  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.
