Random sampling for estimating the performance of fast summations

Abstract

Summation of functions of N source points evaluated at M target points occurs commonly in many applications. To scale these approaches for large datasets, many fast algorithms have been proposed. In this technical report, we propose a Chernoff bound based efficient approach to test the performance of a fast summation algorithms providing a probabilistic accuracy. We further validate and use our approach in separate comparisons.