Accelerating Statistical Analysis Through Parallel Computations

• Activity Number: 89
• Type: Invited
• Date/Time: Sunday, August 9, 2015 : 8:30 PM to 9:15 PM
• Sponsor: Korean International Statistical Society
• Author(s): Dorit Hammerling* and Doug Nychka and Benjamin Jamroz
• Companies: National Center for Atmospheric Research and National Center for Atmospheric Research and National Center for Atmospheric Research
• Keywords: Spatial statistics ; HPC ; parallel

Abstract

Statistical analysis often involves solving standard linear algebra problems. Specifically, for the statistical analysis of large spatial data, such as climate model output, these include: 1) solving a linear positive definite system and 2) finding the determinant of a positive definite matrix are the limiting numerical steps. Making the computation of these two steps faster is directly related to the ability to work with larger data sets and has the potential to create a breakthrough in the analysis of large spatio-temporal data sets. These steps can be greatly accelerated using GPUs and parallel CPUs. We will show examples of implementing such acceleration steps on the National Center for Atmospheric Research's supercomputing environment Yellowstone.