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
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.
