Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS
Linear-scaling density functional theory (DFT) is an efficient method to describe the electronic structures of molecules, semiconductors, and insulators to avoid the high cubic-scaling cost in conventional DFT calculations. Here, we present a parallel implementation of linear-scaling density matrix...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-11-01
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| Series: | Frontiers in Chemistry |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fchem.2020.589910/full |
| _version_ | 1818857400844681216 |
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| author | Zhaolong Luo Xinming Qin Lingyun Wan Wei Hu Jinlong Yang |
| author_facet | Zhaolong Luo Xinming Qin Lingyun Wan Wei Hu Jinlong Yang |
| author_sort | Zhaolong Luo |
| collection | DOAJ |
| description | Linear-scaling density functional theory (DFT) is an efficient method to describe the electronic structures of molecules, semiconductors, and insulators to avoid the high cubic-scaling cost in conventional DFT calculations. Here, we present a parallel implementation of linear-scaling density matrix trace correcting (TC) purification algorithm to solve the Kohn–Sham (KS) equations with the numerical atomic orbitals in the HONPAS package. Such a linear-scaling density matrix purification algorithm is based on the Kohn's nearsightedness principle, resulting in a sparse Hamiltonian matrix with localized basis sets in the DFT calculations. Therefore, sparse matrix multiplication is the most time-consuming step in the density matrix purification algorithm for linear-scaling DFT calculations. We propose to use the MPI_Allgather function for parallel programming to deal with the sparse matrix multiplication within the compressed sparse row (CSR) format, which can scale up to hundreds of processing cores on modern heterogeneous supercomputers. We demonstrate the computational accuracy and efficiency of this parallel density matrix purification algorithm by performing large-scale DFT calculations on boron nitrogen nanotubes containing tens of thousands of atoms. |
| first_indexed | 2024-12-19T08:39:48Z |
| format | Article |
| id | doaj.art-1f03e0db8b334e5cb77745e105a4ddf2 |
| institution | Directory Open Access Journal |
| issn | 2296-2646 |
| language | English |
| last_indexed | 2024-12-19T08:39:48Z |
| publishDate | 2020-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Chemistry |
| spelling | doaj.art-1f03e0db8b334e5cb77745e105a4ddf22022-12-21T20:28:57ZengFrontiers Media S.A.Frontiers in Chemistry2296-26462020-11-01810.3389/fchem.2020.589910589910Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPASZhaolong LuoXinming QinLingyun WanWei HuJinlong YangLinear-scaling density functional theory (DFT) is an efficient method to describe the electronic structures of molecules, semiconductors, and insulators to avoid the high cubic-scaling cost in conventional DFT calculations. Here, we present a parallel implementation of linear-scaling density matrix trace correcting (TC) purification algorithm to solve the Kohn–Sham (KS) equations with the numerical atomic orbitals in the HONPAS package. Such a linear-scaling density matrix purification algorithm is based on the Kohn's nearsightedness principle, resulting in a sparse Hamiltonian matrix with localized basis sets in the DFT calculations. Therefore, sparse matrix multiplication is the most time-consuming step in the density matrix purification algorithm for linear-scaling DFT calculations. We propose to use the MPI_Allgather function for parallel programming to deal with the sparse matrix multiplication within the compressed sparse row (CSR) format, which can scale up to hundreds of processing cores on modern heterogeneous supercomputers. We demonstrate the computational accuracy and efficiency of this parallel density matrix purification algorithm by performing large-scale DFT calculations on boron nitrogen nanotubes containing tens of thousands of atoms.https://www.frontiersin.org/articles/10.3389/fchem.2020.589910/fulllinear-scaling density functional theorydensity matrix purification algorithmsparse matrix multiplicationparallel implementationtens of thousands of atoms |
| spellingShingle | Zhaolong Luo Xinming Qin Lingyun Wan Wei Hu Jinlong Yang Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS Frontiers in Chemistry linear-scaling density functional theory density matrix purification algorithm sparse matrix multiplication parallel implementation tens of thousands of atoms |
| title | Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS |
| title_full | Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS |
| title_fullStr | Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS |
| title_full_unstemmed | Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS |
| title_short | Parallel Implementation of Large-Scale Linear Scaling Density Functional Theory Calculations With Numerical Atomic Orbitals in HONPAS |
| title_sort | parallel implementation of large scale linear scaling density functional theory calculations with numerical atomic orbitals in honpas |
| topic | linear-scaling density functional theory density matrix purification algorithm sparse matrix multiplication parallel implementation tens of thousands of atoms |
| url | https://www.frontiersin.org/articles/10.3389/fchem.2020.589910/full |
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