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UID:submissions.supercomputing.org_SC22_sess275_rpost182@linklings.com
SUMMARY:Exploring Performance of GeoCAT data analysis routines on GPUs
DESCRIPTION:Posters, Research Posters\n\nExploring Performance of GeoCAT d
 ata analysis routines on GPUs\n\nKashgarani, Miller, Suresh, Zacharias\n\n
 The GeoCAT-comp program is a Python toolkit used by the geoscience communi
 ty to analyze data. This project explores ways to port GeoCAT-comp to run 
 on GPUs, as recent supercomputers are shifting to include GPU accelerators
  as the major resource. Although GeoCAT-comp's routines are all sequential
  or utilize Dask parallelization on the CPU, the data processing is embarr
 assingly parallel and computationally costly, enabling us to optimize usin
 g GPUs. GeoCAT uses NumPy, Xarray, and Dask arrays for CPU parallelization
 . In this project, we examined different GPU-accelerated Python packages (
 e.g., Numba and CuPy). Taking into account the deliverability of the final
  porting method to the GeoCAT team, CuPy is selected. CuPy is a Python CUD
 A-enabled array backend module that is quite similar to NumPy. We analyzed
  the performance of the GPU-accelerated code compared to the Dask CPU para
 llelized code over various array sizes and resources, and through strong a
 nd weak scaling.\n\nRegistration Category: Tech Program Reg Pass, Exhibits
  Reg Pass
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