-
Per-Gunnar Martinsson, Dept. of Applied Mathematics, University of Colorado at Boulder, 526 UCB, Boulder, CO 80309-0526, USA.
-
Gregorio Quintana-Orti, Depto. de Ingenieria y Ciencia de Computadores, Universitat Jaume I, 12.071 Castellon, Spain.
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Nathan Heavner, Dept. of Applied Mathematics, University of Colorado at Boulder, 526 UCB, Boulder, CO 80309-0526, USA.
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Robert van de Geijn, Dept. of Computer Science and Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX, USA.
Please send correspondence about the code to Gregorio Quintana-Ortí: gquintan@icc.uji.es
Correspondence about the paper should be sent to Per-Gunnar J. Martinsson: Per-gunnar.Martinsson@colorado.edu
New 3-clause BSD. See file License.txt for more details.
This code is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY EXPRESSED OR IMPLIED.
Householder transformation based QR factorization with column pivoting is an important algorithm for, for example, determining an approximate basis for the column space of a matrix. It is, unfortunately, notoriously difficult to implement for high performance.
Recently, techniques that use randomized sampling have been developed that do achieve high performance by casting most computation in terms of matrix-matrix multiplication.
For example, we describe such an algorithm in our recent paper:
- P.-G. Martinsson, G. Quintana-Orti, N. Heavner, R. van de Geijn. "Householder QR Factorization: Adding Randomization for Column Pivoting. FLAME Working Note #78" http://arxiv.org/abs/1512.02671
This directory contains an implementation that we call Householder QR factorization with Randomization for Pivoting (HQRRP), based on the insights in that paper.
The new code outperforms LAPACK's core routine DGEQP3 both in unicore and multicore architectures for medium and large matrix sizes, often by large factor. The new implementation comes with an interface that is plug compatible with DGEQP3.
The new code can be downloaded from https://github.com/flame/hqrrp/.
The algorithm was originally implemented using the FLAME/C API with a variation of the compact WY transform we call the UT transform. In addition, we also provide an implementation that instead uses the original compact WY transform so that many routines from LAPACK can be employed in a seamless fashion.
This implementation as well as the original implementation based on the UT transform will eventually be included in the libflame library: https://github.com/flame/libflame/
We will appreciate feedback from the community on the use of this code.
We ask those who benefit from this work to cite the following articles:
@article{doi:10.1137/16M1081270,
author = {Martinsson, P. and Quintana Ort\'{\i}, G.
and Heavner, N. and van de Geijn, R.},
title = {Householder QR Factorization
With Randomization for Column Pivoting (HQRRP)},
journal = {SIAM Journal on Scientific Computing},
volume = {39},
number = {2},
pages = {C96-C115},
year = {2017},
doi = {10.1137/16M1081270},
URL = {https://doi.org/10.1137/16M1081270},
eprint = {https://doi.org/10.1137/16M1081270}
}
@ARTICLE{martinsson2015blocked,
title = {Blocked rank-revealing QR factorizations: How randomized
sampling can be used to avoid single-vector pivoting},
author = {Martinsson, Per-Gunnar},
journal = {arXiv preprint arXiv:1505.08115},
year = {2015},
month = {may}
}
@ARTICLE{2015arXiv151202671M,
author = {{Martinsson}, P.-G. and {Quintana-Ort\’{\i}}, G.
and {Heavner}, N. and {van de Geijn}, R.},
title = "{Householder {QR} Factorization: Adding Randomization for
Column Pivoting. {FLAME} {W}orking {N}ote \#78}",
journal = {ArXiv e-prints},
archivePrefix = "arXiv",
eprint = {1512.02671},
primaryClass = "math.NA",
keywords = {Mathematics - Numerical Analysis, Computer Science -
Numerical Analysis},
year = 2015,
month = dec,
adsurl = {http://adsabs.harvard.edu/abs/2015arXiv151202671M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
We offer two variants of the code:
-
LAPACK-compatible pure C code: It uses compact WY transformations. The sources are stored in the
lapack_compatible_sourcesdirectory. -
LAPACK-like libflame code: It uses compact UT transformations. This code resembles the algorithm in the paper. The sources are stored in the
libflame_sourcesdirectory.
The new code contains the following two main routines:
void dgeqp4( int * m, int * n, double * A, int * lda, int * jpvt, double * tau,
double * work, int * lwork, int * info );
//
// This routine is plug compatible with LAPACK's routine dgeqp3.
// It computes the new HQRRP while keeping the same header as LAPACK's dgeqp3.
// It uses dgeqpf or dgeqp3 for small matrices. The thresholds are defined in
// constants THRESHOLD_FOR_DGEQPF and THRESHOLD_FOR_DGEQP3.
// This routine calls the next one with block size 64 and oversampling 10.
//
int NoFLA_HQRRP_WY_blk_var4( int m_A, int n_A, double * buff_A, int ldim_A,
int * buff_jpvt, double * buff_tau,
int nb_alg, int pp, int panel_pivoting );
//
// This routine is not plug compatible with LAPACK's routine dgeqp3.
// It computes the new HQRRP and allows the user to fine tune more arguments,
// such as the block size, oversampling, etc.
//
These two routines are stored in the NoFLA_HQRRP_WY_blk_var4.c file.
The simple_test.c file in this directory
contains a main program to test routine dgeqp4.
The new code contains the following main routine:
int FLA_HQRRP_UT_blk_var2( FLA_Obj A, FLA_Obj p, FLA_Obj s,
int nb_alg, int pp, int panel_pivoting );
//
// This routine is not plug compatible with LAPACK's routine dgeqp3.
// It computes the new HQRRP and allows the user to fine tune more arguments,
// such as the block size, oversampling, etc.
//
This routine is stored in the FLA_HQRRP_UT_blk_var2.c file.
The simple_test.c file in this directory contains a main program to test it.