# Photonic co-processors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra

arXiv: https://arxiv.org/abs/2104.14429 · HTML: https://mlsanity.com/paper/2104.14429/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 9 references that are in the map.
- First posted on arXiv 29 Apr 2021.

## Builds on (most cited first, 3 of 3)

- [Random Projections through multiple optical scattering: Approximating kernels at the speed of light](https://mlsanity.com/paper/1510.06664/) (2015)
- [Kernel computations from large-scale random features obtained by Optical Processing Units](https://mlsanity.com/paper/1910.09880/) (2019)
- [Randomized Algorithms for Scientific Computing (RASC)](https://mlsanity.com/paper/2104.11079/) (2021)

## Abstract (arXiv)

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.