Efficient Parallel Algorithm for Estimating Higher-order Polyspectra

Joseph Tomlinson, Donghui Jeong, Juhan Kim

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Article number116
JournalAstronomical Journal
Issue number3
StatePublished - 2019

All Science Journal Classification (ASJC) codes

  • Astronomy and Astrophysics
  • Space and Planetary Science


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