A GPU implementation of the Correlation Technique for Real-time Fourier Domain Pulsar Acceleration Searches

TL;DR

This paper presents a GPU-accelerated implementation of the Fourier Domain Acceleration Search algorithm for pulsar detection, significantly improving processing speed for real-time analysis in radio astronomy.

Contribution

The authors develop and compare GPU-based implementations of FDAS, including a custom FFT shared memory approach, achieving substantial speedups over existing methods.

Findings

Shared memory FFT is 1.5 to 3.2 times faster than cuFFT for small filters.

GPU implementation is 4 to 6 times faster than previous GPU and OpenMP versions.

The work enables real-time pulsar acceleration searches for large radio astronomy datasets.

Abstract

The study of binary pulsars enables tests of general relativity. Orbital motion in binary systems causes the apparent pulsar spin frequency to drift, reducing the sensitivity of periodicity searches. Acceleration searches are methods that account for the effect of orbital acceleration. Existing methods are currently computationally expensive, and the vast amount of data that will be produced by next generation instruments such as the Square Kilometre Array (SKA) necessitates real-time acceleration searches, which in turn requires the use of High Performance Computing (HPC) platforms. We present our implementation of the Correlation Technique for the Fourier Domain Acceleration Search (FDAS) algorithm on Graphics Processor Units (GPUs). The correlation technique is applied as a convolution with multiple Finite Impulse Response filters in the Fourier domain. Two approaches are compared: the first uses the NVIDIA cuFFT library for applying Fast Fourier Transforms (FFTs) on the GPU, and the second contains a custom FFT implementation in GPU shared memory. We find that the FFT shared memory implementation performs between 1.5 and 3.2 times faster than our cuFFT-based application for smaller but sufficient filter sizes. It is also 4 to 6 times faster than the existing GPU and OpenMP implementations of FDAS. This work is part of the AstroAccelerate project, a many-core accelerated time-domain signal processing library for radio astronomy.