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Randomized Numerical Linear Algebra (RandNLA) is a powerful class of methods,widely used in High Performance Computing (HPC). RandNLA provides approximatesolutions to linear algebra functions applied to large signals, at reducedcomputational costs. However, the randomization step for dimensionalityreduction may itself become the computational bottleneck on traditionalhardware. Leveraging near constant-time linear random projections delivered byLightOn Optical Processing Units we show that randomization can besignificantly accelerated, at negligible precision loss, in a wide range ofimportant RandNLA algorithms, such as RandSVD or trace estimators.

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