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DTSTAMP:20181221T160728Z
LOCATION:D220
DTSTART;TZID=America/Chicago:20181112T140000
DTEND;TZID=America/Chicago:20181112T143000
UID:submissions.supercomputing.org_SC18_sess172_ws_phpsc102@linklings.com
SUMMARY:Accelerating the Signal Alignment Process in Time-Evolving Geometr
 ies Using Python
DESCRIPTION:Workshop\nParallel Application Frameworks, Reproducibility, Sc
 ientific Computing, Workshop Reg Pass\n\nAccelerating the Signal Alignment
  Process in Time-Evolving Geometries Using Python\n\nRamakrishnaiah, Baker
 \n\nThis paper addresses the computational challenges involved in postproc
 essing of signals received using multiple collectors (satellites). Multipl
 e low cost, small sized satellites can be used as dynamic beamforming arra
 ys (DBA) in remote sensing satellites. This usually requires precise metro
 logy and synchronized clocks. In order to mitigate this requirement, corre
 lation searches can be performed across time and frequency offset values t
 o align the signal. However, this process can take considerable time on tr
 aditional CPUs. We explore the use of heterogeneous parallel architectures
  to expedite the computation process, while trying to maintain the flexibi
 lity and ease of development using Python.\n\nThe Cross-Ambiguity Function
  (CAF) is used to perform correlation searches across a range of all possi
 ble frequency differences of arrival and time differences of arrival for a
  given emitter-collector geometry, followed by a phase alignment search. F
 or evolving geometries, maintaining the signal alignment over long time pe
 riods require time evolving CAF searches, which is computationally expensi
 ve. Consequently, we explore the use of massively parallel architectures u
 sing both distributed and shared memory parallelism, and show performance 
 results. We also propose a simple load balancing scheme for efficient use 
 of heterogenous architectures.\n\nWe show that the NumPy implementation pr
 ovides the same performance as the compiled Armadillo C++ code. Using diff
 erent optimization techniques, the results show a performance improvement 
 of 150x on a GPU compared to the naive implementation on a CPU.
URL:https://sc18.supercomputing.org/presentation/?id=ws_phpsc102&sess=sess
 172
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