BrahMap: A scalable and modular map-making framework for the CMB experiments

TL;DR

BrahMap is a scalable, modular map-making framework designed for future CMB experiments, leveraging high-performance computing to efficiently process vast data sets for sky map generation.

Contribution

It introduces BrahMap, a new high-performance, scalable map-making framework with CPU and GPU support, tailored for large-scale CMB data analysis.

Findings

Demonstrates BrahMap's scalability on CPU and GPU platforms.

Shows efficient processing of large CMB datasets.

Provides preliminary performance scaling results.

Abstract

The cosmic microwave background (CMB) experiments have reached an era of unprecedented precision and complexity. Aiming to detect the primordial B-mode polarization signal, these experiments will soon be equipped with $10^{4}$ to $10^{5}$ detectors. Consequently, future CMB missions will face the substantial challenge of efficiently processing vast amounts of raw data to produce the initial scientific outputs - the sky maps - within a reasonable time frame and with available computational resources. To address this, we introduce BrahMap, a new map-making framework that will be scalable across both CPU and GPU platforms. Implemented in C++ with a user-friendly Python interface for handling sparse linear systems, BrahMap employs advanced numerical analysis and high-performance computing techniques to maximize the use of super-computing infrastructure. This work features an overview of the BrahMap's capabilities and preliminary performance scaling results, with application to a generic CMB polarization experiment.