# Efficient parallel algorithm for estimating higher-order polyspectra

## TL;DR

This paper introduces a highly efficient parallel algorithm for estimating higher-order polyspectra in galaxy density fields, leveraging FFT and optimized memory layout to handle large data with minimal communication overhead.

## Contribution

The authors develop a novel parallel algorithm based on the Scoccimarro estimator that significantly improves the efficiency of measuring higher-order polyspectra in large datasets.

## Key findings

- Reduces computational time for polyspectra estimation
- Minimizes inter-CPU communication in parallel processing
- Handles large datasets efficiently with optimized memory layout

## Abstract

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 by using the Fast-Fourier Transform (FFT), 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.