Efficient Parallel Algorithm for Estimating Higher-order Polyspectra

Nonlinearities in the gravitational evolution, galaxy bias, and redshift-space distortion drive the observed galaxy density fields away from the initial near-Gaussian states. Exploiting such a non-Gaussian galaxy density field requires measuring higher-order correlation functions, or, its Fourier co...

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Vydané v:The Astronomical journal Ročník 158; číslo 3; s. 116 - 126
Hlavní autori: Tomlinson, Joseph, Jeong, Donghui, Kim, Juhan
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Madison The American Astronomical Society 01.09.2019
IOP Publishing
Predmet:
Algorithms
Astronomy
Correlation analysis
Density
Estimation
Fast Fourier transformations
Fourier transforms
Galactic evolution
Galaxies
large-scale structure of universe
methods: data analysis
Parallel processing
Red shift
ISSN:0004-6256, 1538-3881, 1538-3881
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Popis
Shrnutí:Nonlinearities in the gravitational evolution, galaxy bias, and redshift-space distortion drive the observed galaxy density fields away from the initial near-Gaussian states. Exploiting such a non-Gaussian galaxy density field requires measuring higher-order correlation functions, or, its Fourier counterpart, polyspectra. Here, we present an efficient parallel algorithm for estimating higher-order polyspectra. Based upon the Scoccimarro estimator, the estimator avoids direct sampling of polygons using the fast Fourier transform, and the parallelization overcomes the large memory requirement of the original estimator. In particular, we design the memory layout to minimize the inter-CPU communications, which excels in the code performance.
Bibliografia:Galaxies and Cosmology
AAS17449
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0004-6256
1538-3881
1538-3881
DOI:10.3847/1538-3881/ab3223