**Avoiding Communication in Cosmic Microwave Background Map-making**
Meisam Sharify

*17 September 2012, 10:00 - 17 September 2012, 10:30*
Salle/Bat : 455/PCRI-N

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**Résumé :**
Map-making is one of the crucial steps in the analysis of the CMB data

sets which can be done by applying the maximum likelihood approach.

This approach yields a solution in a form of a generalized least

square problem, which several software package such as MADmap solve to

produce an estimate of the sky. Enormous sizes of data sets

anticipated from the next generation of the CMB experiments require

massively parallel implementations of the map-making algorithm

suitable for high performance computing (HPC) systems. In this context

communication between

computational nodes is quickly becoming one of the major challenges,

which need to be robustly addressed to ensure scalability of the

map-making codes. Indeed, the analysis of the MADmap code, which uses

the preconditioned conjugate gradient (PCG) algorithm to compute the

map of the sky, shows that the cost of the communication required by

such an algorithm is usually significant, and on occasions dominant,

as compared to the cost of the entire procedure. For example,

Cantalupo et al (2010) find that for a simulated data set of a

Planck-like experiment 24.0% of run time is typically spent on

performing communication. We present a study of so-called

communication-avoiding, iterative solvers, such as the s-step PCG

method, as applied to generalized least squares systems in the context

of generic and CMB map-making applications.

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