Numerical Algorithms for High-Performance Computational Science Issue of Phil Trans R Soc A
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.
Numerical Linear Algebra Group 