A GPU implementation of the harmonic sum algorithm. (arXiv:1812.02647v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Adamek_K/0/1/0/all/0/1">Karel Ad&#xe1;mek</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Armour_W/0/1/0/all/0/1">Wesley Armour</a>

Time-domain radio astronomy utilizes a harmonic sum algorithm as part of the
Fourier domain periodicity search, this type of search is used to discover
single pulsars. The harmonic sum algorithm is also used as part of the Fourier
domain acceleration search which aims to discover pulsars that are locked in
orbit around another pulsar or compact object. However porting the harmonic sum
to many-core architectures like GPUs is not a straightforward task. The main
problem that must be overcome is the very unfavourable memory access pattern,
which gets worse as the dimensionality of the harmonic sum increases. We
present a set of algorithms for calculating the harmonic sum that are more
suited to many-core architectures such as GPUs. We present an evaluation of the
sensitivity of these different approaches, and their performance. This work
forms part of the AstroAccelerate project which is a GPU accelerated software
package for processing time-domain radio astronomy data.

Time-domain radio astronomy utilizes a harmonic sum algorithm as part of the
Fourier domain periodicity search, this type of search is used to discover
single pulsars. The harmonic sum algorithm is also used as part of the Fourier
domain acceleration search which aims to discover pulsars that are locked in
orbit around another pulsar or compact object. However porting the harmonic sum
to many-core architectures like GPUs is not a straightforward task. The main
problem that must be overcome is the very unfavourable memory access pattern,
which gets worse as the dimensionality of the harmonic sum increases. We
present a set of algorithms for calculating the harmonic sum that are more
suited to many-core architectures such as GPUs. We present an evaluation of the
sensitivity of these different approaches, and their performance. This work
forms part of the AstroAccelerate project which is a GPU accelerated software
package for processing time-domain radio astronomy data.

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