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Using hybrid GPU/CPU kernel splitting to accelerate spherical convolutions

2014/09/15 by P. M. Sutter, B. D. Wandelt, Benjamin D. Wandelt +5
Physics and Astronomy · #Astrophysics and Cosmic Phenomena #Cosmology and Gravitation Theories #Cosmology and Nongalactic Astrophysics (astro-ph.CO) #FOS: Physical sciences #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Radio Astronomy Observations and Technology #astro-ph.CO #astro-ph.IM

paper · pdf · doi:10.48550/arxiv.1409.4441

9 pages, 11 figures, 1 table, accepted by Astronomy & Computing w/ minor revisions. arXiv admin note: substantial text overlap with arXiv:1211.3556

openalex publication_date 2014/09/15 · arxiv created 2015/03/29 · arxiv updated 2015/03/31 · openalex created_date 2016/06/24 · openalex updated_date 2026/07/28

Abstract

We present a general method for accelerating by more than an order of magnitude the convolution of pixelated functions on the sphere with a radially-symmetric kernel. Our method splits the kernel into a compact real-space component and a compact spherical harmonic space component. These components can then be convolved in parallel using an inexpensive commodity GPU and a CPU. We provide models for the computational cost of both real-space and Fourier space convolutions and an estimate for the approximation error. Using these models we can determine the optimum split that minimizes the wall clock time for the convolution while satisfying the desired error bounds. We apply this technique to the problem of simulating a cosmic microwave background (CMB) anisotropy sky map at the resolution typical of the high resolution maps produced by the Planck mission. For the main Planck CMB science channels we achieve a speedup of over a factor of ten, assuming an acceptable fractional rms error of order 1.e-5 in the power spectrum of the output map.

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