An N N Parallel Fast Direct Solver for Kernel Matrices [article]

Chenhan D. Yu, William B. March, George Biros
2017 arXiv   pre-print
Kernel matrices appear in machine learning and non-parametric statistics. Given N points in d dimensions and a kernel function that requires O(d) work to evaluate, we present an O(dN N)-work algorithm for the approximate factorization of a regularized kernel matrix, a common computational bottleneck in the training phase of a learning task. With this factorization, solving a linear system with a kernel matrix can be done with O(N N) work. Our algorithm only requires kernel evaluations and does
more » ... ot require that the kernel matrix admits an efficient global low rank approximation. Instead our factorization only assumes low-rank properties for the off-diagonal blocks under an appropriate row and column ordering. We also present a hybrid method that, when the factorization is prohibitively expensive, combines a partial factorization with iterative methods. As a highlight, we are able to approximately factorize a dense 11M×11M kernel matrix in 2 minutes on 3,072 x86 "Haswell" cores and a 4.5M×4.5M matrix in 1 minute using 4,352 "Knights Landing" cores.
arXiv:1701.02324v1 fatcat:cmr5qrd6breh5e6wj67vb56tra