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基干GPU的脉冲星终端关键算法研究
Alternative TitleKev Technologies Research of Pulsar Backend Algorithm Based on GPU
托乎提努尔
Subtype博士
Thesis Advisor王娜
2019
Degree Grantor中国科学院大学
Place of Conferral北京
Degree Name理学博士
Degree Discipline天文技术与方法
KeywordGPU,脉冲星终端算法,PFB,RFI抑制,消色散处理 GPU,Pulsar backend algorithm,PFB,RFI mitigation,De-dispersion processing
Abstract数字终端设备是射电望远镜系统的重要组成部分,主要承担模拟信号的数字化、处理和数据的高速传输。随着GPU性能的提升及开发工具链的不断完善,数字终端中用于信号的处理部分已转向为以GPU为核心的计算系统。基于GPU的脉冲星终端系统比传统的基于FPGA的终端系统具有更高的开发效率、更低的研制成本、更灵活的配置和更强大的信号处理能力。 脉冲星终端系统的核心算法包含多通道数字滤波、RFI抑制、消色散处理及脉冲星折叠等,由于计算量巨大,难以实现超宽带接收设备产生的海量数据的实时处理。高性能GPU集群系统可提供强大的计算资源,理论上可以解决脉冲星终端算法计算量巨大无法实时处理的难题。脉冲星终端算法的并行化与GPU相关算法的加速研究是基于GPU的数字终端研发的关键,同时也是基于GPU集群的脉冲星数据处理系统建设的重中之重。 论文根据新疆天文台观测过程中遇到的实际问题及未来观测发展需要开展了以下几个方面的研究工作。 (1)研究实现了基于新一代GPU和CUDA并行架构的高速分通道技术,解决了PFB算法计算量大、实时信号处理能力较差问题。通过设计分解的多相FIR滤波器,有效减小了FFT频谱泄露。在窗函数、FIR滤波器及FFT方法分析基础上,研究了有效抑制频谱泄露的方法。提出了GPU并行加速算法,与CPU串行算法比,具有显著的加速优势。合理分配所使用的各级存储资源,最大程度优化了GPU算法的性能。基于GPU实现了多相滤波器组,充分利用GPU的多线程、多核并行执行能力,大幅提升了多相滤波实时数据处理性能。 (2)消干扰算法和技术研究方面取得了新的进展。使用参考天线方法,有效抑制强干扰,保留望远镜数据的完整性,提高了观测系统灵敏度。提出了自适应消干扰算法模型,根据参考天线信号灵活实现了滤波参数的自动调整,有效消除了混入天文信号中的射频干扰。深入分析RFI特征,设计并优化了自适应RFI滤波器,大大提高了信号信噪比。GPU并行算法设计中,Kernel函数使用了GPU的全局内存、共享内存、SP寄存器及warp快速加法运算,优化GPU的并行资源,获得了比较理想的加速比,实现了海量数据的高速处理。研究了自适应滤波器输入信号的同步技术,通过仿真数据和Parkes超宽带接收机真实数据对算法进行了测试。 (3)研究实现了基于GPU的脉冲星消色散处理及折叠并行算法。采用高性能并行计算方法对非相干消色散、相干消色散及脉冲星折叠算法的多线程处理进行了深入研究,提出了算法的并行化加速方案。通过相干消色散算法能够完全消除色散效应,得到了接近真实轮廓的脉冲星信号。GPU算法使用cuFFT库高速实现了FFT和IFFT,优化GPU Kernel脉冲星信号处理算法,最终获得了令人满意的加速结果。实现了脉冲星信号的折叠算法,分析算法的密集型计算部分、研究了多线程任务分配、管理及通信,高效利用GPU的全局内存、共享内存、常量内存及寄存器,提高了GPU资源利用率,进而减少了计算时间,显著提升了相关算法的计算性能。 本论文采用NVIDIA新一代GPU及通用并行计算架构对脉冲星终端系统算法进行了系统的研究,介绍了GPU的硬件架构及CUDA编程模型,研究了GPU算法的并行优化方法。设计了多相滤波器组、自适应RFI滤波器、中值滤波器、非相干消色散、相干消色散及折叠并行算法,解决了由于算法计算量巨大在CPU平台上无法实时计算的难题。实验结果表示,设计的并行算法获得了几十倍到几百倍的加速比,算法的计算性能得到了大幅提升,充分发挥了GPU计算平台的巨大优势,所研究的相关技术为新疆天文台脉冲星数字终端系统的自主研制奠定了基础。
Other AbstractDigital backend is an indispensable part of the radio telescope system which mainly responsible for the digitization of analog signal, processing and high-speed transmission of data. With the improvement of GPU performance and development tool chains, the signal processing core of digital backend has turned to a GPU-centric computing system The GPU-based pulsar backend system has higher development efficiency, lower development cost, more flexible configuration and more powerful signal processing capability than the traditional FPGA-based backend system. The core algorithm of the pulsar backend system includes multi-channel digital filtering, RFI mitigation, de-dispersion processing and pulsar folding. Due to the hugeamount of computation, it is difficult to realize the real-time processing of massive data generated by ultra-wideband receiver equipment. The high-performance GPU cluster system can provide powerful computing resources, and theoretically solve the problem that the pulsar backend algorithm cannot be processed in real time. The parallelization of pulsar backend algorithm and the acceleration research of GPU-related algorithms are the key factor to the development of GPU-based digital backend, it is also crucial in the construction of pulsar data processing systems based on GPU clusters. According to the actual problems encountered in the observation process of Xinjiang Astronomical Observatory and the needs of future observation and development, the thesis carried out the following research work. (1) The research has realized high-speed polyphase channelizer technology basedon the new generation GPU and CUDA paralel architecture, and solved the problem that the large computational complexity and poor real-time signal processing capability of PFB algorithm. By designing the analysis polyphase FIR filter, we effectively reduced the spectrum leakage of Fourier transform, and investigated the method of suppressing spectrum leakages based on the window function, FIR filterand FFT. In this paper, the GPU parallel acceleration algorithm is proposed, which has a significant acceleration advantages compared with the CPU serial algorithm. The use of aligned and coalesced memory accesses maximizes the performance of the GPU algorithm. We applied the polyphase filer banks based on GPU, which fully utilized the multi-thread and multi-core paralel execution capability of the GPU, and greatly improved the real-time data processing performance of algorithm.(2)A new progress has been achieved in the RFI mitigation algorithms and techniques. We used the reference antenna methods to effectively suppress strong interference, preserve the integrity of the telescope data, and improved the sensitivity of the observation system An algorithm model of adaptive interference mitigation was proposed, and which automatically adjusts the filtering parameters according to the reference antenna signal, effectively eliminating the radio frequency interference mixed into the astronomical signal. We depth analyzed the RFI features, designed and optimized the adaptive RFI filters, and greatly improved the signal-to-noise ratio. In the design of GPU parallel algorithm, the kemel function use the GPU's global memory, shared memory, SP register and warp fast addition operation to optimized the parallel resources of the GPUs, and obtained an ideal speedup ratio, realized high-speed massive data processing. We studied the synchronization technique of the adaptive filter input signal. The RFI mitigation algorithm was test by simulation data and real data of Parkes ultra-wideband receiver. (3) The GPU-based pulsar de-dispersion and folding parallel algorithm are implemented and studied, the high-performance parallel computing method is use to study the multi-thread processing of incoherent de-dispersion, coherent de-dispersion and pulsar folding algorithm. The parallelization acceleration scheme of the algorithm is proposed. By the coherent de-dispersion algorithm, we completely eliminated the dispersion effect and obtained a pulsar signal that close to real pulsar profile. The GPU algorithm used the cuFFT library to implement FFT and IFFT at high speed, optimized the GPU kemel of pulsar signal processing algorithm, and finally obtained satisfactory acceleration results. The of pulsar signal folding algorithm is implement, and the intensive calculation part of the algorithm is analyzed, the multi-thread task assignment, management and communication are studied, and the GPU's global memory, shared memory, constant memory and registers are utilized efficiently, thereby improved GPU resource utilization. In turn, the calculation time is been reduced and computational performance of the related algorithm is significantly improved. In this thesis, we use NVIDIA's new generation GPU and general parallelcomputing architecture to study the algorithm of pulsar backend system systematically, introduced the hardware architecture and CUDA programming model of GPUs, and studied the parallel optimization methods of GPU algorithm. The polyphase fiter bank, adaptive RFI filtering, median filtering, incoherent de-dispersion, coherent de-dispersion and folding parallel algorithm are designed to solve the problem that the algorithm cannot calculated in real time on the CPU platform due to the huge amount of computation. The experimental results show that the designed parallel algorithm obtained several tens to hundreds of times acceleration ratio, and greatly improved the computational performance of the algorithm, fully demonstrated the huge advantages of the GPU computing platform. The related technologies have laid a foundation for the independent development of the pulsar digital backend system of the Xinjiang Astronomical Observatory.
Pages115
Language中文
Document Type学位论文
Identifierhttp://ir.xao.ac.cn/handle/45760611-7/2713
Collection研究生学位论文
Recommended Citation
GB/T 7714
托乎提努尔. 基干GPU的脉冲星终端关键算法研究[D]. 北京. 中国科学院大学,2019.
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