. Sparse matrices in MATLAB: Design and implementation. SIAM Journal on Matrix Analysis and Applications, 13(1):333--356, January 1992. Google ScholarDigital Library
- 7.S. Goil and A. Choudhary. High performance O LAP and data mining on parallel computers. In l~th International Parallel Processing Symposium 9th Symposium on Parallel and Distributed Processing. April 1998.Google Scholar
- 8.G.-H. Hwang, J. K. Lee, and D.-C. Ju. An array operation synthesis scheme to optimize Fortran 90 programs. In Proceedings of the Fifth A UM SIGPLAN Symposium on Principles ~ Practice of Parallel Programming, pages 112-122. July 1995. Google ScholarDigital Library
- 9.V. Kotlyar, K. Pingali, and P. Stodghill. Compiling parallel sparse code for user-defined data structures. In Eighth SIAM Conference on Parallel Processing for Scientific Computing. March 1997.Google Scholar
- 10.W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical recipes in Fortran 90: The Art of Parallel Scientific Computing. Cambridge University Press, 1996. Google ScholarDigital Library
- 11.Y. Saad. SPARSKIT: A basic tool kit for sparse computations, VERSION 2. Technical report, Computer Science Department, University of Minnesota, June 1994.Google Scholar
- 12.S. D. Stearns and R. A. David. Signal Processing Algorithms Using Fortran and C. Prentice-Hall, 1993. Google ScholarDigital Library
- 13.M. Ujaldon, E. Zapata, B. M. Chapman, and H. P. Zima. Vienna-Fortran/HPF extensions for sparse and irregular problems and their compilation. IEEE Transactions on Parallel and Distributed Systems, 8(10):1068-1083, October 1997. Google ScholarDigital Library
- 14.J. Wu, R. Das, J. Saltz, H. Berryman, and S. Hiranandani. Distributed memory compiler design for sparse problems. IEEE Transaction on Computers, 44(6):737-753, June 1995. Google ScholarDigital Library
- 15.Y. Zhao, P. M. Deshpande, and J. F. Naughton. An array-bases algorithm for simultaneous multidimensional aggregates. In Proceedings of the 1997 A CM SIGMOD International Conference on Management of Data, pages 159-170. May 1997. Google ScholarDigital Library
Index Terms
- Efficient support of parallel sparse computation for array intrinsic functions of Fortran 90
Recommendations
Parallel Sparse Supports for Array Intrinsic Functions of Fortran 90
Fortran 90 provides a rich set of array intrinsic functions. Each of these array intrinsic functions operates on the elements of multi-dimensional array objects concurrently. They provide a rich source of parallelism and play an increasingly important ...
Support and optimization for parallel sparse programs with array intrinsics of Fortran 90
Fortran 90 provides a rich set of array intrinsic functions that are useful for representing array expressions and data parallel programming. However, the application of these intrinsic functions to sparse data sets in distributed memory environments, ...
Array language support for parallel sparse computation
ICS '01: Proceedings of the 15th international conference on SupercomputingThis paper describes an array-based language-level approach to parallel sparse computation. Our approach is unique due to its separation of sparse index sets from arrays, both syntactically and in the implementation. This design allows users to express ...
Comments