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Related Experiment Video

Updated: Mar 29, 2026

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Computation of Atmospheric Concentrations of Molecular Clusters from ab initio Thermochemistry

Published on: April 8, 2020

Multicore Parallelization of Kohn-Sham Theory.

Christopher J Woods¹, Philip Brown¹, Frederick R Manby¹

¹Centre for Computational Chemistry, School of Chemistry, University of Bristol, Bristol BS8 1TS, U.K.

Journal of Chemical Theory and Computation

|November 27, 2015

Summary

This study presents a multicore parallelization of Kohn-Sham theory, achieving near-linear scaling on up to sixteen cores. This computational approach significantly reduces runtime, offering performance competitive with accelerator cards.

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Area of Science:

Computational physics
Quantum chemistry

Background:

Kohn-Sham theory is a fundamental method in quantum chemistry and condensed matter physics.
Efficient computation is crucial for large-scale simulations.

Purpose of the Study:

To describe a multicore parallelization of Kohn-Sham theory.
To evaluate its performance on commodity hardware.

Main Methods:

Implementation of Kohn-Sham theory on shared-memory parallel processors.
Benchmarking on multisocket/multicore systems.

Main Results:

Near-linear scaling observed up to sixteen cores.
An order of magnitude reduction in computation time achieved.
Performance comparable to numerical accelerator cards.

Conclusions:

Multicore parallelization offers a viable and efficient approach for Kohn-Sham theory calculations.
Commodity hardware can achieve high performance for quantum simulations.