Optimizing the computation of n-point correlations on large-scale astronomical data

Authors:
William B. March;Kenneth Czechowski;Marat Dukhan;Thomas Benson;Dongryeol Lee;Andrew J. Connolly;Richard Vuduc;Edmond Chow;Alexander G. Gray
Affiliations:
Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Tech Research Institute, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;University of Washington, Seattle, WA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA;Georgia Institute of Technology, Atlanta, GA
Venue:
SC '12 Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis
Year:
2012

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Abstract

The n-point correlation functions (npcf) are powerful statistics that are widely used for data analyses in astronomy and other fields. These statistics have played a crucial role in fundamental physical breakthroughs, including the discovery of dark energy. Unfortunately, directly computing the npcf at a single value requires O(Nn) time for N points and values of n of 2, 3, 4, or even larger. Astronomical data sets can contain billions of points, and the next generation of surveys will generate terabytes of data per night. To meet these computational demands, we present a highly-tuned npcf computation code that show an order-of-magnitude speedup over current state-of-the-art. This enables a much larger 3-point correlation computation on the galaxy distribution than was previously possible. We show a detailed performance evaluation on many different architectures.