A comprehensive study of popular eigenvalue methods employed for quantum calculation of energy eigenstates in nanostructures using GPUs
In this work, we concentrate on the graphics processing unit (GPU) implementation of three different methods that are common among peers in the electronic computational domain. We calculate the energy eigenstates of GaN/AlGaN quantum dots on GPU using the tight-binding approach with a s p 3 d 5 s ∗...
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| Veröffentlicht in: | Journal of computational electronics 2015-06, Vol.14 (2), p.593-603 |
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| container_title | Journal of computational electronics |
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| creator | Rodrigues, W. Pecchia, A. Auf der Maur, M. Di Carlo, A. |
| description | In this work, we concentrate on the graphics processing unit (GPU) implementation of three different methods that are common among peers in the electronic computational domain. We calculate the energy eigenstates of GaN/AlGaN quantum dots on GPU using the tight-binding approach with a
s
p
3
d
5
s
∗
+ spin-orbit parametrization for structures ranging from 8039 atoms to 351,600 atoms corresponding to a Hamiltonian matrix size of around 160,780–7,032,000. We perform an analysis for timing, memory occupancy and convergence on a multi-GPU workstation and a high performance computing (HPC) cluster. We also present comparisons between the multi-GPU system having 4 Nvidia Kepler graphic cards and a HPC cluster where the algorithms are benchmarked on up to 256 CPU cores. |
| doi_str_mv | 10.1007/s10825-015-0695-z |
| format | Article |
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3
d
5
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+ spin-orbit parametrization for structures ranging from 8039 atoms to 351,600 atoms corresponding to a Hamiltonian matrix size of around 160,780–7,032,000. We perform an analysis for timing, memory occupancy and convergence on a multi-GPU workstation and a high performance computing (HPC) cluster. We also present comparisons between the multi-GPU system having 4 Nvidia Kepler graphic cards and a HPC cluster where the algorithms are benchmarked on up to 256 CPU cores.</description><subject>Algorithms</subject><subject>Aluminum</subject><subject>Aluminum gallium nitrides</subject><subject>Clusters</subject><subject>Eigenvalues</subject><subject>Eigenvectors</subject><subject>Electrical Engineering</subject><subject>Energy</subject><subject>Engineering</subject><subject>Graphics processing units</subject><subject>Hamiltonian functions</subject><subject>Mathematical analysis</subject><subject>Mathematical and Computational Engineering</subject><subject>Mathematical and Computational Physics</subject><subject>Mechanical Engineering</subject><subject>Methods</subject><subject>Optical and Electronic Materials</subject><subject>Parameterization</subject><subject>Quantum dots</subject><subject>Sparsity</subject><subject>Spectrum allocation</subject><subject>Theoretical</subject><subject>Workstations</subject><issn>1569-8025</issn><issn>1572-8137</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp1kMtqwzAQRU1poWnaD-hO0LXbkeSXliH0BYF20ayFIsuOgy05egScH-hv18aFrroYZhjumYETRfcYHjFA_uQwFCSNAY-VsTQ-X0QLnOYkLjDNL6c5Y3EBJL2Obpw7ABAgCV5E3yskTddbtVfaNSeFnA_lgEyFetOHVlikmlrpk2iDQp3ye1M6pLq-NYMqUWUsOgahfeiQFK0cAd8YPeFKK1sPM-288MqhRiMttHHeBumDHTfBNbpGr59bdxtdVaJ16u63L6Pty_PX-i3efLy-r1ebWNIi8zGBnJTZrpCQlFVGpMxJykoJUMlclFiyAnYMiCRUpCUViUgTVlQ7hikllaRAl9HDfLe35hiU8_xggtXjS04YLkgOjKVjCs8paY1zVlW8t00n7MAx8Mk3n33z0TeffPPzyJCZcWNW18r-Xf4f-gEybIcG</recordid><startdate>20150601</startdate><enddate>20150601</enddate><creator>Rodrigues, W.</creator><creator>Pecchia, A.</creator><creator>Auf der Maur, M.</creator><creator>Di Carlo, A.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope></search><sort><creationdate>20150601</creationdate><title>A comprehensive study of popular eigenvalue methods employed for quantum calculation of energy eigenstates in nanostructures using GPUs</title><author>Rodrigues, W. ; 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| ispartof | Journal of computational electronics, 2015-06, Vol.14 (2), p.593-603 |
| issn | 1569-8025 1572-8137 |
| language | eng |
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| source | SpringerNature Journals; ProQuest Central UK/Ireland; ProQuest Central |
| subjects | Algorithms Aluminum Aluminum gallium nitrides Clusters Eigenvalues Eigenvectors Electrical Engineering Energy Engineering Graphics processing units Hamiltonian functions Mathematical analysis Mathematical and Computational Engineering Mathematical and Computational Physics Mechanical Engineering Methods Optical and Electronic Materials Parameterization Quantum dots Sparsity Spectrum allocation Theoretical Workstations |
| title | A comprehensive study of popular eigenvalue methods employed for quantum calculation of energy eigenstates in nanostructures using GPUs |
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