English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The predominance of Kohn-Sham density functional theory (KS-DFT) for thetheoretical treatment of large experimentally relevant systems in molecularchemistry and materials science relies primarily on the existence of efficientsoftware implementations which are capable of leveraging the latest advances inmodern high performance computing (HPC). With recent trends in HPC leadingtowards in increasing reliance on heterogeneous accelerator based architecturessuch as graphics processing units (GPU), existing code bases must embrace thesearchitectural advances to maintain the high-levels of performance which havecome to be expected for these methods. In this work, we purpose a three-levelparallelism scheme for the distributed numerical integration of theexchange-correlation (XC) potential in the Gaussian basis set discretization ofthe Kohn-Sham equations on large computing clusters consisting of multiple GPUsper compute node. In addition, we purpose and demonstrate the efficacy of theuse of batched kernels, including batched level-3 BLAS operations, in achievinghigh-levels of performance on the GPU. We demonstrate the performance andscalability of the implementation of the purposed method in the NWChemExsoftware package by comparing to the existing scalable CPU XC integration inNWChem.

On the Efficient Evaluation of the Exchange Correlation Potential on Graphics Processing Unit Clusters