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EECS Publication

Two-stage Tridiagonal Reduction for Dense Symmetric Matrices using Tile Algorithms on Multicore Architectures

Piotr Luszczek, Hatem Ltaief, Jack Dongarra

While successful implementations have already been written for one-sided transformations (e.g., QR, LU and Cholesky factorizations) on multi-core architecture, getting high performance for two-sided reductions (e.g., Hessenberg, tridiagonal and bidiagonal reductions) is still an open and difficult research problem due to expensive memorybound operations occurring during the panel factorization. The processor-memory speed gap continues to widen, which has even further exacerbated the problem. This paper focuses on an efficient implementation of the tridiagonal reduction, which is the first algorithmic step toward computing the spectral decomposition of a dense symmetric matrix. The original matrix is translated into a tile layout i.e., a high performance data representation, which substantially enhances data locality. Following a twostage approach, the tile matrix is then transformed into band tridiagonal form using compute intensive kernels. The band form is further reduced to the required tridiagonal form using a left-looking bulge chasing technique to reduce memory traffic and memory contention. A dependence translation layer associated with a dynamic runtime system allows for scheduling and overlapping tasks generated from both stages. The obtained tile tridiagonal reduction significantly outperforms the state-of-theart numerical libraries (10X against multithreaded LAPACK with optimized MKL BLAS and 2.5X against the commercial numerical software Intel MKL) from medium to large matrix sizes.

Published  2011-04-15 04:00:00  as  ut-cs-11-670 (ID:32)


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