Improving the Scaling and Performance of Multiple Time Stepping based Molecular Dynamics with Hybrid Density Functionals

TL;DR

This paper enhances the efficiency of hybrid density functional ab initio molecular dynamics simulations by implementing advanced multiple time stepping methods with parallelization, achieving significant speed-ups on high-performance computing platforms.

Contribution

It reports a parallelized implementation of MTACE and s-MTACE methods in CPMD, improving hybrid functional AIMD performance and addressing computational bottlenecks.

Findings

Achieved 7-9x speed-up in hybrid AIMD simulations.

Implemented task-group parallelization in CPMD.

Identified and proposed solutions for bottlenecks in s-MTACE.

Abstract

Density functionals at the level of the Generalized Gradient Approximation (GGA) and a plane-wave basis set are widely used today to perform ab initio molecular dynamics (AIMD) simulations. Going up in the ladder of accuracy of density functionals from GGA (2nd rung) to hybrid density functionals (4th rung) is much desired pertaining to the accuracy of the latter in describing structure, dynamics, and energetics of molecular and condensed matter systems. On the other hand, hybrid density functional based AIMD simulations are about two orders of magnitude slower than GGA based AIMD for systems containing ~100 atoms using ~100 compute cores. Two methods, namely MTACE and s-MTACE, based on a multiple time step integrator and adaptively compressed exchange operator formalism are able to provide a speed-up of about 7-9 in performing hybrid density functional based AIMD. In this work, we report an implementation of these methods using a task-group based parallelization within the CPMD program package, with the intention to take advantage of the large number of compute cores available on modern high-performance computing platforms. We present here the boost in performance achieved through this algorithm. This work also identifies the computational bottleneck in the s-MTACE method, and proposes a way to overcome that.