Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2832080.2832083acmconferencesArticle/Chapter ViewAbstractPublication PagesscConference Proceedingsconference-collections
research-article

A scalable randomized least squares solver for dense overdetermined systems

Published: 15 November 2015 Publication History

Abstract

We present a fast randomized least-squares solver for distributed-memory platforms. Our solver is based on the Blendenpik algorithm, but employs a batchwise randomized unitary transformation scheme. The batchwise transformation enables our algorithm to scale the distributed memory vanilla implementation of Blendenpik by up to ×3 and provides up to ×7.5 speedup over a state-of-the-art scalable least-squares solver based on the classic QR based algorithm. Experimental evaluations on terabyte scale matrices demonstrate excellent speedups on up to 16384 cores on a Blue Gene/Q supercomputer.

References

[1]
H. Avron, P. Maymounkov, and S. Toledo. Blendenpik: Supercharging lapack's least-squares solver. SIAM J. Scientific Computing, 32(3):1217--1236, 2010.
[2]
K. L. Clarkson and D. P. Woodruff. Low rank approximation and regression in input sparsity time. CoRR, abs/1207.6365, 2012.
[3]
L. Dagum and R. Menon. OpenMP: An industry-standard API for shared-memory programming. IEEE Comput. Sci. Eng., 5(1):46--55, Jan. 1998.
[4]
T. A. Davis and Y. Hu. The university of florida sparse matrix collection. ACM Trans. Math. Softw., 38(1):1:1--1:25, Dec. 2011.
[5]
J. Demmel, L. Grigori, M. Hoemmen, and J. Langou. Communication-optimal parallel and sequential QR and LU factorizations. ArXiv e-prints, Aug. 2008.
[6]
J. Demmel and K. Yelick. Communication avoiding (CA) and other innovative algorithms. The Berkeley Par Lab: Progress in the Parallel Computing Landscape, pages 243--250.
[7]
P. Drineas, M. W. Mahoney, S. Muthukrishnan, and T. Sarlós. Faster least squares approximation. Numer. Math., 117(2):219--249, Feb. 2011.
[8]
M. P. Forum. MPI: A message-passing interface standard. Technical report, Knoxville, TN, USA, 1994.
[9]
M. Frigo and S. G. Johnson. FFTW: An adaptive software architecture for the FFT. In Proceedings of the International Conference on Acoustics, Speech, and Signal Processing, volume 3, pages 1381--1384, Seattle, Washington, 1998.
[10]
K. A. Gallivan, R. J. Plemmons, and A. H. Sameh. Parallel Algorithms for Dense Linear Algebra Computations. SIAM Review, 32(1):54--135, 1990.
[11]
J. R. Hammond, A. Schäfer, and R. Latham. To INT_MAX... and beyond!: Exploring large-count support in MPI. In Proceedings of the 2014 Workshop on Exascale MPI, ExaMPI '14, pages 1--8, Piscataway, NJ, USA, 2014. IEEE Press.
[12]
C. Lattner and V. Adve. LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation. In Proceedings of the 2004 International Symposium on Code Generation and Optimization (CGO'04), Palo Alto, California, Mar 2004.
[13]
X. Meng, M. A. Saunders, and M. W. Mahoney. LSRN: A parallel iterative solver for strongly over- or under-determined systems. CoRR, abs/1109.5981, 2011.
[14]
C. C. Paige and M. A. Saunders. LSQR: An algorithm for sparse linear equations and sparse least squares. ACM Trans. Math. Softw., 8(1):43--71, Mar. 1982.
[15]
J. Poulson, B. Marker, R. A. van de Geijn, J. R. Hammond, and N. A. Romero. Elemental: A new framework for distributed memory dense matrix computations. ACM Trans. Math. Softw., 39(2):13:1--13:24, Feb. 2013.
[16]
V. Rokhlin and M. Tygert. A fast randomized algorithm for overdetermined linear least-squares regression. Proc. Natl. Acad. Sci. USA, 105(36):13212--13217, 2008.
[17]
J. Yang, X. Meng, and M. W. Mahoney. Implementing randomized matrix algorithms in parallel and distributed environments. CoRR, abs/1502.03032, 2015.

Cited By

View all
  • (2022)Territorial design optimization for business sales planJournal of Computational and Applied Mathematics10.1016/j.cam.2018.02.010340:C(501-507)Online publication date: 13-Apr-2022
  • (2016)Randomized sketching for large-scale sparse ridge regression problemsProceedings of the 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems10.5555/3019094.3019103(65-72)Online publication date: 13-Nov-2016
  • (2016)Randomized Sketching for Large-Scale Sparse Ridge Regression Problems2016 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)10.1109/ScalA.2016.013(65-72)Online publication date: Nov-2016

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
ScalA '15: Proceedings of the 6th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems
November 2015
53 pages
ISBN:9781450340113
DOI:10.1145/2832080
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 15 November 2015

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. dense least squares regression
  2. high-performance computing
  3. randomized numerical linear algebra

Qualifiers

  • Research-article

Conference

SC15
Sponsor:

Acceptance Rates

ScalA '15 Paper Acceptance Rate 6 of 10 submissions, 60%;
Overall Acceptance Rate 12 of 20 submissions, 60%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)4
  • Downloads (Last 6 weeks)1
Reflects downloads up to 13 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2022)Territorial design optimization for business sales planJournal of Computational and Applied Mathematics10.1016/j.cam.2018.02.010340:C(501-507)Online publication date: 13-Apr-2022
  • (2016)Randomized sketching for large-scale sparse ridge regression problemsProceedings of the 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems10.5555/3019094.3019103(65-72)Online publication date: 13-Nov-2016
  • (2016)Randomized Sketching for Large-Scale Sparse Ridge Regression Problems2016 7th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA)10.1109/ScalA.2016.013(65-72)Online publication date: Nov-2016

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media