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Improving the scaling and performance of multiple time stepping‐based molecular dynamics with hybrid density functionals
Author(s) -
Mandal Sagarmoy,
Kar Ritama,
Klöffel Tobias,
Meyer Bernd,
Nair Nisanth N.
Publication year - 2022
Publication title -
journal of computational chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.907
H-Index - 188
eISSN - 1096-987X
pISSN - 0192-8651
DOI - 10.1002/jcc.26816
Subject(s) - bottleneck , scaling , molecular dynamics , computer science , formalism (music) , integrator , density functional theory , hybrid functional , linear scale , statistical physics , operator (biology) , algorithm , physics , mathematics , chemistry , quantum mechanics , bandwidth (computing) , art , repressor , computer network , visual arts , embedded system , musical , biochemistry , geometry , geodesy , transcription factor , gene , geography
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 (second rung) to hybrid density functionals (fourth 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 it.