Probabilistic Error Analysis For Sequential Summation of Real Floating Point Numbers

Abstract

We derive two probabilistic bounds for the relative forward error in the floating point summation of $n$ real numbers, by representing the roundoffs as independent, zero-mean, bounded random variables. The first probabilistic bound is based on Azuma's concentration inequality, and the second on the Azuma-Hoeffding Martingale. Our numerical experiments illustrate that the probabilistic bounds, with a stringent failure probability of $10^{-16}$, can be 1-2 orders of magnitude tighter than deterministic bounds. We performed the numerical experiments in Julia by summing up to $n=10^7$ single precision (binary32) floating point numbers, and up to $n=10^4$ half precision (binary16) floating point numbers. We simulated exact computation with double precision (binary64). The bounds tend to be tighter when all summands have the same sign.