DOI: 10.4018/IJGHPC.2018010105 International Journal of Grid and High Performance Computing Volume 10 • Issue 1 • January-March 2018 Copyright © 2018, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 71 Embarrassingly Parallel GPU Based Matrix Inversion Algorithm for Big Climate Data Assimilation M. Varalakshmi, VIT University, Vellore, India Amit Parashuram Kesarkar, National Atmospheric Research Laboratory, Chittoor, India Daphne Lopez, VIT University, Vellore, India ABSTRACT Attempts to harness the big climate data that come from high-resolution model output and advanced sensors to provide more accurate and rapidly-updated weather prediction, call for innovations in the existing data assimilation systems. Matrix inversion is a key operation in a majority of data assimilation techniques. Hence, this article presents out-of-core CUDA implementation of an iterative method of matrix inversion. The results show significant speed up for even square matrices of size 1024 X 1024 and more, without sacrificing the accuracy of the results. In a similar test environment, the comparison of this approach with a direct method such as the Gauss-Jordan approach, modified to process large matrices that cannot be processed directly within a single kernel call shows that the former is twice as efficient as the latter. This acceleration is attributed to the division-free design and the embarrassingly parallel nature of every sub-task of the algorithm. The parallel algorithm has been designed to be highly scalable when implemented with multiple GPUs for handling large matrices. KEywoRDS Big Climate Data, Convergence Rate, GPU, Iterative Method, Matrix Type Identification, Numerical Weather Prediction, Parallel Matrix Inverse, Parallel Reduction 1. INTRoDUCTIoN The advent of Big data technology has brought a great revolution in the science of Numerical Weather Prediction. Big data in NWP actually refers to ‘climate big data’ that come from rapid and dense observations from advanced sensors and very high-resolution model output. A ten-fold increase in the model resolution would require 10 4 more computations for the four dimensions in space and time. To achieve this massively challenging throughput and to fully utilize this big data so as to provide more accurate and rapidly updated weather prediction, innovations have to be brought to the existing Data Assimilation and NWP systems (Big Data Assimilation) (Miyoshi et al., 2016a; Miyoshi et al., 2016b). This can help strengthen our early warning system against regional, sudden and severe calamities such as hurricanes, heavy rain, flooding, landslides and the alike. Innovative research has already started towards speeding up the various phases of NWP such as observation data processing, model run and data transfer between model and DA. Even in the Data assimilation phase, ways to improve storage and processing of large matrices and vectors can be explored. With the three spatial dimensions and one temporal dimension considered in Variational data assimilation algorithms and Kalman Filter based assimilation algorithms, the atmospheric state variables such as Wind, Pressure,