MADmap: A MASSIVELY PARALLEL MAXIMUM LIKELIHOOD COSMIC MICROWAVE BACKGROUND MAP-MAKER
MADmap: A MASSIVELY PARALLEL MAXIMUM LIKELIHOOD COSMIC MICROWAVE BACKGROUND MAP-MAKER
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DOI：
10.1088/0067-0049/187/1/212
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发表时间：
2009-06
期刊：
影响因子：
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通讯作者：
C. Cantalupo;J. Borrill;A. Jaffe;T. Kisner;R. Stompor
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作者：
C. Cantalupo;J. Borrill;A. Jaffe;T. Kisner;R. Stompor
MADmap是一个软件应用程序，用于从包含相关噪声的时间序列数据中生成天空的最大似然图像，例如宇宙微波背景（CMB）实验收集的数据。它可以在从小型工作站到大规模并行超级计算机的平台上高效工作。地图制作是分析所有CMB数据集的关键步骤，最大似然法是最准确和最广泛适用的算法;然而，这是一项具有挑战性的计算任务。随着下一代地基、球载和卫星CMB偏振实验的发展，这一挑战只会增加。这些实验试图测量的B模式信号的微弱性要求他们收集大量的数据集。MADmap已经在O（1011）时间样本，O（108）像素和O（104）内核上运行，并正在进行扩展到下一代数据集和超级计算机的工作。我们描述了MADmap的算法的基础上周围的预处理共轭梯度求解器，快速傅立叶变换，稀疏矩阵运算。我们强调MADmap的能力，以解决现实的CMB数据集的分析中通常遇到的问题，并描述其应用程序模拟普朗克和EBEX实验。大规模并行和分布式实现的详细和缩放的复杂性所需的资源。MADmap能够分析目前可用的计算资源上收集的最大数据集，我们认为，鉴于摩尔定律，MADmap将能够减少最大规模的投影数据集。
MADmap is a software application used to produce maximum likelihood images of the sky from time-ordered data which include correlated noise, such as those gathered by cosmic microwave background (CMB) experiments. It works efficiently on platforms ranging from small workstations to the most massively parallel supercomputers. Map-making is a critical step in the analysis of all CMB data sets, and the maximum likelihood approach is the most accurate and widely applicable algorithm; however, it is a computationally challenging task. This challenge will only increase with the next generation of ground-based, balloon-borne, and satellite CMB polarization experiments. The faintness of the B-mode signal that these experiments seek to measure requires them to gather enormous data sets. MADmap is already being run on up to O(1011) time samples, O(108) pixels, and O(104) cores, with ongoing work to scale to the next generation of data sets and supercomputers. We describe MADmap's algorithm based around a preconditioned conjugate gradient solver, fast Fourier transforms, and sparse matrix operations. We highlight MADmap's ability to address problems typically encountered in the analysis of realistic CMB data sets and describe its application to simulations of the Planck and EBEX experiments. The massively parallel and distributed implementation is detailed and scaling complexities are given for the resources required. MADmap is capable of analyzing the largest data sets now being collected on computing resources currently available, and we argue that, given Moore's Law, MADmap will be capable of reducing the most massive projected data sets.