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引用次数: 10
摘要
雷达是一种数据密集型的测量技术,通常需要大量的处理才能充分利用接收到的信号。然而,远程或移动雷达装置的计算能力有限,因此限制了用于实时决策的雷达数据产品。我们使用图形处理单元(gpu)来加速处理来自阿拉斯加加科纳高频主动极光研究计划(HAARP)设施的模块化UHF电离层雷达(MUIR)的高分辨率相位编码雷达数据。在此之前,这些数据无法在足够的时间内进行现场处理,从而无法在积极的实验活动中做出有用的决策,也无法将数据上传到费尔班克斯北极地区超级计算中心(ARSC)的高性能计算(HPC)资源中进行场外处理。在本文中,我们提出了一个工作站配备双NVIDIA GeForce GTX 480 GPU加速卡和ARSC PACMAN集群节点配备双NVIDIA Tesla M2050卡的雷达数据处理性能的比较。两种平台都满足性能要求,相对便宜并且可以在HAARP等远程观测站有效地操作。
GPU Performance Comparison for Accelerated Radar Data Processing
Radar is a data-intensive measurement technique often requiring significant processing to make full use of the received signal. However, computing capacity is limited at remote or mobile radar installations thereby limiting radar data products used for real-time decisions. We used graphics processing units (GPUs) to accelerate processing of high resolution phase-coded radar data from the Modular UHF Ionosphere Radar (MUIR) at the High-frequency Active Auroral Research Program (HAARP) facility in Gakona, Alaska. Previously, this data could not be processed on-site in sufficient time to be useful for decisions made during active experiment campaigns, nor could the data be uploaded for off-site processing to high-performance computing (HPC) resources at the Arctic Region Supercomputing Center (ARSC) in Fairbanks. In this paper, we present a radar data-processing performance comparison of a workstation equipped with dual NVIDIA GeForce GTX 480 GPU accelerator cards and a node from ARSC's PACMAN cluster equipped with dual NVIDIA Tesla M2050 cards. Both platforms meet performance requirements, are relatively inexpensive and could operate effectively at remote observatories such as HAARP.