1. Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra
- Author
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Raphael Lafargue, Gustave Pariente, Laurent Daudet, Igor Carron, Kilian Müller, Ruben Ohana, Daniel Hesslow, Alessandro Cappelli, and Iacopo Poli
- Subjects
FOS: Computer and information sciences ,Numerical linear algebra ,Computer Science - Machine Learning ,Computer science ,business.industry ,Computation ,Machine Learning (stat.ML) ,Opus ,computer.software_genre ,Computational science ,Machine Learning (cs.LG) ,Extreme scale ,Statistics - Machine Learning ,Linear algebra ,Photonics ,business ,computer - Abstract
Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods, widely used in High Performance Computing (HPC). RandNLA provides approximate solutions to linear algebra functions applied to large signals, at reduced computational costs. However, the randomization step for dimensionality reduction may itself become the computational bottleneck on traditional hardware. Leveraging near constant-time linear random projections delivered by LightOn Optical Processing Units we show that randomization can be significantly accelerated, at negligible precision loss, in a wide range of important RandNLA algorithms, such as RandSVD or trace estimators., Comment: Add "This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 860830"
- Published
- 2021
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