A GPU parallel randomized CUR compression method for the Method of Moments

In this work, we propose a GPU parallel implementation of the randomized CUR (or Pseudo Skeleton) Approximation to compress the H-matrices of linear systems that arise in the discretization of integral equations modeling electromagnetic scattering problems. This compression method is highly parallelizable, in contrast with other similar methods such as the Adaptive Cross Approximation. It involves dense linear algebra computations that can be efficiently implemented on a GPU device. Besides, a stochastic convergence criterion is introduced to minimize the communication between the host and the device. Testing the code with standard cases shows the efficiency and accuracy of the method. (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons .org /licenses /by-nc -nd /4 .0/).