1 research outputs found
A New Fast Parallel Statistical Measurement Technique for Computational Cosmology
Higher order cumulants of point processes, such as skew and kurtosis, require
significant computational effort to calculate. The traditional counts-in-cells
method implicitly requires a large amount of computation since, for each
sampling sphere, a count of particles is necessary. Although alternative
methods based on tree algorithms can reduce execution time considerably, such
methods still suffer from shot noise when measuring moments on low amplitude
signals. We present a novel method for calculating higher order moments that is
based upon first top-hat filtering the point process data on to a grid. After
correcting for the smoothing process, we are able to sample this grid using an
interpolation technique to calculate the statistics of interest. The filtering
technique also suppresses noise and allows us to calculate skew and kurtosis
when the point process is highly homogeneous. The algorithm can be implemented
efficiently in a shared memory parallel environment provided a data-local
random sampling technique is used. The local sampling technique allows us to
obtain close to optimal speed-up for the sampling process on the Alphaserver
GS320 NUMA architecture.Comment: Non-specialist paper, 9 pages, 5 figures, accepted for publication in
Int. J. of High Perf. Comp. & Networkin