This year’s group photo was taken on March 5, 2020 after the NLA group meeting. Most group members are in the photo; those missing include Jack Dongarra, Stefan Güttel, Ramaseshan Kannan and Marcus Webb.
The banner on this website has also been updated with the new group photo. A high resolution version of the photo is available here.
By row from the back: Craig Lucas, Nick Higham, Xinye Chen, Steven Elsworth, Xiaobo (Bob) Liu, Michael Connolly, Mantas Mikaitis, Len Freeman, Massimiliano Fasi, Pierre Blanchard, Sven Hammarling, Asad Raza Aitor Mehasi Mehasi, Stephanie Lai, Gian Maria Negri Porzio, Thomas McSweeney Mawussi Zounon, Françoise Tisseur, Srikara Pranesh, Yuqing (Mila) Zhang, Eleni Vlachopoulou.
Several members of the group attended the SIAM Conference on Parallel Processing for Scientific Computing held in Seattle on February 12-15, 2020.
The presentations given are as follows:
Nick Higham and Srikara Pranesh also organised a two part mini-symposium (Advances in Algorithms Exploiting Low Precision Floating-Point Arithmetic, MS10 and MS21) at the conference.
Max Fasi, Mantas Mikatis, Mawussi Zounon, Sri Pranesh, Theo Mary at the SIAM Conference on Parallel Processing for Scientific Computing, Seattle, Washington, February 12-15, 2020.
by Sven Hammarling, Nick Higham, and Françoise Tisseur
July 18, 2020 is the 70th birthday of Professor Jack Dongarra, who holds appointments at the University of Tennessee, Oak Ridge National Laboratory, and the University of Manchester.
Jack has made seminal contributions to algorithms for numerical linear algebra and the design and development of high performance mathematical software for machines ranging from workstations to the largest parallel computers. His recent honours include election as a Foreign Member of the Royal Society and receipt of the
SIAM/ACM Prize in Computational Science and Engineering (2019)and the IEEE Computer Society Computer Pioneer Award (2020).
To celebrate Jack’s birthday we are organizing a conference New Directions in Numerical Linear Algebra and High Performance Computing: Celebrating the 70th Birthday of Jack Dongarra at The University of Manchester, July 17, 2020. Registration is now open and we welcome submission of posters.
Professors Jack Dongarra and Nick Higham, together with Dr Laura Grigori (Inria Paris), have edited the issue Numerical Algorithms for High-Performance Computational Science of the journal Philosophical Transaction of The Royal Society A. The issue is now available online.
The issue contains papers from a Discussion meeting of the same title organized at the Royal Society in April 2019. A report on that meeting, along with photos from it, is available here. The content of the issue, with links to the papers, is as follows.
Table of Contents
Numerical algorithms for high-performance computational science by Jack Dongarra, Laura Grigori and Nicholas J. Higham.
The future of computing beyond Moore’s Law by John Shalf.
Hierarchical algorithms on hierarchical architectures by D. E. Keyes , H. Ltaief and G. Turkiyyah.
Stochastic rounding and reduced-precision fixed-point arithmetic for solving neural ordinary differential equations by Michael Hopkins, Mantas Mikaitis, Dave R. Lester and Steve Furber.
Preparing sparse solvers for exascale computing by Hartwig Anzt, Erik Boman, Rob Falgout et al.
On the cost of iterative computations by Erin Carson and Zdeněk Strakoš.
Rethinking arithmetic for deep neural networks by G. A. Constantinides.
Machine learning and big scientific data by Tony Hey , Keith Butler, Sam Jackson and Jeyarajan Thiyagalingam.
The physics of numerical analysis: a climate modelling case study by T. N. Palmer.
Exascale applications: skin in the game by Francis Alexander, Ann Almgren, John Bell et al.
Big telescope, big data: towards exascale with the Square Kilometre Array by A. M. M. Scaife.
Optimal memory-aware backpropagation of deep join networks by Olivier Beaumont, Julien Herrmann, Guillaume Pallez (Aupy) and Alena Shilova.
High-performance sampling of generic determinantal point processes by Jack Poulson.
A survey of algorithms for transforming molecular dynamics data into metadata for in situ analytics based on machine learning methods by Michela Taufer , Trilce Estrada and Travis Johnston.
The parallelism motifs of genomic data analysis by Katherine Yelick , Aydın Buluç, Muaaz Awan et al.
Professor Jack Dongarra, a member of the Manchester Numerical Linear Algebra Group who also holds appointments at the University of Tennessee and Oak Ridge National Laboratory, has been named as recipient of the IEEE Computer Society’s 2020 Computer Pioneer Award.
The award is given for significant contributions to early concepts and developments in the electronic computer field that have clearly advanced the state-of-the-art in computing. Dongarra is being recognized “for leadership in the area of high-performance mathematical software.”
Dongarra will receive his award at the Computer Society’s annual awards dinner and presentation to be held on Wednesday 27 May 2020 at the Hilton McLean Tysons Corner during the IEEE Computer Society Board of Governors meeting. The award consists of a silver medal and an invitation to speak at the award presentation.
This article is based on an IEEE news release. Further information about the award is available here.
Group photo at SIAM UKIE 2020
Several members of the group attended the SIAM UKIE Section Meeting held at the University of Edinburgh on Friday January 10, 2020. Françoise Tisseur, President of the Section and one of the co-organizers, chaired the morning session.
PhD students Michael Connolly, Xiaobo (Bob) Liu, Gian Maria Negri Porzio, and Research Associate Srikara Pranesh presented posters.
Congratulations to Sri and Michael, who won first and second best poster prizes, respectively:
a cheque for £75 to both and also a copy of the book /50 Visions of Mathematics/ to Sri.
Xiaobo (Bob) Liu
Gian Maria Negri Prozio
Photo credit to Gian Maria Negri Porzio
The Numerical Linear Algebra Group had a busy year in 2019. This post summarizes what we got up to. Publications are not included here, but many of them can be found on MIMS EPrints under the category Numerical Analysis; see also these news stories about our publications.
Marcus Webb joined the group in September 2019 as Lecturer in Applied Mathematics.
Some of the group at the 2019 SIAM Conference on Computational Science and Engineering.
We make our research codes available as open source, principally on GitHub; see the repositories of Connolly, Fasi, Higham, Liu, Pranesh, Zounon.
We also put MATLAB software on MATLAB Central File Exchange and on our own web sites, e.g., the Rational Krylov Toolbox (RKToolbox).
Massimiliano Fasi successfully defended his PhD Computing Matrix Functions in Arbitrary Precision Arithmetic in May 2019.
Postdoctoral Research Associates (PDRAs)
Massimiliano Fasi joined us in April 2019 to work with Nick Higham on algorithms for high-performance numerical linear algebra. He was a Visiting Fellow at the University of Pisa from November 2019 to January 2020.
Mantas Mikaitis joined us in October 2019 on a EPSRC Doctoral Prize Fellowship, having just completed his PhD in the School of Computer Science.
Pierre Blanchard left the group in May 2019 and is now a Numerical Software Engineer at Arm.
Maksims Abalenkovs and Theo Mary left the group in September 2019. Maksims is now a Research Software Engineer with the Science and Technology Facilities Council and Theo is a CNRS researcher at LIP6 in Paris.
Stefan Güttel, Nick Higham and Françoise Tisseur were awarded a new 30-month KTP project with Arup. See this news story.
Presentations at Conference and Workshops
Model Order Reduction Summer School 2019, Academisch Genootschap, Eindhoven, The Netherlands, September 23-27, 2019: Higham.
Computational Science Engineering, Data Science and Artificial Intelligence by Total R&D (MATHIAS), Serris, France, October 14-17, 2019: Riccietti.
Conference and Workshop Organization
ANLA19 group photo
Vanni Noferini listening to a question from Cleve Moler at ANLA19.
Rob Corless from University of Western Ontario visited the group in November 2019.
Recognition and Service
- Françoise Tisseur delivered the Olga Taussky-Todd Lecture and Nick Higham was an invited speaker at the International Congress on Industrial and Applied Mathematics (ICIAM) in Valencia, Spain, July 2019.
- Jack Dongarra received the SIAM/ACM Prize in Computational Science and Engineering at the SIAM Conference on Computational Science and Engineering (CSE19)
- Jack Dongarra was elected as a Foreign Member of the Royal Society.
- Françoise Tisseur was elected as SIAM UKIE Section President, 2019-20.
- Steven Elsworth obtained a SIAM Student Travel Award to attend the 2019 SIAM Conference on Computational Science and Engineering (CSE19) in Spokane, Washington.
- Nick Higham received the London Mathematics Society’s Naylor Prize and Lectureship.
- Nick Higham delivered the Feng Kang Distinguished Lecture at the Institute of Computational Mathematics and Scientific/Engineering Computing, Chinese Academy of Sciences, Beijing, in April 2019, and the LAA Lecture (formerly the Hans Schneider Lecture) in the Department of Mathematics, University of Wisconsin, Madison, December 2019.
Other Notable Tweets
Dr Stefan Güttel, Professor Nick Higham, and Professor Françoise Tisseur have been awarded a new 30-month project with Arup, a multidisciplinary engineering firm operating in all areas of built environment.
This Knowledge Transfer Partnership (KTP), funded by Arup and Innovate UK, aims to embed new matrix eigenvalue solvers into Arup’s next generation software for structural engineering simulation.
The Numerical Linear Algebra (NLA) group team will be working with Dr Stephen Hendry and Dr Ramaseshan Kannan of Arup, along with a KTP Associate, for which the position is advertised here.
The project builds on a long history of collaboration between Arup and the NLA Group, which has previously led to the development of “model stability analysis” in Arup’s flagship structural engineering simulation package, Oasys GSA (see the paper What is Your Structural Model Not Telling You?).
The November 2019 edition of SIAM News contains an article by Research Associate Srikara Pranesh about the growing importance of low precision floating-point arithmetic. Sri describes the opportunities provided by recent hardware and explains how new algorithms are being derived to exploit low precision arithmetic. To read the article click the image below.
Also see Sri’s recent blog post Simulating Low Precision Floating-Point Arithmetics.
Photo provided by @ICIAMnews
Professor Nick Higham delivered an invited talk at the International Congress on Industrial and Applied Mathematics (ICIAM) on July 19, 2019, in Valencia, Spain. His talk “Exploiting Low Precision Arithmetic in the Solution of Linear Systems” reported work by Higham and his colleagues over the last three years to use the fast half precision arithmetic available on accelerators such as GPUs to speed up the solution of linear systems. A video of the talk is available here and the slides are available here.
The talk was summarized in the Friday 19th July edition of the ICIAM2019 newsletter:
“Nick Higham (University of Manchester) advocated in his invited lecture using arithmetics of different precision at different stages of computations in order to design algorithms that are faster, require less communications and consume less energy. The main motivation is that last-generation GPUs may be up to eight times faster when they perform arithmetic operations in half precision than when they do in single precision.
The general philosophy is doing the bulk of the computations in half precision, and then perform some kind of clean-up refinement of the solution in higher precisions. As an archetypal example to illustrate this line of thought Higham chose how to accelerate the solution of linear systems via Gaussian elimination. He showed that if the LU factorization is computed in half precision, followed by iterative refinement using a mix of half, double and quadruple precision, the solution can be sped up significantly without accuracy loss. This works in principle for systems with moderate condition number, but even ill-conditioned systems can be dealt with by performing the iterative refinement via GMRES on a suitably pre-conditioned system.”
Photo credit Françoise Tisseur