Scalability Analysis of CGLS Algorithm for Sparse Least Squares Problems on Massively Distributed Memory Computers
نویسنده
چکیده
In this paper we study the parallelization of CGLS, a basic iterative method for large and sparse least squares problems whose main idea is to organize the computation of conjugate gradient method to normal equations. A performance model of computation and communication phases with isoeeciency concept are used to analyze the qualitative scalability behavior of this method implemented on massively parallel distributed memory computers with two dimensional mesh communication scheme. Two diierent mappings of data to processors, namely simple stripe and cyclic stripe partitionings are compared by putting these communication times into the isoeeciency concept which models scalability aspects. Theoretically, the cyclic stripe partitioning is shown to be asymptotically more scalable.
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