Analysing Astronomy Algorithms for GPUs and Beyond

TL;DR

This paper analyzes astronomy algorithms to facilitate their adaptation to GPU and parallel architectures, demonstrating how algorithm characteristics influence performance gains in massively-parallel computing.

Contribution

It introduces a generalized approach for analyzing astronomy algorithms to ease their transition to GPU and parallel computing architectures, supported by case studies.

Findings

Algorithms with predictable memory access and high arithmetic intensity benefit most from parallel architectures.

Decision-heavy algorithms may face challenges in leveraging massively-parallel hardware.

The approach aids in identifying suitable algorithms for GPU acceleration in astronomy.

Abstract

Astronomy depends on ever increasing computing power. Processor clock-rates have plateaued, and increased performance is now appearing in the form of additional processor cores on a single chip. This poses significant challenges to the astronomy software community. Graphics Processing Units (GPUs), now capable of general-purpose computation, exemplify both the difficult learning-curve and the significant speedups exhibited by massively-parallel hardware architectures. We present a generalised approach to tackling this paradigm shift, based on the analysis of algorithms. We describe a small collection of foundation algorithms relevant to astronomy and explain how they may be used to ease the transition to massively-parallel computing architectures. We demonstrate the effectiveness of our approach by applying it to four well-known astronomy problems: Hogbom CLEAN, inverse ray-shooting for gravitational lensing, pulsar dedispersion and volume rendering. Algorithms with well-defined memory access patterns and high arithmetic intensity stand to receive the greatest performance boost from massively-parallel architectures, while those that involve a significant amount of decision-making may struggle to take advantage of the available processing power.