Multi-GPU based evaluation and analysis of prehistoric ice cores using OpenCL

Abstract

The analysis of prehistoric ice cores is a well established instrument in the field of climate research. Until recently, common methods were often based on the analysis of carbon dioxide and methane concentrations. The use of computed tomography based 3-D reconstructions for the evaluation and analysis of prehistoric ice cores yields the possibility to improve the accuracy of age determination by an order order of magnitude, from hundreds of years to decades. This, in turn, allows the improvement of the underlying model of the climatic development over the last several hundreds of thousands of years. The use of 3-D volumes allows a much more detailed analysis with respect to the size, amount, distribution and connectivity of air bubbles in the ice cores as a new climatic proxy. In this setting, we present a GPU-based approach for the efficient evaluation and analysis of air bubbles using OpenCL. As the raw data size can grow up to 10 TB per meter of ice core, we focus on a distributable and scalable approach, which is based on component labeling and can be scaled to multiple-GPUs using OpenCL.