Speeding up Monte Carlo Computations by Parallel Processing Using GPU for Uncertainty Evaluation in Accordance with GUM Supplement 2

Abstract

The GUM Supplement 2 deals with measurement models with more than one output quantities, which may be mutually correlated. Such measurement models are common in electrical metrology where the measurand can be complex-valued quantities, such as S-parameters. The GUM Supplement 2 describes a Monte Carlo Method (MCM) for evaluating the output quantities, their standard uncertainties, the covariances between them and the coverage region. The Standards and Calibration Laboratory (SCL) has developed six years ago a software tool for evaluation of measurement models for complex-valued quantities in accordance with GUM Supplement 2. The SCL software tool was written in Visual C++ and Visual Basic for Application (VBA), with Microsoft Excel as frontend user interface. As MCM involves large number of repetitive computations, this old SCL software tool has long processing time especially for complicated measurement models such as coaxial airline. Nowadays many personal computers are equipped with Graphics Processing Unit (GPU) containing massive number of floating point cores. A high end GPU may have nearly 2000 cores while the main CPU normally has only up to 4 cores. As MCM is well suited to parallel processing, to speed up the uncertainty computation, SCL has ported the algorithm to GPU using the Open Computing Language (OpenCL) which was specially designed to support parallel computing. The new SCL tool is an add-on module to Microsoft Excel which allows uncertainty budget listed in spreadsheet table to be calculated by MCM. GPU from the major suppliers Nvidia, AMD and Intel are supported. The uncertainty computation time can be reduced by more than ten times. This paper describes the design and implementation of this new software tool.