Fast Period Searches Using the Lomb-Scargle Algorithm on Graphics Processing Units for Large Datasets and Real-Time Applications

TL;DR

This paper introduces a GPU-accelerated Lomb-Scargle algorithm that significantly speeds up period detection in large astronomical datasets, enabling near real-time analysis for projects like LSST.

Contribution

The paper presents a novel GPU implementation of the Lomb-Scargle periodogram with optimizations that outperform CPU methods, facilitating rapid analysis of massive datasets.

Findings

Achieves up to 174.53x speedup over CPU implementations.

Enables near real-time period detection for large astronomical surveys.

Demonstrates the impact of floating point precision on GPU performance.

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$\times$. 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 code has been made publicly available.