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Wednesday, April 11 • 11:45am - 12:00pm
SYMPOSIA-13: Scalable Geospatiotemporal Clustering on Novel Fine-Grained Parallel Computer Architectures

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AUTHORS: Richard Tran Mills, Argonne National Laboratory; Vamsi Sripathi, Intel Corporation; Sarat Sreepathi, Oak Ridge National Laboratory; Forrest M. Hoffman, Oak Ridge National Laboratory; William W. Hargrove, USDA Forest Service Southern Research Station

ABSTRACT: The increasing availability of high-resolution geospatiotemporal data sets from sources such as observatory networks, remote sensing platforms, and computational Earth system models has opened new possibilities for knowledge discovery using data sets fused from disparate sources. Traditional algorithms and computing platforms may be impractical for the analysis and synthesis of data sets of this size; however, new algorithmic approaches and implementations that can effectively utilize the complex memory hierarchies and the extremely high levels of available parallelism in state-of-the-art high-performance computing platforms can enable such analysis. We describe a hybrid parallelism (MPI-OpenMP) based implementation of accelerated k-means clustering and some optimizations to boost computational intensity and utilization of wide SIMD lanes and many hardware threads on state-of-the art multi- and manycore processors, including the second-generation Intel Xeon Phi ("Knights Landing") processor based on the Intel Many Integrated Core (MIC) architecture, and on cutting edge GPGPU architectures, and we explore several applications thereof to large-scale analysis of MODIS NDVI and LiDAR-derived forest ecosystem data sets.

Wednesday April 11, 2018 11:45am - 12:00pm CDT
Water Tower Parlor