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arXiv 1702.00844astro-ph.IM
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用众核硬件关联射电天文信号

Correlating Radio Astronomy Signals with Many-Core Hardware

Rob V. van Nieuwpoort, John W. Romein

摘要:

射电天文学最近的发展是用许多小天线取代传统的碟形天线。这些信号被组合成一个巨大的虚拟望远镜。巨大的数据流被互相关以滤除噪声。这尤其具有挑战性,因为计算需求随着数据流的数量呈二次方增长。此外,相关器不仅是计算密集型的,而且是非常密集的I/O。例如,LOFAR望远镜每天将产生超过100 TB的数据。未来的SKA望远镜甚至需要艾次浮点运算和千兆比特/秒的I/O。最近的趋势是在软件而不是专用硬件中进行关联,以增加灵活性并减少开发工作。我们在多核CPU和众核架构(如NVIDIA和ATI GPU以及Cell/B. E)上评估相关器算法。correlator是一个流式实时应用程序,比目前通常在众核硬件上实现的应用程序的I/O密集度要高得多。与IBM Blue Gene/P超级计算机上的LOFAR产生式相关器进行了比较。我们调查性能,功率效率和可编程性。我们确定了几个重要的架构问题,导致架构执行次优。我们的研究结果适用于一般的数据密集型应用程序。当前GPU的处理能力和存储器带宽对于相关性目的而言是高度不平衡的。虽然Blue Gene/P上的产品相关器达到了理论峰值性能的96%,但在ATI GPU上只有16%,在NVIDIA GPU上只有32%。细胞/B.E.相比之下,处理器达到了出色的92%。我们发现Cell/B.E.和NVIDIA GPU是最节能的解决方案,它们运行相关器的能效至少是Blue Gene/P的4倍。

英文摘要:

A recent development in radio astronomy is to replace traditional dishes with many small antennas. The signals are combined to form one large, virtual telescope. The enormous data streams are cross-correlated to filter out noise. This is especially challenging, since the computational demands grow quadratically with the number of data streams. Moreover, the correlator is not only computationally intensive, but also very I/O intensive. The LOFAR telescope, for instance, will produce over 100 terabytes per day. The future SKA telescope will even require in the order of exaflops, and petabits/s of I/O. A recent trend is to correlate in software instead of dedicated hardware, to increase flexibility and to reduce development efforts. We evaluate the correlator algorithm on multi-core CPUs and many-core architectures, such as NVIDIA and ATI GPUs, and the Cell/B.E. The correlator is a streaming, real-time application, and is much more I/O intensive than applications that are typically implemented on many-core hardware today. We compare with the LOFAR production correlator on an IBM Blue Gene/P supercomputer. We investigate performance, power efficiency, and programmability. We identify several important architectural problems which cause architectures to perform suboptimally. Our findings are applicable to data-intensive applications in general. The processing power and memory bandwidth of current GPUs are highly imbalanced for correlation purposes. While the production correlator on the Blue Gene/P achieves a superb 96% of the theoretical peak performance, this is only 16% on ATI GPUs, and 32% on NVIDIA GPUs. The Cell/B.E. processor, in contrast, achieves an excellent 92%. We found that the Cell/B.E. and NVIDIA GPUs are the most energy-efficient solutions, they run the correlator at least 4 times more energy efficiently than the Blue Gene/P.

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。

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