多处理器近似消息传递综述
An Overview of Multi-Processor Approximate Message Passing
摘要：
近似消息传递（AMP）是一种算法框架，用于从噪声测量中解决线性逆问题，具有令人兴奋的应用，例如重建图像、音频、超光谱图像和各种其他信号，包括在压缩信号采集系统中采集的信号。大数据系统的日益普及增加了对大规模问题的兴趣，这可能涉及不适合传统计算系统的巨大测量矩阵。为了应对大规模处理的挑战，已经开发了AMP的多处理器（MP）版本。我们提供了两个这样的MP-AMP变体的概述。在row-MP-AMP中，每个计算节点存储矩阵的行的子集并且处理对应的测量。在列- MP-AMP中，每个节点存储列的子集，并且单独负责重构信号的一部分。我们将讨论这两种方法的优缺点，总结每种方法的最新研究结果，并解释每种方法何时可能是可行的方法。突出的方面包括一些最近的结果，两个MP-AMP算法的状态演变，并使用数据压缩，以减少在MP网络中的通信。
英文摘要：
Approximate message passing (AMP) is an algorithmic framework for solving linear inverse problems from noisy measurements, with exciting applications such as reconstructing images, audio, hyper spectral images, and various other signals, including those acquired in compressive signal acquisiton systems. The growing prevalence of big data systems has increased interest in large-scale problems, which may involve huge measurement matrices that are unsuitable for conventional computing systems. To address the challenge of large-scale processing, multiprocessor (MP) versions of AMP have been developed. We provide an overview of two such MP-AMP variants. In row-MP-AMP, each computing node stores a subset of the rows of the matrix and processes corresponding measurements. In column- MP-AMP, each node stores a subset of columns, and is solely responsible for reconstructing a portion of the signal. We will discuss pros and cons of both approaches, summarize recent research results for each, and explain when each one may be a viable approach. Aspects that are highlighted include some recent results on state evolution for both MP-AMP algorithms, and the use of data compression to reduce communication in the MP network.
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