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Forthcoming cosmic microwave background (CMB) polarized anisotropyexperiments have the potential to revolutionize our understanding of theUniverse and fundamental physics. The sought-after, tale-telling signatureswill be however distributed over voluminous data sets which these experimentswill collect. These data sets will need to be efficiently processed andunwanted contributions due to astrophysical, environmental, and instrumentaleffects characterized and efficiently mitigated in order to uncover thesignatures. This poses a significant challenge to data analysis methods,techniques, and software tools which will not only have to be able to cope withhuge volumes of data but to do so with unprecedented precision driven by thedemanding science goals posed for the new experiments. A keystone of efficientCMB data analysis are solvers of very large linear systems of equations. Suchsystems appear in very diverse contexts throughout CMB data analysis pipelines,however they typically display similar algebraic structures and can thereforebe solved using similar numerical techniques. Linear systems arising in theso-called map-making problem are one of the most prominent and common ones. Inthis 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 frameworkimplements iterative solvers based on conjugate gradient techniques: enlargedand preconditioned using different preconditioners. We demonstrate theframework on simulated examples reflecting basic characteristics of theforthcoming data sets issued by ground-based and satellite-borne instruments,executing it on as many as 16,384 compute cores. The software is developed asan open source project freely available to the community at:https://github.com/B3Dcmb/midapack .

MAPPRAISER: A massively parallel map-making framework for multi-kilo pixel CMB experiments