Fast period searches using the Lomb–Scargle algorithm on Graphics Processing Units for large datasets and real-time applications

M. Gowanlock, D. Kramer, D. E. Trilling, N. R. Butler, B. Donnelly

Research output: Contribution to journalArticlepeer-review

Abstract

Computing the periods of variable objects is well-known to be computationally expensive. Modern astronomical catalogs contain a significant number of observed objects. Therefore, even if the period ranges for particular classes of objects are well-constrained due to expected physical properties, periods must be derived for a tremendous number of objects. In this paper, we propose a GPU-accelerated Lomb–Scargle period finding algorithm that computes periods for single objects or for batches of objects as is necessary in many data processing pipelines. We demonstrate the performance of several optimizations, including comparing the use of shared and global memory GPU kernels and using multiple CUDA streams to copy periodogram data from the GPU to the host. Also, we quantify the difference between 32-bit and 64-bit floating point precision on two classes of GPUs, and show that the performance degradation of using 64-bit over 32-bit is greater on the CPU than a GPU designed for scientific computing. We find that the GPU algorithm achieves superior performance over the baseline parallel CPU implementation, achieving a speedup of up to 174.53×. The Vera C. Rubin Observatory will carry out the Legacy Survey of Space and Time (LSST). We perform an analysis that shows we can derive the rotation periods of batches of Solar System objects at LSST scale in near real-time, which will be employed in a future LSST event broker. All source codes have been made publicly available.

Original languageEnglish (US)
Article number100472
JournalAstronomy and Computing
Volume36
DOIs
StatePublished - Jul 2021

Keywords

  • Asteroids: General, Massively parallel algorithms
  • Methods: Data analysis
  • Methods: Numerical, Single instruction, Multiple data

ASJC Scopus subject areas

  • Astronomy and Astrophysics
  • Computer Science Applications
  • Space and Planetary Science

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