The conjugate gradient (CG) method is a popular Krylov space method for solving systems of linear equations of the form Ax = b, where A is a symmetric positive-definite matrix. This method can be apphed regardless of whether A is dense or sparse, tn this paper, we show how restructuring compiler technology can be appfied to transform a sequential, dense matrix CG program into a parallel, sparse matrix CG program. On the IBM SP-2, the performance of our compiled code is comparable to that of handwritten code from the PETSc library at Argonne.
CITATION STYLE
Kotlyar, V., Pingali, K., & Stodghill, P. (1996). Automatic parallelization of the conjugate gradient algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1033, pp. 480–499). Springer Verlag. https://doi.org/10.1007/bfb0014219
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