OpenMPI环境下的VASP软件的并行与进程
The Parallel and Progress of VASP in OpenMPI Environment
DOI: 10.12677/CSA.2021.111010, PDF,    国家科技经费支持
作者: 潘 勇, 赵琉涛, 刘 彤：北京市计算中心，北京；史海洪：北京北科融智云计算科技有限公司，北京
关键词: VASP；矩阵对角化；MPI；跨节点并行；VASP； Matrix Diagonalization； MPI； Inter-Node Parallelism
摘要: VASP是材料微观物性模拟的重要软件，并行性能优良，其中矩阵对角化在其运算过程中占据大量时间。在HPC作业调度下，VASP并行的进程分配表现单节点和跨节点的差异。通过对MPI并行的节点内和节点间进程、EDDIAG和RMM-DIIS运行时间分析，明确了VASP程序并行中，矩阵对角化的重要性和OpenMPI对矩阵对角化并行的作用。并编写了一个通讯优化的矩阵对角化程序，通过测试并行程序托比串行的马斯算法表现出强大的可扩展性，基于OpenMPI的并行程序能够极大提高对角化的并行效率，但其并行度、通讯代价与计算规模之间的关系需要进一步研究。
Abstract: VASP is an important software for material microscopic physical properties simulation, with excellent parallel performance, among which matrix diagonalization takes up a lot of time in its calculation process. Under HPC job scheduling, the parallel process allocation of VASP shows the difference between the single node and across multiple nodes. Through the analysis of the intra-node and internode processes of MPI parallel, the running time of EDDIAG and RMM-DIIS, we clarified the im-portance of matrix diagonalization in VASP program parallelism and the role of OpenMPI on matrix diagonalization parallelism. Furthermore, we write a communication-optimized matrix diagonalization program. By test, the parallel program using the Thomas algorithm shows strong scalability than the serial algorithm. The OpenMPI-based parallel program can significantly improve diago-nalization’s parallel efficiency, but the relationship between its parallelism, communication cost, and computing scale needs further study.
文章引用：潘勇, 史海洪, 赵琉涛, 刘彤. OpenMPI环境下的VASP软件的并行与进程[J]. 计算机科学与应用, 2021, 11(1): 84-94. https://doi.org/10.12677/CSA.2021.111010

参考文献

[1] Thijssen, J.M. (2007) Computational Physics. 2nd Edition, Cambridge University Press, Cambridge, England.
[2] Ong, S.P., Chlia, S., Jain, A., Brafman, M., Gunter, D., Ceder, G. and Persson, K.A. (2015) The Materials Application Programming In-terface (API): A Simple, Flexible and Efficient API for Materials Data Based on REpresentational State Transfer (REST) Princi-ples. Computational Materials Science, 97, 209R. [Google Scholar] [CrossRef] 
[3] Potyrailo, A. Chisholm, B.J., Morris, W.G., Cawse, J.N., Flanagan, W.P., Hassib, L., Molaison, C.A., Ezbiansky, K., Medford, G. and Reitz, H. (2003) Development of Combinatorial Chemistry Methods for Coatings:  High-Throughput Adhesion Evaluation and Scale-Up of Combinatorial Leads. Journal of Computational Chemistry, 5, 472. [Google Scholar] [CrossRef] [PubMed]
[4] Potyrailo, R.A. and Takeuchi, I. (2005) Role of High-Throughput Characteriza-tion Tools in Combinatorial Materials Science. Materials Science and Technology, 16, 1. [Google Scholar] [CrossRef] 
[5] Persson, K. (2020) Materials Project. http://www.materialsgenome.org
[6] Setyawan, W. and Curtarolo, S. (2010) High-Throughput Electronic Band Structure Calculations: Challenges and Tools. Computational Materials Science, 49, 299. [Google Scholar] [CrossRef] 
[7] Jain, A. and Hautier, G., Moore, C.J., Ong, S.P., Fischer, C.C., Mueller, T., Persson, K.A. and Ceder, G. (2011) A High-Throughput Infrastructure for Density Functional Theory Calculations. Computational Materials Science, 50, 2295. [Google Scholar] [CrossRef] 
[8] Yang, X., Wang, Z., Zhao, X., Song, J., Zhang, M. and Liu, H. (2018) MatCloud: A High-Throughput Computational Infrastructure for Integrated Management of Materials Simulation, Data and Resources. Computational Materials Science, 146, 319. [Google Scholar] [CrossRef] 
[9] Kresse, G., Marsman, M. and Furthmüller, J. (2016) VASP the Guide. http://cms.mpi.univie.ac.at/VASP/
[10] Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G.L., Cococcioni, M., Dabo, I., Dal Corso, A., Fabris, S., Fratesi, G., de Gironcoli, S., Gebauer, R., Gerstmann, U., Gougoussis, C., Kokalj, A., Lazzeri, M., Martin-Samos, L., Marzari, N., Mauri, F., Mazzarello, R., Paolini, S., Pasquarello, A., Paulatto, L., Sbraccia, C., Scandolo, S., Sclauzero, G., Seitsonen, A.P., Smogunov, A., Umari P. and Wentzcovitch, R.M. (2009) QUANTUM ESPRESSO: A Modular and Open-Source Software Project for Quantum Simulations of Materials. Journal of Physics: Condensed Matter,21, Article ID: 395502. [Google Scholar] [CrossRef] [PubMed]
[11] Jia, W., Fu, J., Cao, Z., Wang, L., Chi, X., Gao, W. and Wang, L.-W. (2013) Fast Plane Wave Density Functional Theory Molecular Dynamics Calculations on Multi-GPU Machines. Journal of Computational Physics, 251, 102. [Google Scholar] [CrossRef] 
[12] Hohenberg, P. and Kohn, W. (1964) Inhomogeneous Electron Gas. Physi-cal Review, 136, B864. [Google Scholar] [CrossRef] 
[13] Kohn, W. and Sham, L.J. (1965) Self-Consistent Equations Including Exchange and Correlation Effect. Physical Review, 140, A1133. [Google Scholar] [CrossRef] 
[14] Kresse, G. and Hafner, J. (1994) Norm-Conserving and Ultrasoft Pseudopotentials for First-Row and Transition Elements. Journal of Physics: Condensed Matter, 6, 8245. [Google Scholar] [CrossRef] 
[15] Kresse, G. and Furthmüller, J. (1996) Efficiency of Ab-Initio Tottal Energy Calculations for Metals and Semiconductors Using a Plane-Wave Basis Set. Com-putational Materials Science, 6, 15. [Google Scholar] [CrossRef] 
[16] Kresse, G. and Furthmüller, J. (1996) Efficient Iterative Schemes for Ab Initio Total-Energy Calculations Using a Plane-Wave Basis Set. Physical Review, 54, Article ID: 11169. [Google Scholar] [CrossRef] 
[17] Blöchl, P.E. (1994) Projector Augmented-Wave Method. Physical Re-view B, 50, Article ID: 17953. [Google Scholar] [CrossRef] 
[18] Kresse, G. and Joubert, D. (1999) From Ultrasoft Pseudopotentials to the Projector Augmented-Wave Method. Physical Review B, 59, 1758. [Google Scholar] [CrossRef]