Parallelization Strategies for Density Matrix Renormalization Group Algorithms on Shared-Memory Systems

TL;DR

This paper explores shared-memory parallelization techniques for DMRG algorithms, demonstrating efficient scalability and enabling the solution of larger, more complex physical models on SMP systems.

Contribution

It introduces two novel parallelization approaches for DMRG algorithms and evaluates their performance on modern SMP architectures.

Findings

Good scalability up to at least eight processors

Able to solve larger problems than sequential DMRG

Effective parallelization of complex physical models

Abstract

Shared-memory parallelization (SMP) strategies for density matrix renormalization group (DMRG) algorithms enable the treatment of complex systems in solid state physics. We present two different approaches by which parallelization of the standard DMRG algorithm can be accomplished in an efficient way. The methods are illustrated with DMRG calculations of the two-dimensional Hubbard model and the one-dimensional Holstein-Hubbard model on contemporary SMP architectures. The parallelized code shows good scalability up to at least eight processors and allows us to solve problems which exceed the capability of sequential DMRG calculations.