Parallel Electronic Structure Calculations Using Multiple Graphics Processing Units (GPUs)

Hakala, Samuli; Havu, Ville; Enkovaara, Jussi; Nieminen, Risto M.

doi:10.1007/978-3-642-36803-5_4

Cited by 19 publications

(14 citation statements)

References 23 publications

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“…We have used GPUs to speed up most of the computationally intensive parts of the code: solving the Poisson equation, iterative refinement of the eigenvectors, subspace diagonalization and orthonormalization of the wave functions. Overall, we have achieved speedups of up to 15 on large systems and a good parallel scalability with up to 200 GPUs using NVIDIA Tesla M2070 cards [62].…”

Section: Density-functional Theorymentioning

confidence: 93%

Computational Physics on Graphics Processing Units

Harju

Siro

Canova

et al. 2013

Applied Parallel and Scientific Computing

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The use of graphics processing units for scientific computations is an emerging strategy that can significantly speed up various different algorithms. In this review, we discuss advances made in the field of computational physics, focusing on classical molecular dynamics, and on quantum simulations for electronic structure calculations using the density functional theory, wave function techniques, and quantum field theory.Comment: Proceedings of the 11th International Conference, PARA 2012, Helsinki, Finland, June 10-13, 201

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Section: Density-functional Theorymentioning

confidence: 93%

Computational Physics on Graphics Processing Units

Harju

Siro

Canova

et al. 2013

Applied Parallel and Scientific Computing

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“…DFT methods based on all broad classes identified above have been adapted for GPU-based hardware, including real-space grids [68,69], plane-waves [70,71], and GTOs [72,73,74].…”

Section: Feasibility Of Large-scale Simulationsmentioning

confidence: 99%

Applications of large-scale density functional theory in biology

Cole

Hine

2016

J. Phys.: Condens. Matter

139

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Abstract. Density functional theory (DFT) has become a routine tool for the computation of electronic structure in the physics, materials and chemistry fields. Yet the application of traditional DFT to problems in the biological sciences is hindered, to a large extent, by the unfavourable scaling of the computational effort with system size. Here, we review some of the major software and functionality advances that enable insightful electronic structure calculations to be performed on systems comprising many thousands of atoms. We describe some of the early applications of large-scale DFT to the computation of the electronic properties and structure of biomolecules, as well as to paradigmatic problems in enzymology, metalloproteins, photosynthesis and computer-aided drug design. With this review, we hope to demonstrate that first principles modelling of biological structure-function relationships are approaching a reality.

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“…[5][6][7][8][9][10][11][12]). Much work has gone into developing the methods used by such codes, and ongoing optimization of the underlying algorithms is essential to keep up with the possibilities offered by new computer architectures [13][14][15][16]. matrix operations can be minimized by careful consideration of the line search algorithm (Sec.…”

Section: Introductionmentioning

confidence: 99%

The orbital minimization method for electronic structure calculations with finite-range atomic basis sets

Corsetti

2014

Computer Physics Communications

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The implementation of the orbital minimization method (OMM) for solving the selfconsistent Kohn-Sham (KS) problem for electronic structure calculations in a basis of non-orthogonal numerical atomic orbitals of finite-range is reported. We explore the possibilities for using the OMM as an exact cubic-scaling solver for the KS problem, and compare its performance with that of explicit diagonalization in realistic systems. We analyze the efficiency of the method depending on the choice of line search algorithm and on two free parameters, the scale of the kinetic energy preconditioning and the eigenspectrum shift. The results of several timing tests are then discussed, showing that the OMM can achieve a noticeable speedup with respect to diagonalization even for minimal basis sets for which the number of occupied eigenstates represents a significant fraction of the total basis size (> 15%). We investigate the hard and soft parallel scaling of the method on multiple cores, finding a performance equal to or better than diagonalization depending on the details of the OMM implementation. Finally, we discuss the possibility of making use of the natural sparsity of the operator matrices for this type of basis, leading to a method that scales linearly with basis size.

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Parallel Electronic Structure Calculations Using Multiple Graphics Processing Units (GPUs)

Cited by 19 publications

References 23 publications

Computational Physics on Graphics Processing Units

Computational Physics on Graphics Processing Units

Applications of large-scale density functional theory in biology

The orbital minimization method for electronic structure calculations with finite-range atomic basis sets

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