Fast Quadratic Discriminant Analysis Using GPGPU for Sea Ice Forecasting

General Purpose computing on Graphics Processor Units (GPGPU) brings massively parallel computing (hundreds of compute cores) to the desktop at a reasonable cost, but requires that algorithms be carefully designed to take advantage of this power. The present work explores the possibilities of CUDA (NVIDIA Compute Unified Device Architecture) using GPGPU for Quadratic Discriminant (QD) analysis. QD analysis is a form of multivariate statistical analysis that can be applied to forecasting seasonal sea ice freeze-up and break-up. The forecast problem is formulated as a classification problem, with two classes (e.g., "ice" and "no ice") and the objective of the analysis is to decide which of the classes best describes the ice/no ice condition at a particular geographic point on a specified date. We have conducted experiments to measure the performance of the GPU with respect to the serial CPU, parallel CPU (OpenMP), MATLAB, MATLAB (Parallel for) implementations. The experiments consist of implementing a serial CPU, parallel CPU (OpenMP), MATLAB, MATLAB (Parallel for) and GPU versions of the QD analysis algorithm and executing all versions on several data sets to compare the performance. Our results show speed up of up to 426 times, reducing the elapsed time from over 15 hours to about 2 minutes.