Multithreaded parallelization of the energy and analytic gradient in the fragment molecular orbital method

Mironov, Vladimir; Alexeev, Yuri; Fedorov, Dmitri G.

doi:10.1002/qua.25937

Cited by 12 publications

(12 citation statements)

References 90 publications

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“…Bottlenecks in the RI-G3 method, including the HF/6-31G(d) gradient, RI-MP2/6-31G(d) gradient, RI-CCSD(T)/6-31G(d), and RI-MP2/G3L single point energy calculations have been parallelized using the hybrid distributed/shared memory models based on MPI and OpenMP API. In practice, one MPI rank is created on each compute node or socket, which then spawns a team of threads for the actual computation.…”

Section: Mpi/openmp Ri-g3/pdg Methodsmentioning

confidence: 99%

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PDG: A Composite Method Based on the Resolution of the Identity

Pham¹,

Datta²,

Gordon³

2021

J. Phys. Chem. A

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The Gaussian-3 (G3) composite approach for thermochemical properties is revisited in light of the enhanced computational efficiency and reduced memory costs by applying the resolution-of-the-identity (RI) approximation for two-electron repulsion integrals (ERIs) to the computationally demanding component methods in the G3 model: the energy and gradient computations via the second-order Møller–Plesset perturbation theory (MP2) and the energy computations using the coupled-cluster singles–doubles method augmented with noniterative triples corrections [CCSD(T)]. Efficient implementation of the RI-based methods is achieved by employing a hybrid distributed/shared memory model based on MPI and OpenMP. The new variant of the G3 composite approach based on the RI approximation is termed the RI-G3 scheme, or alternatively the PDG method. The accuracy of the new RI-G3/PDG scheme is compared to the “standard” G3 composite approach that employs the memory-expensive four-center ERIs in the MP2 and CCSD(T) calculations. Taking the computation of the heats of formation of the closed-shell molecules in the G3/99 test set as a test case, it is demonstrated that the RI approximation introduces negligible changes to the mean absolute errors relative to the standard G3 model (less than 0.1 kcal/mol), while the standard deviations remain unaltered. The efficiency and memory requirements for the RI-MP2 and RI-CCSD(T) methods are compared to the standard MP2 and CCSD(T) approaches, respectively. The hybrid MPI/OpenMP-based RI-MP2 energy plus gradient computation is found to attain a 7.5× speedup over the standard MP2 calculations. For the most demanding CCSD(T) calculations, the application of the RI approximation is found to nearly halve the memory demand, confer about a 4–5× speedup for the CCSD iterations, and reduce the computational time for the compute-intensive triples correction step by several hours.

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Section: Mpi/openmp Ri-g3/pdg Methodsmentioning

confidence: 99%

“…These new algorithms have been demonstrated to significantly increase the performance and the applicability domain of these electron correlation methods. In addition, the HF energy and gradient methods in GAMESS have also been threaded, thereby attaining significant speedups. , …”

Section: Introductionmentioning

confidence: 99%

PDG: A Composite Method Based on the Resolution of the Identity

Pham¹,

Datta²,

Gordon³

2021

J. Phys. Chem. A

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“…Lyakh introduces abstract that promise to increase performance portability and hide from the scientist some of the complexity of programming current heterogeneous high‐performance computer designs. Mironov et al describe the OpenMP+MPI hybrid parallelization of the Fragment Molecular Orbital (FMO) method in GAMESS and its excellent performance on current NSF and DOE supercomputers. Peng et al present their massively parallel implementation of CCSD(T) within MPQC and demonstrate excellent performance on a wide range of hardware, including hybrid systems.…”

Section: Research Resourcesmentioning

confidence: 99%

Special Issue on Emerging Architectures in Computational Chemistry

Alexeev

Harrison

2019

Int J of Quantum Chemistry

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“…The fragment molecular orbital (FMO) method [28][29][30][31][32] is a fragmentation approach for which analytic first [33,34] and second derivatives [35,36] have been developed. FMO has been extensively used for analyzing protein-ligand interactions [37][38][39] and properties of large systems, [40,41] taking advantage of fragmentation for acceleration [42] and analysis. The cost of geometry optimizations for flexible systems is high because of the large number of degrees of freedom.…”

Section: Introductionmentioning

confidence: 99%