The Removal of Numerical Drift from Scientific Models

TL;DR

This paper presents an automated technique to remove numerical drift in scientific models, enabling better detection of coding errors and compiler bugs in complex simulations like weather forecasting models.

Contribution

It introduces a novel automated method for comparing program runs that isolates numerical drift, improving debugging accuracy in scientific computing.

Findings

Successfully applied to Weather Research and Forecasting model

Effectively exposes coding errors and compiler bugs

Reduces manual intervention in debugging processes

Abstract

Computer programs often behave differently under different compilers or in different computing environments. Relative debugging is a collection of techniques by which these differences are analysed. Differences may arise because of different interpretations of errors in the code, because of bugs in the compilers or because of numerical drift, and all of these were observed in the present study. Numerical drift arises when small and acceptable differences in values computed by different systems are integrated, so that the results drift apart. This is well understood and need not degrade the validity of the program results. Coding errors and compiler bugs may degrade the results and should be removed. This paper describes a technique for the comparison of two program runs which removes numerical drift and therefore exposes coding and compiler errors. The procedure is highly automated and requires very little intervention by the user. The technique is applied to the Weather Research and Forecasting model, the most widely used weather and climate modelling code.