MAPPRAISER：一个用于数千像素CMB实验的大规模并行地图制作框架
MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments
摘要：
即将到来的宇宙微波背景（CMB）极化各向异性实验有可能彻底改变我们对宇宙和基础物理的理解。然而，这些实验将收集大量数据集，这些数据集将被有效地处理，由于天体物理、环境和仪器效应而产生的不必要的贡献将被表征和有效地减轻，以揭示这些特征。这对数据分析方法、技术和软件工具提出了重大挑战，这些方法、技术和软件工具不仅要能够处理大量数据，而且要以前所未有的精度来处理这些数据，这是由新实验提出的苛刻科学目标所驱动的。有效的CMB数据分析的关键是求解非常大的线性方程组。这样的系统出现在CMB数据分析流水线中的非常不同的上下文中，然而它们通常显示类似的代数结构，因此可以使用类似的数值技术来求解。线性方程组是地图绘制问题中最突出和最常见的一类。在这项工作中，我们提出了一个大规模并行，灵活和可扩展的框架，包括一个数值库，MIDAPACK，和一个高级别的代码，MAPPRAISER，它提供了有效地解决这些系统的工具。该框架实现了基于共轭梯度技术的迭代求解器：使用不同的预处理器进行放大和预处理。我们展示了模拟示例的框架，反映了地面和卫星仪器发出的即将到来的数据集的基本特征，在多达16，384个计算核心上执行。该软件是作为一个开源项目开发的，可在https://github.com/B3Dcmb/midapack上免费提供给社区。
英文摘要：
Forthcoming cosmic microwave background (CMB) polarized anisotropy experiments have the potential to revolutionize our understanding of the Universe and fundamental physics. The sought-after, tale-telling signatures will be however distributed over voluminous data sets which these experiments will collect. These data sets will need to be efficiently processed and unwanted contributions due to astrophysical, environmental, and instrumental effects characterized and efficiently mitigated in order to uncover the signatures. This poses a significant challenge to data analysis methods, techniques, and software tools which will not only have to be able to cope with huge volumes of data but to do so with unprecedented precision driven by the demanding science goals posed for the new experiments. A keystone of efficient CMB data analysis are solvers of very large linear systems of equations. Such systems appear in very diverse contexts throughout CMB data analysis pipelines, however they typically display similar algebraic structures and can therefore be solved using similar numerical techniques. Linear systems arising in the so-called map-making problem are one of the most prominent and common ones. In this work we present a massively parallel, flexible and extensible framework, comprised of a numerical library, MIDAPACK, and a high level code, MAPPRAISER, which provide tools for solving efficiently such systems. The framework implements iterative solvers based on conjugate gradient techniques: enlarged and preconditioned using different preconditioners. We demonstrate the framework on simulated examples reflecting basic characteristics of the forthcoming data sets issued by ground-based and satellite-borne instruments, executing it on as many as 16,384 compute cores. The software is developed as an open source project freely available to the community at: https://github.com/B3Dcmb/midapack .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
