加速非相干解色散
Accelerating incoherent dedispersion
摘要：
非相干解色散是脉冲星和瞬变天文学中经常出现的计算密集型问题。对于当前和未来的瞬态流水线，去色散可以支配总执行时间，这意味着其计算速度对科学结果的质量和数量起着约束作用。因此，关键是算法能够利用商品计算硬件的趋势。考虑到这一目标，我们提出了分析的“直接”，“树”和“子带”去色散算法的潜力，有效地执行现代图形处理单元（GPU）。我们发现这三个都是优秀的候选者，并继续使用从分析中获得的洞察力来描述C for CUDA的实现。使用最新的CPU和GPU硬件，与优化的四核CPU代码相比，向GPU的过渡为直接算法提供了9倍的速度提升。对于现实的最新勘测参数，这些速度足够高，不需要进一步优化来实现实时处理。在需要进一步加速的情况下，我们发现树和子带算法能够以一定的拖尾、内存消耗和开发时间权衡为代价提供3- 7倍的更好性能。最后，我们讨论了这些结果对未来瞬态调查的影响。我们的GPU去色散代码以C库的形式公开提供，网址为：http://dedisp.googlecode.com/
英文摘要：
Incoherent dedispersion is a computationally intensive problem that appears frequently in pulsar and transient astronomy. For current and future transient pipelines, dedispersion can dominate the total execution time, meaning its computational speed acts as a constraint on the quality and quantity of science results. It is thus critical that the algorithm be able to take advantage of trends in commodity computing hardware. With this goal in mind, we present analysis of the 'direct', 'tree' and 'sub-band' dedispersion algorithms with respect to their potential for efficient execution on modern graphics processing units (GPUs). We find all three to be excellent candidates, and proceed to describe implementations in C for CUDA using insight gained from the analysis. Using recent CPU and GPU hardware, the transition to the GPU provides a speed-up of 9x for the direct algorithm when compared to an optimised quad-core CPU code. For realistic recent survey parameters, these speeds are high enough that further optimisation is unnecessary to achieve real-time processing. Where further speed-ups are desirable, we find that the tree and sub-band algorithms are able to provide 3-7x better performance at the cost of certain smearing, memory consumption and development time trade-offs. We finish with a discussion of the implications of these results for future transient surveys. Our GPU dedispersion code is publicly available as a C library at: http://dedisp.googlecode.com/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
