Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101)
This report documents the program and the outcomes of Dagstuhl Seminar 22101 "Tensor Computations: Applications and Optimization". Tensors are higher dimensional analogs of matrices, and represent a key data abstraction for many applications in computational science and data science. Widely used shared infrastructure exists for linear algebra, while, in contrast, for tensor computations, there is no consensus on standard building blocks. This Dagstuhl Seminar aimed to bring together users, and performance optimization specialists, to build such foundations.
We present the abstracts of the 5 tutorials and 14 talks given. The working groups and their outcomes so far are then presented.
Tensor
Optimisation
Linear Algebra
Compilers
Benchmark
Domain Specific Language
Computing methodologies~Linear algebra algorithms
Applied computing~Physical sciences and engineering
Mathematics of computing~Mathematical software performance
Mathematics of computing~Computations on matrices
1-14
Regular Paper
Paolo
Bientinesi
Paolo Bientinesi
University of UmeĆ„, SE
David
Ham
David Ham
Imperial College London, GB
Furong
Huang
Furong Huang
University of Maryland - College Park, US
Paul H. J.
Kelly
Paul H. J. Kelly
Imperial College London, GB
P. (Saday)
Sadayappan
P. (Saday) Sadayappan
University of Utah - Salt Lake City, US
Edward
Stow
Edward Stow
Imperial College London, GB
10.4230/DagRep.12.3.1
Paolo Bientinesi, David Ham, Furong Huang, Paul H. J. Kelly, P. (Saday) Sadayappan, and Edward Stow
Creative Commons Attribution 4.0 International license
https://creativecommons.org/licenses/by/4.0/legalcode