  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 .
