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DTSTART:19700308T020000
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DTSTAMP:20260522T150116Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181112T153000
DTEND;TZID=America/Chicago:20181112T160000
UID:submissions.supercomputing.org_SC18_sess172_ws_phpsc107@linklings.com
SUMMARY:Data-Parallel Python for High Energy Physics Analyses
DESCRIPTION:Marc Paterno, Christopher Green, Jim Kowalkowski, and Saba Seh
 rish (Fermi National Accelerator Laboratory)\n\nIn this paper, we explore 
 features available in Python which are useful for data reduction tasks in 
 High Energy Physics (HEP). High-level abstractions in Python are convenien
 t for implementing data reduction tasks. However, in order for such abstra
 ctions to be practical, the efficiency of their performance must also be h
 igh. Because the data sets we process are typically large, we care about b
 oth I/O performance and in-memory processing speed. In particular, we eval
 uate the use of data-parallel programming, using MPI and numpy, to process
  a large experimental data set (42 TiB) stored in an HDF5 file. We measure
  the speed of processing of the data, distinguishing between the time spen
 t reading data and the time spent processing the data in memory, and demon
 strate the scalability of both, using up to 1200 KNL nodes (76800 cores) o
 n Cori at NERSC.\n\nTag: Parallel Application Frameworks, Reproducibility,
  Scientific Computing\n\nRegistration Category: Workshop Reg Pass\n\n
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