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Creators/Authors contains: "Mukherjee, Lipi"

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  1. ; ; ; ; ; ; ; (, International Symposium on Benchmarking, Measuring and Optimization)
    The study of clouds, i.e., where they occur and what are their characteristics, plays a key role in the understanding of climate change. Clustering is a common machine learning technique used in atmospheric science to classify cloud types. Many parallelism techniques e.g., MPI, OpenMP and Spark, could achieve efficient and scalable clustering of large-scale satellite observation data. In order to understand their differences, this paper studies and compares three different approaches on parallel clustering of satellite observation data. Benchmarking experiments with k-means clustering are conducted with three parallelism techniques, namely OpenMP, OpenMP+MPI, and Spark, on a HPC cluster using up to 16 nodes. 
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  2. ; ; ; (, Optics Express)
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