MPI/OpenMP hybrid parallel algorithm for resolution of identity second‐order Møller–Plesset perturbation calculation of analytical energy gradient for massively parallel multicore supercomputers
A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is presented for geometry optimization calculations and one‐electron property calculations of large mo...
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| Published in | Journal of computational chemistry Vol. 38; no. 8; pp. 489 - 507 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
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United States
Wiley Subscription Services, Inc
30.03.2017
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| Online Access | Get full text |
| ISSN | 0192-8651 1096-987X 1096-987X |
| DOI | 10.1002/jcc.24701 |
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| Abstract | A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is presented for geometry optimization calculations and one‐electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi‐Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two‐dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N5) step such as calculations of occupied–occupied and virtual–virtual blocks of MP2 one‐particle density matrix and MP2 two‐particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C60@C60H28 (144 atoms, 1820 atomic orbitals (AOs) for def2‐SVP basis set, and 3888 AOs for def2‐TZVP), nanographene dimer (C96H24)2 (240 atoms, 2928 AOs for def2‐SVP, and 6432 AOs for cc‐pVTZ), and trp‐cage protein 1L2Y (304 atoms and 2906 AOs for def2‐SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp‐cage protein 1L2Y at the RI‐MP2/def2‐SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc.
A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is developed for geometry optimization calculations of large molecules using supercomputers. The analytical energy gradient calculation of Trp‐cage protein 1L2Y at the RI‐MP2/def2‐SVP level is speeded up considerably enough to perform the geometry optimization using the new algorithm and implementation on the K computer. |
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| AbstractList | A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller-Plesset perturbation (RI-MP2) method from the restricted Hartree-Fock reference is presented for geometry optimization calculations and one-electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two-dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N5 ) step such as calculations of occupied-occupied and virtual-virtual blocks of MP2 one-particle density matrix and MP2 two-particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C60 @C60 H28 (144 atoms, 1820 atomic orbitals (AOs) for def2-SVP basis set, and 3888 AOs for def2-TZVP), nanographene dimer (C96 H24 )2 (240 atoms, 2928 AOs for def2-SVP, and 6432 AOs for cc-pVTZ), and trp-cage protein 1L2Y (304 atoms and 2906 AOs for def2-SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp-cage protein 1L2Y at the RI-MP2/def2-SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc.A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller-Plesset perturbation (RI-MP2) method from the restricted Hartree-Fock reference is presented for geometry optimization calculations and one-electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two-dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N5 ) step such as calculations of occupied-occupied and virtual-virtual blocks of MP2 one-particle density matrix and MP2 two-particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C60 @C60 H28 (144 atoms, 1820 atomic orbitals (AOs) for def2-SVP basis set, and 3888 AOs for def2-TZVP), nanographene dimer (C96 H24 )2 (240 atoms, 2928 AOs for def2-SVP, and 6432 AOs for cc-pVTZ), and trp-cage protein 1L2Y (304 atoms and 2906 AOs for def2-SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp-cage protein 1L2Y at the RI-MP2/def2-SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc. A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Moller-Plesset perturbation (RI-MP2) method from the restricted Hartree-Fock reference is presented for geometry optimization calculations and one-electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two-dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N^sup 5^) step such as calculations of occupied-occupied and virtual-virtual blocks of MP2 one-particle density matrix and MP2 two-particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C^sub 60^@C^sub 60^H^sub 28^ (144 atoms, 1820 atomic orbitals (AOs) for def2-SVP basis set, and 3888 AOs for def2-TZVP), nanographene dimer (C^sub 96^H^sub 24^)^sub 2^ (240 atoms, 2928 AOs for def2-SVP, and 6432 AOs for cc-pVTZ), and trp-cage protein 1L2Y (304 atoms and 2906 AOs for def2-SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp-cage protein 1L2Y at the RI-MP2/def2-SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller-Plesset perturbation (RI-MP2) method from the restricted Hartree-Fock reference is presented for geometry optimization calculations and one-electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi-Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two-dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N ) step such as calculations of occupied-occupied and virtual-virtual blocks of MP2 one-particle density matrix and MP2 two-particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C @C H (144 atoms, 1820 atomic orbitals (AOs) for def2-SVP basis set, and 3888 AOs for def2-TZVP), nanographene dimer (C H ) (240 atoms, 2928 AOs for def2-SVP, and 6432 AOs for cc-pVTZ), and trp-cage protein 1L2Y (304 atoms and 2906 AOs for def2-SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp-cage protein 1L2Y at the RI-MP2/def2-SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc. A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is presented for geometry optimization calculations and one‐electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi‐Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two‐dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O ( N 5 ) step such as calculations of occupied–occupied and virtual–virtual blocks of MP2 one‐particle density matrix and MP2 two‐particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C 60 @C 60 H 28 (144 atoms, 1820 atomic orbitals (AOs) for def2‐SVP basis set, and 3888 AOs for def2‐TZVP), nanographene dimer (C 96 H 24 ) 2 (240 atoms, 2928 AOs for def2‐SVP, and 6432 AOs for cc‐pVTZ), and trp‐cage protein 1L2Y (304 atoms and 2906 AOs for def2‐SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp‐cage protein 1L2Y at the RI‐MP2/def2‐SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc. A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is presented for geometry optimization calculations and one‐electron property calculations of large molecules. This algorithm is designed for massively parallel computation on multicore supercomputers applying the Message Passing Interface (MPI) and Open Multi‐Processing (OpenMP) hybrid parallel programming model. In this algorithm, the two‐dimensional hierarchical MP2 parallelization scheme is applied using a huge number of MPI processes (more than 1000 MPI processes) for acceleration of the computationally demanding O(N5) step such as calculations of occupied–occupied and virtual–virtual blocks of MP2 one‐particle density matrix and MP2 two‐particle density matrices. The new parallel algorithm performance is assessed using test calculations of several large molecules such as buckycatcher C60@C60H28 (144 atoms, 1820 atomic orbitals (AOs) for def2‐SVP basis set, and 3888 AOs for def2‐TZVP), nanographene dimer (C96H24)2 (240 atoms, 2928 AOs for def2‐SVP, and 6432 AOs for cc‐pVTZ), and trp‐cage protein 1L2Y (304 atoms and 2906 AOs for def2‐SVP) using up to 32,768 nodes and 262,144 central processing unit (CPU) cores of the K computer. The results of geometry optimization calculations of trp‐cage protein 1L2Y at the RI‐MP2/def2‐SVP level using the 3072 nodes and 24,576 cores of the K computer are presented and discussed to assess the efficiency of the proposed algorithm. © 2017 Wiley Periodicals, Inc. A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from the restricted Hartree–Fock reference is developed for geometry optimization calculations of large molecules using supercomputers. The analytical energy gradient calculation of Trp‐cage protein 1L2Y at the RI‐MP2/def2‐SVP level is speeded up considerably enough to perform the geometry optimization using the new algorithm and implementation on the K computer. |
| Author | Katouda, Michio Nakajima, Takahito |
| Author_xml | – sequence: 1 givenname: Michio surname: Katouda fullname: Katouda, Michio organization: Computational Molecular Science Research Team, RIKEN Advanced Institute for Computational Science – sequence: 2 givenname: Takahito surname: Nakajima fullname: Nakajima, Takahito email: nakajima@riken.jp organization: Computational Molecular Science Research Team, RIKEN Advanced Institute for Computational Science |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28133838$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1021/ct100199k 10.1109/MCSE.2010.29 10.1090/S0025-5718-1980-0572855-7 10.1063/1.4956454 10.1002/jcc.540030413 10.1016/0009-2614(93)87156-W 10.1021/jp9028968 10.1007/s002140050269 10.1080/00268976.2014.952696 10.1002/jcc.540030314 10.1016/0009-2614(93)89151-7 10.1063/1.2777146 10.1021/jp0776762 10.1007/s00214-007-0295-5 10.1002/wcms.58 10.1021/ct900543q 10.1039/c002859b 10.1016/S0167-8191(00)00020-X 10.1063/1.1445115 10.1039/b515355g 10.1021/ct400795v 10.1002/jcc.540100111 10.1063/1.450106 10.1063/1.1679012 10.1021/ct4002202 10.1002/qua.24563 10.1063/1.455553 10.1002/wcms.1162 10.1016/j.cplett.2006.06.059 10.1016/j.cplett.2006.05.092 10.1063/1.4903269 10.1002/jcc.20604 10.1002/wcms.81 10.1002/jcc.24221 10.1021/acs.jctc.6b00015 10.1063/1.4896235 10.1063/1.456153 10.1021/ct300531w 10.1021/ct100083w 10.1063/1.2712433 10.1002/wcms.1159 10.1039/b508541a 10.1021/jz201697x 10.1002/qua.22068 10.1103/PhysRev.46.618 10.1063/1.447489 10.1063/1.4927325 10.1002/9781119019572 10.1038/nsb798 10.1016/0009-2614(96)00054-1 10.1080/00268976900100941 10.1007/978-1-4684-8541-7_4 10.1002/qua.560120408 10.1007/BF01589116 10.1063/1.438728 10.1063/1.4919238 10.1016/S0009-2614(98)00862-8 10.1002/qua.24860 |
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| Copyright | 2017 Wiley Periodicals, Inc. Copyright Wiley Subscription Services, Inc. Mar 30, 2017 |
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| Keywords | geometry optimization NTChem massively parallel algorithm K computer analytical energy gradient RI-MP2 |
| Language | English |
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| Notes | This work was supported by the Next‐Generation Supercomputer project, FLAGSHIP2020 project (Development of new fundamental technologies for high‐efficiency energy creation, conversion/storage and use), and Kakenhi (Grant‐in‐aid for Young Scientists B: 15K17816 and Grant‐in‐Aid for Scientific Research on Innovative Areas “p‐System Figuration”: 15H01006) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. Calculations were performed on the K computer (RIKEN, Kobe, Japan) and the supercomputer system of Research Center for Computational Science (Institute for Molecular Science, Okazaki, Japan). SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-1 ObjectType-Feature-2 content type line 23 |
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| References | 2010; 12 1989; 45 1993; 208 1973; 58 2015; 143 2016; 145 2009; 113 2002; 116 2016; 37 1979; 71 2013; 9 1977 2007; 28 2014; 4 1986; 84 2000 1997; 97 1980; 35 1982; 3 1996; 250 1988; 89 1993; 213 2008; 112 2010; 6 2007; 126 2007; 127 1984; 81 2011; 1 2002; 9 2000; 26 2011 1934; 46 2006; 8 2006 1969; 17 2008; 120 2014; 114 1998; 294 2016; 12 2014; 113 2012; 2 2006; 427 2012; 3 1989; 10 2015; 115 1989; 90 2005; 7 2016 1977; 12 2006; 426 2014; 141 2009; 109 2012; 8 e_1_2_6_51_1 e_1_2_6_32_1 e_1_2_6_70_1 e_1_2_6_30_1 Weigend F. (e_1_2_6_15_1) 1997; 97 e_1_2_6_19_1 e_1_2_6_13_1 e_1_2_6_36_1 e_1_2_6_59_1 e_1_2_6_11_1 e_1_2_6_34_1 e_1_2_6_17_1 e_1_2_6_55_1 e_1_2_6_38_1 e_1_2_6_57_1 e_1_2_6_62_1 e_1_2_6_64_1 e_1_2_6_43_1 e_1_2_6_20_1 e_1_2_6_41_1 e_1_2_6_60_1 e_1_2_6_9_1 e_1_2_6_5_1 e_1_2_6_7_1 e_1_2_6_24_1 e_1_2_6_49_1 e_1_2_6_3_1 e_1_2_6_22_1 e_1_2_6_66_1 e_1_2_6_28_1 e_1_2_6_45_1 e_1_2_6_26_1 e_1_2_6_47_1 e_1_2_6_68_1 e_1_2_6_52_1 e_1_2_6_54_1 e_1_2_6_10_1 e_1_2_6_31_1 Jensen F. (e_1_2_6_1_1) 2006 e_1_2_6_14_1 e_1_2_6_35_1 Haeser M. (e_1_2_6_53_1) 1989; 10 e_1_2_6_12_1 e_1_2_6_33_1 e_1_2_6_18_1 e_1_2_6_39_1 e_1_2_6_56_1 e_1_2_6_16_1 e_1_2_6_37_1 e_1_2_6_58_1 e_1_2_6_63_1 e_1_2_6_42_1 Yokokawa M. (e_1_2_6_50_1) 2011 e_1_2_6_65_1 e_1_2_6_21_1 e_1_2_6_40_1 e_1_2_6_61_1 e_1_2_6_8_1 e_1_2_6_4_1 e_1_2_6_6_1 e_1_2_6_25_1 e_1_2_6_48_1 e_1_2_6_23_1 e_1_2_6_2_1 e_1_2_6_29_1 e_1_2_6_44_1 e_1_2_6_67_1 e_1_2_6_27_1 e_1_2_6_46_1 e_1_2_6_69_1 |
| References_xml | – volume: 294 start-page: 143 year: 1998 publication-title: Chem. Phys. Lett. – volume: 97 start-page: 331 year: 1997 publication-title: Theor. Chim. Acta – volume: 12 start-page: 683 year: 1977 publication-title: Int. J. Quantum Chem. – volume: 8 start-page: 1159 year: 2006 publication-title: Phys. Chem. Chem. Phys. – volume: 7 start-page: 3297 year: 2005 publication-title: Phys. Chem. Chem. Phys. – volume: 2 start-page: 73 year: 2012 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 58 start-page: 4496 year: 1973 publication-title: J. Chem. Phys. – volume: 17 start-page: 197 year: 1969 publication-title: Mol. Phys. – volume: 84 start-page: 3963 year: 1986 publication-title: J. Chem. Phys. – volume: 141 start-page: 219901 year: 2014 publication-title: J. Chem. Phys. – volume: 6 start-page: 2325 year: 2010 publication-title: J. Chem. Theory Comput. – volume: 426 start-page: 197 year: 2006 publication-title: Chem. Phys. Lett. – volume: 45 start-page: 503 year: 1989 publication-title: Math. Program. – volume: 112 start-page: 2049 year: 2008 publication-title: J. Phys. Chem. A – volume: 109 start-page: 2121 year: 2009 publication-title: Int. J. Quantum Chem. – volume: 208 start-page: 359 year: 1993 publication-title: Chem. Phys. Lett. – volume: 113 start-page: 184 year: 2014 publication-title: Mol. Phys. – volume: 116 start-page: 3175 year: 2002 publication-title: J. Chem. Phys. – volume: 3 start-page: 385 year: 1982 publication-title: J. Comput. Chem. – volume: 90 start-page: 1007 year: 1989 publication-title: J. Chem. Phys. – volume: 9 start-page: 425 year: 2002 publication-title: Nat. Struct. Biol. – volume: 12 start-page: 6896 year: 2010 publication-title: Phys. Chem. Chem. Phys. – volume: 9 start-page: 5373 year: 2013 publication-title: J. Chem. Theory Comput. – volume: 115 start-page: 349 year: 2015 publication-title: Int. J. Quantum Chem. – volume: 145 start-page: 024106 year: 2016 publication-title: J. Chem. Phys. – volume: 46 start-page: 618 year: 1934 publication-title: Phys. Rev. – volume: 8 start-page: 4177 year: 2012 publication-title: J. Chem. Theory Comput. – volume: 143 start-page: 054506 year: 2015 publication-title: J. Chem. Phys. – volume: 3 start-page: 375 year: 2012 publication-title: J. Phys. Chem. Lett. – volume: 250 start-page: 477 year: 1996 publication-title: Chem. Phys. Lett. – volume: 6 start-page: 135 year: 2010 publication-title: J. Chem. Theory Comput. – volume: 35 start-page: 773 year: 1980 publication-title: Math. Comput. – volume: 143 start-page: 102803 year: 2015 publication-title: J. Chem. Phys. – volume: 3 start-page: 556 year: 1982 publication-title: J. Comput. Chem. – volume: 81 start-page: 5031 year: 1984 publication-title: J. Chem. Phys. – volume: 120 start-page: 185 year: 2008 publication-title: Theor. Chem. Acc – volume: 71 start-page: 3396 year: 1979 publication-title: J. Chem. Phys. – volume: 4 start-page: 91 year: 2014 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 89 start-page: 5777 year: 1988 publication-title: J. Chem. Phys. – year: 2000 – start-page: 371 year: 2011 publication-title: ISLPED – volume: 26 start-page: 945 year: 2000 publication-title: Parallel Comput. – year: 2016 – volume: 12 start-page: 40 year: 2010 publication-title: Comput. Sci. Eng. – volume: 141 start-page: 124108 year: 2014 publication-title: J. Chem. Phys. – volume: 10 start-page: 104 year: 1989 publication-title: J. Comput. Chem. – start-page: 153 year: 1977 end-page: 185 – volume: 1 start-page: 509 year: 2011 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 213 start-page: 514 year: 1993 publication-title: Chem. Phys. Lett. – volume: 12 start-page: 2214 year: 2016 publication-title: J. Chem. Theory Comput. – volume: 4 start-page: 15 year: 2014 publication-title: Wiley Interdiscip. Rev. Comput. Mol. Sci. – volume: 37 start-page: 506 year: 2016 publication-title: J. Comput. Chem. – year: 2006 – volume: 126 start-page: 124115 year: 2007 publication-title: J. Chem. Phys. – volume: 28 start-page: 839 year: 2007 publication-title: J. Comput. Chem. – volume: 9 start-page: 2654 year: 2013 publication-title: J. Chem. Theory Comput. – volume: 113 start-page: 11856 year: 2009 publication-title: J. Phys. Chem. A – volume: 114 start-page: 321 year: 2014 publication-title: Int. J. Quantum Chem. – volume: 127 start-page: 114107 year: 2007 publication-title: J. Chem. Phys. – volume: 6 start-page: 1075 year: 2010 publication-title: J. Chem. Theory Comput. – volume: 427 start-page: 225 year: 2006 publication-title: Chem. Phys. Lett. – ident: e_1_2_6_23_1 doi: 10.1021/ct100199k – ident: e_1_2_6_41_1 doi: 10.1109/MCSE.2010.29 – ident: e_1_2_6_68_1 doi: 10.1090/S0025-5718-1980-0572855-7 – ident: e_1_2_6_27_1 doi: 10.1063/1.4956454 – ident: e_1_2_6_57_1 doi: 10.1002/jcc.540030413 – ident: e_1_2_6_28_1 – ident: e_1_2_6_9_1 doi: 10.1016/0009-2614(93)87156-W – ident: e_1_2_6_59_1 – ident: e_1_2_6_36_1 – ident: e_1_2_6_66_1 doi: 10.1021/jp9028968 – volume: 97 start-page: 331 year: 1997 ident: e_1_2_6_15_1 publication-title: Theor. Chim. Acta doi: 10.1007/s002140050269 – ident: e_1_2_6_31_1 doi: 10.1080/00268976.2014.952696 – ident: e_1_2_6_52_1 doi: 10.1002/jcc.540030314 – ident: e_1_2_6_5_1 doi: 10.1016/0009-2614(93)89151-7 – ident: e_1_2_6_12_1 doi: 10.1063/1.2777146 – ident: e_1_2_6_39_1 doi: 10.1021/jp0776762 – ident: e_1_2_6_58_1 doi: 10.1007/s00214-007-0295-5 – ident: e_1_2_6_4_1 doi: 10.1002/wcms.58 – ident: e_1_2_6_40_1 doi: 10.1021/ct900543q – ident: e_1_2_6_38_1 doi: 10.1039/c002859b – ident: e_1_2_6_44_1 doi: 10.1016/S0167-8191(00)00020-X – ident: e_1_2_6_55_1 – ident: e_1_2_6_70_1 doi: 10.1063/1.1445115 – ident: e_1_2_6_16_1 doi: 10.1039/b515355g – ident: e_1_2_6_46_1 doi: 10.1021/ct400795v – volume: 10 start-page: 104 year: 1989 ident: e_1_2_6_53_1 publication-title: J. Comput. Chem. doi: 10.1002/jcc.540100111 – ident: e_1_2_6_62_1 doi: 10.1063/1.450106 – ident: e_1_2_6_7_1 doi: 10.1063/1.1679012 – ident: e_1_2_6_43_1 doi: 10.1021/ct4002202 – ident: e_1_2_6_22_1 doi: 10.1002/qua.24563 – ident: e_1_2_6_61_1 doi: 10.1063/1.455553 – ident: e_1_2_6_29_1 doi: 10.1002/wcms.1162 – ident: e_1_2_6_34_1 – ident: e_1_2_6_11_1 doi: 10.1016/j.cplett.2006.06.059 – ident: e_1_2_6_17_1 doi: 10.1016/j.cplett.2006.05.092 – ident: e_1_2_6_21_1 doi: 10.1063/1.4903269 – ident: e_1_2_6_18_1 doi: 10.1002/jcc.20604 – ident: e_1_2_6_35_1 doi: 10.1002/wcms.81 – ident: e_1_2_6_48_1 – ident: e_1_2_6_33_1 doi: 10.1002/jcc.24221 – ident: e_1_2_6_26_1 doi: 10.1021/acs.jctc.6b00015 – ident: e_1_2_6_20_1 doi: 10.1063/1.4896235 – ident: e_1_2_6_69_1 doi: 10.1063/1.456153 – ident: e_1_2_6_42_1 doi: 10.1021/ct300531w – ident: e_1_2_6_60_1 – ident: e_1_2_6_56_1 doi: 10.1021/ct100083w – ident: e_1_2_6_19_1 doi: 10.1063/1.2712433 – ident: e_1_2_6_37_1 doi: 10.1002/wcms.1159 – ident: e_1_2_6_63_1 doi: 10.1039/b508541a – ident: e_1_2_6_24_1 doi: 10.1021/jz201697x – start-page: 371 year: 2011 ident: e_1_2_6_50_1 publication-title: ISLPED – ident: e_1_2_6_45_1 doi: 10.1002/qua.22068 – ident: e_1_2_6_3_1 doi: 10.1103/PhysRev.46.618 – ident: e_1_2_6_51_1 doi: 10.1063/1.447489 – volume-title: Introduction to Computational Chemistry year: 2006 ident: e_1_2_6_1_1 – ident: e_1_2_6_47_1 doi: 10.1063/1.4927325 – ident: e_1_2_6_30_1 – ident: e_1_2_6_2_1 doi: 10.1002/9781119019572 – ident: e_1_2_6_65_1 doi: 10.1038/nsb798 – ident: e_1_2_6_10_1 doi: 10.1016/0009-2614(96)00054-1 – ident: e_1_2_6_13_1 doi: 10.1080/00268976900100941 – ident: e_1_2_6_32_1 – ident: e_1_2_6_14_1 doi: 10.1007/978-1-4684-8541-7_4 – ident: e_1_2_6_8_1 doi: 10.1002/qua.560120408 – ident: e_1_2_6_67_1 doi: 10.1007/BF01589116 – ident: e_1_2_6_54_1 – ident: e_1_2_6_6_1 doi: 10.1063/1.438728 – ident: e_1_2_6_25_1 doi: 10.1063/1.4919238 – ident: e_1_2_6_64_1 doi: 10.1016/S0009-2614(98)00862-8 – ident: e_1_2_6_49_1 doi: 10.1002/qua.24860 |
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| Snippet | A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller–Plesset perturbation (RI‐MP2) method from... A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Møller-Plesset perturbation (RI-MP2) method from... A massively parallel algorithm of the analytical energy gradient calculations based the resolution of identity Moller-Plesset perturbation (RI-MP2) method from... |
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| SubjectTerms | Algorithms analytical energy gradient Energy geometry optimization K computer massively parallel algorithm Matrix NTChem Optimization RI‐MP2 Supercomputers |
| Title | MPI/OpenMP hybrid parallel algorithm for resolution of identity second‐order Møller–Plesset perturbation calculation of analytical energy gradient for massively parallel multicore supercomputers |
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