BIGDATA: Small: DCM: DA: Advancing real-time data processing and reduction in radio astronomical detectors

BIGDATA：小：DCM：DA：推进射电天文探测器的实时数据处理和减少

• 批准号: 1250720
• 负责人: Albrecht Karle
• 金额: $ 74.86万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-15 至 2017-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1250720&HistoricalAwards=false
• 关键词: BIGDATA; Small; DCM; DA; Advancing

This award is to support development and test the accelerated, efficient reconstruction and beam-forming algorithms for radio astronomical detectors. When possible, computations will be ported out to the Graphics Processing Unit (GPU) clusters that provide massively parallel floating-point capabilities. This approach is ideally suited to the data generated by digital radio astronomy arrays, and especially well suited when the respective hardware is deployment at remote observation sites - because of the proposed algorithms show excellent performance-to-power efficiency when compared to general-purpose supercomputers. Additionally, the real-time (Field Programmable Gate Arrays) FPGA-based algorithms will be developed for radio transient data reduction. They will help developing accelerated radio simulation methods to support and validate the proposed methodology. These algorithms and methodology will be tested at the Askaryan Radio Array (ARA), an array of in-ice antennae deployed in the U.S. Antarctic South Pole Station to detect highest-energy neutrinos coming from the deep Universe sources. The award's broader impacts are in the use of high-performance parallel computing and efficient real-time data reduction in the fields of radio astronomy, cosmology, and particle astrophysics. Expansion of the use of GPUs will provide the necessary computing power for next-generation experiments in a sustainable, efficient way. The post-doctoral researcher and graduate student will be part of the overall development and testing. In addition, undergraduate students will also be involved in the research. An on-line public research component will be developed to engage "citizen scientists" in the analysis of radio astronomy data on home computers.

该奖项旨在支持开发和测试射电天文探测器的加速，有效的重建和波束形成算法。在可能的情况下，计算将被移植到图形处理单元（GPU）集群，提供大规模并行浮点能力。这种方法非常适合数字射电天文阵列生成的数据，特别适合在远程观测站点部署相应硬件时-因为与通用超级计算机相比，所提出的算法显示出出色的性能-功率效率。此外，将开发基于FPGA的实时（现场可编程门阵列）算法，用于无线电瞬态数据简化。他们将帮助开发加速无线电仿真方法，以支持和验证拟议的方法。这些算法和方法将在Askaryan射电阵列（ARA）进行测试，这是一个部署在美国南极南极站的冰中天线阵列，用于探测来自宇宙深处的最高能量中微子。该奖项更广泛的影响是在射电天文学，宇宙学和粒子天体物理学领域使用高性能并行计算和高效的实时数据减少。GPU使用的扩展将以可持续、高效的方式为下一代实验提供必要的计算能力。博士后研究员和研究生将参与整体开发和测试。此外，本科生也将参与研究。将开发一个联机公共研究部分，使“公民科学家”参与在家用计算机上分析射电天文学数据。