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DTSTART:19700308T020000
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BEGIN:VEVENT
DTSTAMP:20181221T160903Z
LOCATION:C2/3/4 Ballroom
DTSTART;TZID=America/Chicago:20181115T083000
DTEND;TZID=America/Chicago:20181115T170000
UID:submissions.supercomputing.org_SC18_sess324_post126@linklings.com
SUMMARY:An Efficient SIMD Implementation of Pseudo-Verlet Lists for Neighb
 or Interactions in Particle-Based Codes
DESCRIPTION:Poster\nTech Program Reg Pass, Exhibits Reg Pass\n\nAn Efficie
 nt SIMD Implementation of Pseudo-Verlet Lists for Neighbor Interactions in
  Particle-Based Codes\n\nWillis, Schaller, Gonnet\n\nIn particle-based sim
 ulations, neighbour finding (i.e. finding pairs of particles to interact w
 ithin a given range) is the most time consuming part of the computation. O
 ne of the best such algorithms, which can be used for both Molecular Dynam
 ics (MD) and Smoothed Particle Hydrodynamics (SPH) simulations is the pseu
 do-Verlet list algorithm. The algorithm improves the neighbour finding by 
 reducing the number of spurious pair-wise distance calculations. This algo
 rithm, however, does not vectorize trivially, and hence makes it difficult
  to exploit SIMD-parallel architectures. On this poster, we present severa
 l novel modifications as well as a vectorization strategy for the algorith
 m which lead to overall speed-ups over the scalar version of the algorithm
  of 2.21x for the AVX instruction set (SIMD width of 8), 2.41x for AVX2, a
 nd 3.65x for AVX-512 (SIMD width of 16).
URL:https://sc18.supercomputing.org/presentation/?id=post126&sess=sess324
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