DagRep.12.3.1.pdf
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Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101)
Authors and all authors of the abstracts in this report
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Part of:
Issue:
Dagstuhl Reports, Volume 12, Issue 3
Volume: Dagstuhl Reports, Volume 12
Journal: Dagstuhl Reports (DagRep) - License:
Creative Commons Attribution 4.0 International license
- Publication Date: 2022-11-14
Cite AsGet BibTex
Paolo Bientinesi, David Ham, Furong Huang, Paul H. J. Kelly, P. (Saday) Sadayappan, and Edward Stow. Tensor Computations: Applications and Optimization (Dagstuhl Seminar 22101). In Dagstuhl Reports, Volume 12, Issue 3, pp. 1-14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022)
https://doi.org/10.4230/DagRep.12.3.1
https://doi.org/10.4230/DagRep.12.3.1
Abstract
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.
Subject Classification
ACM Subject Classification
- Computing methodologies → Linear algebra algorithms
- Applied computing → Physical sciences and engineering
- Mathematics of computing → Mathematical software performance
- Mathematics of computing → Computations on matrices
Keywords
- Tensor
- Optimisation
- Linear Algebra
- Compilers
- Benchmark
- Domain Specific Language
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