Massively Parallel Computation of Similarity Matrices from Piecewise Constant Invariants

Abstract

We present a computational framework for piecewise constant functions (PCFs) and use this for several types of computations that are useful in statistics, e.g., averages, similarity matrices, and so on. We give a linear-time, allocation-free algorithm for working with pairs of PCFs at machine precision. From this, we derive algorithms for computing reductions of several PCFs. The algorithms have been implemented in a highly scalable fashion for parallel execution on CPU and, in some cases, (multi-)GPU, and are provided in a \proglang{Python} package. In addition, we provide support for multidimensional arrays of PCFs and vectorized operations on these. As a stress test, we have computed a distance matrix from 500,000 PCFs using 8 GPUs.