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Communication-optimal parallel and sequential eigenvalue and singular value algorithms

By James Demmel, Laura Grigori, Mark Hoemmen and Julien Langou


We present parallel and sequential dense QR factorization algorithms that are both optimal (up to polylogarithmic factors) in the amount of communication they perform, and just as stable as Householder QR. Our first algorithm, Tall Skinny QR (TSQR), factors m × n matrices in a one-dimensional (1-D) block cyclic row layout, and is optimized for m ≫ n. Our second algorithm, CAQR (Communication-Avoiding QR), factors general rectangular matrices distributed in a two-dimensional block cyclic layout. It invokes TSQR for each block column factorization. The new algorithms are superior in both theory and practice. We have extended known lower bounds on communication for sequential and parallel matrix multiplication to provide latency lower bounds, and show these bounds apply to the LU and QR decompositions. We not only show that our QR algorithms attain these lower bounds (up to polylogarithmic factors), but that existing LAPACK and ScaLAPACK algorithm

Year: 2011
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