分析GPU及其他领域的天文学算法
Analysing Astronomy Algorithms for GPUs and Beyond
摘要：
天文学依赖于不断增长的计算能力。处理器时钟频率已经趋于稳定，性能的提高现在以单个芯片上附加处理器内核的形式出现。这对天文软件社区提出了重大挑战。图形处理单元（GPU），现在能够通用计算，克服了困难的学习曲线和显着的加速表现出的并行硬件架构。我们提出了一种通用的方法来解决这种范式转变，基于算法的分析。我们描述了一个小集合的基础算法相关的天文学，并解释如何使用它们来缓解过渡到并行计算架构。我们证明了我们的方法的有效性，将其应用到四个著名的天文学问题：Hogbom清洁，逆射线拍摄的引力透镜，脉冲星去色散和体绘制。具有明确定义的内存访问模式和高运算强度的算法可以从并行架构中获得最大的性能提升，而那些涉及大量决策的算法可能难以利用可用的处理能力。
英文摘要：
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.
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