GPU Acceleration of Image Convolution using Spatially-varying Kernel

TL;DR

This paper demonstrates a GPU-accelerated implementation of spatially-varying kernel convolution for astronomical image subtraction, achieving significant speedups suitable for large-scale, high-throughput image processing.

Contribution

It introduces an efficient GPU-based method for convolution with spatially-varying kernels, overcoming limitations of FFT-based approaches and enabling faster astronomical image analysis.

Findings

Achieved 50x speedup over ANSI-C implementation.

Achieved 1000x speedup over initial IDL implementation.

Validated the method's practicality for petascale astronomical data processing.

Abstract

Image subtraction in astronomy is a tool for transient object discovery such as asteroids, extra-solar planets and supernovae. To match point spread functions (PSFs) between images of the same field taken at different times a convolution technique is used. Particularly suitable for large-scale images is a computationally intensive spatially-varying kernel. The underlying algorithm is inherently massively parallel due to unique kernel generation at every pixel location. The spatially-varying kernel cannot be efficiently computed through the Convolution Theorem, and thus does not lend itself to acceleration by Fast Fourier Transform (FFT). This work presents results of accelerated implementation of the spatially-varying kernel image convolution in multi-cores with OpenMP and graphic processing units (GPUs). Typical speedups over ANSI-C were a factor of 50 and a factor of 1000 over the initial IDL implementation, demonstrating that the techniques are a practical and high impact path to terabyte-per-night image pipelines and petascale processing.