
Lectures on Randomized Numerical Linear Algebra
This chapter is based on lectures on Randomized Numerical Linear Algebra...
read it

Lecture Notes on Randomized Linear Algebra
These are lecture notes that are based on the lectures from a class I ta...
read it

Randomized Linear Algebra Approaches to Estimate the Von Neumann Entropy of Density Matrices
The von Neumann entropy, named after John von Neumann, is the extension ...
read it

Doubleprecision FPUs in HighPerformance Computing: an Embarrassment of Riches?
Among the (uncontended) common wisdom in HighPerformance Computing (HPC...
read it

Beyond Linear Algebra
Our title challenges the reader to venture beyond linear algebra in desi...
read it

Faster Randomized Methods for Orthogonality Constrained Problems
Recent literature has advocated the use of randomized methods for accele...
read it

Determinantal Point Processes in Randomized Numerical Linear Algebra
Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop...
read it
Photonic coprocessors in HPC: using LightOn OPUs for Randomized Numerical Linear Algebra
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 constanttime 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.
READ FULL TEXT
Comments
There are no comments yet.