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Acceleration without Disruption: DFT Software as a Service

Authors :
Ju, Fusong
Wei, Xinran
Huang, Lin
Jenkins, Andrew J.
Xia, Leo
Zhang, Jia
Zhu, Jianwei
Yang, Han
Shao, Bin
Dai, Peggy
Mayya, Ashwin
Hooshmand, Zahra
Efimovskaya, Alexandra
Baker, Nathan A.
Troyer, Matthias
Liu, Hongbin
Publication Year :
2024

Abstract

Density functional theory (DFT) has been a cornerstone in computational chemistry, physics, and materials science for decades, benefiting from advancements in computational power and theoretical methods. This paper introduces a novel, cloud-native application, Accelerated DFT, which offers an order of magnitude acceleration in DFT simulations. By integrating state-of-the-art cloud infrastructure and redesigning algorithms for graphic processing units (GPUs), Accelerated DFT achieves high-speed calculations without sacrificing accuracy. It provides an accessible and scalable solution for the increasing demands of DFT calculations in scientific communities. The implementation details, examples, and benchmark results illustrate how Accelerated DFT can significantly expedite scientific discovery across various domains.

Subjects

Subjects :
Physics - Chemical Physics

Details

Database :
arXiv
Publication Type :
Report
Accession number :
edsarx.2406.11185
Document Type :
Working Paper