Accelerating Resolution-of-the-Identity Second-Order Møller−Plesset Quantum Chemistry Calculations with Graphical Processing Units

Vogt, Leslie; Olivares‐Amaya, Roberto; Kermes, Sean; Shao, Yihan; Amador‐Bedolla, Carlos; Aspuru‐Guzik, Alán

doi:10.1021/jp0776762

Cited by 146 publications

(159 citation statements)

References 18 publications

(41 reference statements)

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“…15 Previously we showed that step 4, the formation of the approximate MO integrals, was by far the most expensive operation for medium to large-sized systems, and requires the matrix multiplication…”

Section: Gpu Acceleration Of Ri-mp2mentioning

confidence: 99%

“…15 Concerning the batching over occupied orbitals, as discussed in section 4.2, only the step 4 matrices were batched. For taxol, the batch size was 7, as before.…”

Section: Ri-mp2 Acceleration Benchmarksmentioning

confidence: 99%

“…5 For computational chemistry, GPUs are emerging as an extremely promising architecture for molecular dynamics simulations, 6,7 quantum Monte Carlo, 8 density-functional theory and self-consistent field calculations [9][10][11][12][13][14] and correlated quantum chemistry 15 methods. Efficiency gains of between one and three orders of magnitude using NVIDIA graphics cards have been reported compared to conventional implementations on a CPU.…”

Section: Introductionmentioning

confidence: 99%

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Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

Olivares‐Amaya

Watson

Edgar

et al. 2009

J. Chem. Theory Comput.

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103

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Two new tools for the acceleration of computational chemistry codes using graphical processing units (GPUs) are presented. First, we propose a general black-box approach for the efficient GPU acceleration of matrix−matrix multiplications where the matrix size is too large for the whole computation to be held in the GPU’s onboard memory. Second, we show how to improve the accuracy of matrix multiplications when using only single-precision GPU devices by proposing a heterogeneous computing model, whereby single- and double-precision operations are evaluated in a mixed fashion on the GPU and central processing unit, respectively. The utility of the library is illustrated for quantum chemistry with application to the acceleration of resolution-of-the-identity second-order Møller−Plesset perturbation theory calculations for molecules, which we were previously unable to treat. In particular, for the 168-atom valinomycin molecule in a cc-pVDZ basis set, we observed speedups of 13.8, 7.8, and 10.1 times for single-, double- and mixed-precision general matrix multiply (SGEMM, DGEMM, and MGEMM), respectively. The corresponding errors in the correlation energy were reduced from −10.0 to −1.2 kcal mol−1 for SGEMM and MGEMM, respectively, while higher accuracy can be easily achieved with a different choice of cutoff parameter.

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Section: Gpu Acceleration Of Ri-mp2mentioning

confidence: 99%

“…15 Concerning the batching over occupied orbitals, as discussed in section 4.2, only the step 4 matrices were batched. For taxol, the batch size was 7, as before.…”

Section: Ri-mp2 Acceleration Benchmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

Olivares‐Amaya

Watson

Edgar

et al. 2009

J. Chem. Theory Comput.

Self Cite

103

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“…4 Graphical processing units (GPUs), which are characterized as stream processors, 12 are especially suitable for parallel computing involving massive data and numerous groups have explored their use for electronic structure theory. [13][14][15][16][17][18][19][20][21][22][23][24] Implementation of gas phase ab initio molecular calculations [19][20][21] on GPUs led to greatly enhanced performance for large systems. [25][26] Here, we harness the advances 27 of stream processors to accelerate the computation of implicit solvent effects, effectively reducing the cost of PCM calculations.…”

Section: Introductionmentioning

confidence: 99%

Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models

Liu

Luehr

Kulik

et al. 2015

J. Chem. Theory Comput.

121

138

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Abstract:The conductor-like polarization model (C-PCM) with switching/Gaussian smooth discretization is a widely used implicit solvation model in chemical simulations. However, its application in quantum mechanical calculations of large-scale biomolecular systems can be limited by computational expense of both the gas phase electronic structure and the solvation interaction. We have previously used graphical processing units (GPUs) to accelerate the first of these steps. Here, we extend the use of GPUs to accelerate electronic structure calculations including C-PCM solvation. Implementation on the GPU leads to significant acceleration of the generation of the required integrals for C-PCM. We further propose two strategies to improve the solution of the required linear equations: a dynamic convergence threshold and a randomized block-Jacobi preconditioner. These strategies are not specific to GPUs and are expected to be beneficial for both CPU and GPU implementations. We benchmark the performance of the new implementation using over 20 small proteins in solvent environment. Using a single GPU, our method evaluates the C-PCM related integrals and their derivatives more than 10X faster than a conventional CPUbased implementation. Our improvements to the linear solver provide a further 3X acceleration. The overall calculations including C-PCM solvation require typically 20-40% more effort than their gas phase counterparts for moderate basis set and molecule surface discretization level. The relative cost of the C-PCM solvation correction decreases as the basis sets and/or cavity radii increase. Therefore description of solvation with this model should be routine. We also discuss applications to the study of the conformational landscape of an amyloid fibril.Liu, Luehr, Kulik, and Martínez -GPU-based PCM Calculations -Page 2 IntroductionModeling the influence of solvent in quantum chemical calculations is of great importance to understanding solvation effects on electronic properties, nuclear distributions, spectroscopic properties, acidity/basicity, and mechanisms of enzymatic and chemical reactions.

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“…Despite these exciting developments in basic theory, fast numerical algorithms, and novel hardware utilization, [1][2][3][4][5] it is not yet possible to routinely simulate such large systems using readily available computer resources. However, emerging nano-scale simulation challenges involving, for example, large biomolecular aggregates or nanotechnology devices are increasing the demand for firstprinciples methods that can deliver this performance.…”

Section: Introductionmentioning

confidence: 99%

Electronic structure calculations in arbitrary electrostatic environments

Watson

Rappoport

Lee

et al. 2012

The Journal of Chemical Physics

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Modeling of electronic structure of molecules in electrostatic environments is of considerable relevance for surface-enhanced spectroscopy and molecular electronics. We have developed and implemented a novel approach to the molecular electronic structure in arbitrary electrostatic environments that is compatible with standard quantum chemical methods and can be applied to mediumsized and large molecules. The scheme denoted CheESE (chemistry in electrostatic environments) is based on the description of molecular electronic structure subject to a boundary condition on the system/environment interface. Thus, it is particularly suited to study molecules on metallic surfaces. The proposed model is capable of describing both electrostatic effects near nanostructured metallic surfaces and image-charge effects. We present an implementation of the CheESE model as a library module and show example applications to neutral and negatively charged molecules.

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Accelerating Resolution-of-the-Identity Second-Order Møller−Plesset Quantum Chemistry Calculations with Graphical Processing Units

Abstract: graphics card. Furthermore, speedups of other matrix algebra based electronic structure calculations are anticipated as a result of using a similar approach.

Cited by 146 publications

References 18 publications

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

Accelerating Correlated Quantum Chemistry Calculations Using Graphical Processing Units and a Mixed Precision Matrix Multiplication Library

Quantum Chemistry for Solvated Molecules on Graphical Processing Units Using Polarizable Continuum Models

Electronic structure calculations in arbitrary electrostatic environments

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