Improved parallelization techniques for the density matrix renormalization group

TL;DR

This paper introduces an enhanced distributed-memory parallelization strategy for the density matrix renormalization group, significantly improving scalability and efficiency for computing correlation functions in large-scale quantum systems.

Contribution

It presents a novel parallelization approach with detailed analysis and solutions to overcome previous limitations, achieving better performance and scalability.

Findings

Serial fraction of 9.4% in scalability analysis

Parallel efficiency of around 60% with up to eight nodes

Identification and mitigation of parallel slowdown sources

Abstract

A distributed-memory parallelization strategy for the density matrix renormalization group is proposed for cases where correlation functions are required. This new strategy has substantial improvements with respect to previous works. A scalability analysis shows an overall serial fraction of 9.4% and an efficiency of around 60% considering up to eight nodes. Sources of possible parallel slowdown are pointed out and solutions to circumvent these issues are brought forward in order to achieve a better performance.