QR Decomposition in a Multicore Environment
نویسنده
چکیده
In this study we examine performance benefits of implementing the QR decomposition in a way that takes advantage of multiple processes or threads. This is done by partitioning the matrix into blocks of a certain number of rows, which is called the blocksize. We examine this algorithm on “tall and skinny” matrices, which are matrices that have a very large number of rows, but comparatively fewer columns. These matrices are very important, as one of their most common uses is Linear Regression, which is a tool used in many different fields. We also compare this implementation to one which uses a MapReduce environment to compute the QR decomposition. We find that partitioning the matrix and using multiple processes to compute the QR decomposition in parallel provides for computing the decomposition much faster than computing the QR decomposition immediately on the original matrix.
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