Quantum Espresso Intro Student Cluster Competition
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GPAW, Gpus, and LUMI
GPAW, GPUs, and LUMI Martti Louhivuori, CSC - IT Center for Science Jussi Enkovaara GPAW 2021: Users and Developers Meeting, 2021-06-01 Outline LUMI supercomputer Brief history of GPAW with GPUs GPUs and DFT Current status Roadmap LUMI - EuroHPC system of the North Pre-exascale system with AMD CPUs and GPUs ~ 550 Pflop/s performance Half of the resources dedicated to consortium members Programming for LUMI Finland, Belgium, Czechia, MPI between nodes / GPUs Denmark, Estonia, Iceland, HIP and OpenMP for GPUs Norway, Poland, Sweden, and how to use Python with AMD Switzerland GPUs? https://www.lumi-supercomputer.eu GPAW and GPUs: history (1/2) Early proof-of-concept implementation for NVIDIA GPUs in 2012 ground state DFT and real-time TD-DFT with finite-difference basis separate version for RPA with plane-waves Hakala et al. in "Electronic Structure Calculations on Graphics Processing Units", Wiley (2016), https://doi.org/10.1002/9781118670712 PyCUDA, cuBLAS, cuFFT, custom CUDA kernels Promising performance with factor of 4-8 speedup in best cases (CPU node vs. GPU node) GPAW and GPUs: history (2/2) Code base diverged from the main branch quite a bit proof-of-concept implementation had lots of quick and dirty hacks fixes and features were pulled from other branches and patches no proper unit tests for GPU functionality active development stopped soon after publications Before development re-started, code didn't even work anymore on modern GPUs without applying a few small patches Lesson learned: try to always get new functionality to the -
Free and Open Source Software for Computational Chemistry Education
Free and Open Source Software for Computational Chemistry Education Susi Lehtola∗,y and Antti J. Karttunenz yMolecular Sciences Software Institute, Blacksburg, Virginia 24061, United States zDepartment of Chemistry and Materials Science, Aalto University, Espoo, Finland E-mail: [email protected].fi Abstract Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. We point out the existence of a variety of free and open source software (FOSS) packages for computational chemistry that offer a wide range of functionality all the way from approximate semiempirical calculations with tight- binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. By their very definition, FOSS packages allow usage for whatever purpose by anyone, meaning they can also be used in industrial applications without limitation. Also, FOSS software has no limitations to redistribution in source or binary form, allowing their easy distribution and installation by third parties. Many FOSS scientific software packages are available as part of popular Linux distributions, and other package managers such as pip and conda. Combined with the remarkable increase in the power of personal devices—which rival that of the fastest supercomputers in the world of the 1990s—a decentralized model for teaching computational chemistry is now possible, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device 1 (BYOD) scheme. In addition to the programs’ use for various applications, open access to the programs’ source code also enables comprehensive teaching strategies, as actual algorithms’ implementations can be used in teaching. -
D6.1 Report on the Deployment of the Max Demonstrators and Feedback to WP1-5
Ref. Ares(2020)2820381 - 31/05/2020 HORIZON2020 European Centre of Excellence Deliverable D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 Pablo Ordejón, Uliana Alekseeva, Stefano Baroni, Riccardo Bertossa, Miki Bonacci, Pietro Bonfà, Claudia Cardoso, Carlo Cavazzoni, Vladimir Dikan, Stefano de Gironcoli, Andrea Ferretti, Alberto García, Luigi Genovese, Federico Grasselli, Anton Kozhevnikov, Deborah Prezzi, Davide Sangalli, Joost VandeVondele, Daniele Varsano, Daniel Wortmann Due date of deliverable: 31/05/2020 Actual submission date: 31/05/2020 Final version: 31/05/2020 Lead beneficiary: ICN2 (participant number 3) Dissemination level: PU - Public www.max-centre.eu 1 HORIZON2020 European Centre of Excellence Deliverable D6.1 Report on the deployment of the MaX Demonstrators and feedback to WP1-5 Document information Project acronym: MaX Project full title: Materials Design at the Exascale Research Action Project type: European Centre of Excellence in materials modelling, simulations and design EC Grant agreement no.: 824143 Project starting / end date: 01/12/2018 (month 1) / 30/11/2021 (month 36) Website: www.max-centre.eu Deliverable No.: D6.1 Authors: P. Ordejón, U. Alekseeva, S. Baroni, R. Bertossa, M. Bonacci, P. Bonfà, C. Cardoso, C. Cavazzoni, V. Dikan, S. de Gironcoli, A. Ferretti, A. García, L. Genovese, F. Grasselli, A. Kozhevnikov, D. Prezzi, D. Sangalli, J. VandeVondele, D. Varsano, D. Wortmann To be cited as: Ordejón, et al., (2020): Report on the deployment of the MaX Demonstrators and feedback to WP1-5. Deliverable D6.1 of the H2020 project MaX (final version as of 31/05/2020). -
Quan Wan, "First Principles Simulations of Vibrational Spectra
THE UNIVERSITY OF CHICAGO FIRST PRINCIPLES SIMULATIONS OF VIBRATIONAL SPECTRA OF AQUEOUS SYSTEMS A DISSERTATION SUBMITTED TO THE FACULTY OF THE INSTITUTE FOR MOLECULAR ENGINEERING IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY BY QUAN WAN CHICAGO, ILLINOIS AUGUST 2015 Copyright c 2015 by Quan Wan All Rights Reserved For Jing TABLE OF CONTENTS LISTOFFIGURES .................................... vii LISTOFTABLES ..................................... xii ACKNOWLEDGMENTS ................................. xiii ABSTRACT ........................................ xiv 1 INTRODUCTION ................................... 1 2 FIRST PRINCIPLES CALCULATIONS OF GROUND STATE PROPERTIES . 5 2.1 Densityfunctionaltheory. 5 2.1.1 TheSchr¨odingerEquation . 5 2.1.2 Density Functional Theory and the Kohn-Sham Ansatz . .. 6 2.1.3 Approximations to the Exchange-Correlation Energy Functional... 9 2.1.4 Planewave Pseudopotential Scheme . 11 2.1.5 First Principles Molecular Dynamics . 12 3 FIRST-PRINCIPLES CALCULATIONS IN THE PRESENCE OF EXTERNAL ELEC- TRICFIELDS ..................................... 14 3.1 Density functional perturbation theory for Homogeneous Electric Field Per- turbations..................................... 14 3.1.1 LinearResponsewithinDFPT. 14 3.1.2 Homogeneous Electric Field Perturbations . 15 3.1.3 ImplementationofDFPTintheQboxCode . 17 3.2 Finite Field Methods for Homogeneous Electric Field . 20 3.2.1 Finite Field Methods . 21 3.2.2 Computational Details of Finite Field Implementations . 25 3.2.3 Results for Single Water Molecules and Liquid Water . 27 3.2.4 Conclusions ................................ 32 4 VIBRATIONAL SIGNATURES OF CHARGE FLUCTUATIONS IN THE HYDRO- GENBONDNETWORKOFLIQUIDWATER . 34 4.1 IntroductiontoVibrationalSpectroscopy . ..... 34 4.1.1 NormalModeandTCFApproaches. 35 4.1.2 InfraredSpectroscopy. .. .. 36 4.1.3 RamanSpectroscopy ........................... 37 4.2 RamanSpectroscopyforLiquidWater . 39 4.3 TheoreticalMethods ............................... 41 4.3.1 Simulation Details . 41 4.3.2 Effective molecular polarizabilities . -
5 Jul 2020 (finite Non-Periodic Vs
ELSI | An Open Infrastructure for Electronic Structure Solvers Victor Wen-zhe Yua, Carmen Camposb, William Dawsonc, Alberto Garc´ıad, Ville Havue, Ben Hourahinef, William P. Huhna, Mathias Jacqueling, Weile Jiag,h, Murat Ke¸celii, Raul Laasnera, Yingzhou Lij, Lin Ling,h, Jianfeng Luj,k,l, Jonathan Moussam, Jose E. Romanb, Alvaro´ V´azquez-Mayagoitiai, Chao Yangg, Volker Bluma,l,∗ aDepartment of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA bDepartament de Sistemes Inform`aticsi Computaci´o,Universitat Polit`ecnica de Val`encia,Val`encia,Spain cRIKEN Center for Computational Science, Kobe 650-0047, Japan dInstitut de Ci`enciade Materials de Barcelona (ICMAB-CSIC), Bellaterra E-08193, Spain eDepartment of Applied Physics, Aalto University, Aalto FI-00076, Finland fSUPA, University of Strathclyde, Glasgow G4 0NG, UK gComputational Research Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA hDepartment of Mathematics, University of California, Berkeley, CA 94720, USA iComputational Science Division, Argonne National Laboratory, Argonne, IL 60439, USA jDepartment of Mathematics, Duke University, Durham, NC 27708, USA kDepartment of Physics, Duke University, Durham, NC 27708, USA lDepartment of Chemistry, Duke University, Durham, NC 27708, USA mMolecular Sciences Software Institute, Blacksburg, VA 24060, USA Abstract Routine applications of electronic structure theory to molecules and peri- odic systems need to compute the electron density from given Hamiltonian and, in case of non-orthogonal basis sets, overlap matrices. System sizes can range from few to thousands or, in some examples, millions of atoms. Different discretization schemes (basis sets) and different system geometries arXiv:1912.13403v3 [physics.comp-ph] 5 Jul 2020 (finite non-periodic vs. -
An Ab Initio Materials Simulation Code
CP2K: An ab initio materials simulation code Lianheng Tong Physics on Boat Tutorial , Helsinki, Finland 2015-06-08 Faculty of Natural & Mathematical Sciences Department of Physics Brief Overview • Overview of CP2K - General information about the package - QUICKSTEP: DFT engine • Practical example of using CP2K to generate simulated STM images - Terminal states in AGNR segments with 1H and 2H termination groups What is CP2K? Swiss army knife of molecular simulation www.cp2k.org • Geometry and cell optimisation Energy and • Molecular dynamics (NVE, Force Engine NVT, NPT, Langevin) • STM Images • Sampling energy surfaces (metadynamics) • DFT (LDA, GGA, vdW, • Finding transition states Hybrid) (Nudged Elastic Band) • Quantum Chemistry (MP2, • Path integral molecular RPA) dynamics • Semi-Empirical (DFTB) • Monte Carlo • Classical Force Fields (FIST) • And many more… • Combinations (QM/MM) Development • Freely available, open source, GNU Public License • www.cp2k.org • FORTRAN 95, > 1,000,000 lines of code, very active development (daily commits) • Currently being developed and maintained by community of developers: • Switzerland: Paul Scherrer Institute Switzerland (PSI), Swiss Federal Institute of Technology in Zurich (ETHZ), Universität Zürich (UZH) • USA: IBM Research, Lawrence Livermore National Laboratory (LLNL), Pacific Northwest National Laboratory (PNL) • UK: Edinburgh Parallel Computing Centre (EPCC), King’s College London (KCL), University College London (UCL) • Germany: Ruhr-University Bochum • Others: We welcome contributions from -
Automated Construction of Quantum–Classical Hybrid Models Arxiv:2102.09355V1 [Physics.Chem-Ph] 18 Feb 2021
Automated construction of quantum{classical hybrid models Christoph Brunken and Markus Reiher∗ ETH Z¨urich, Laboratorium f¨urPhysikalische Chemie, Vladimir-Prelog-Weg 2, 8093 Z¨urich, Switzerland February 18, 2021 Abstract We present a protocol for the fully automated construction of quantum mechanical-(QM){ classical hybrid models by extending our previously reported approach on self-parametri- zing system-focused atomistic models (SFAM) [J. Chem. Theory Comput. 2020, 16 (3), 1646{1665]. In this QM/SFAM approach, the size and composition of the QM region is evaluated in an automated manner based on first principles so that the hybrid model describes the atomic forces in the center of the QM region accurately. This entails the au- tomated construction and evaluation of differently sized QM regions with a bearable com- putational overhead that needs to be paid for automated validation procedures. Applying SFAM for the classical part of the model eliminates any dependence on pre-existing pa- rameters due to its system-focused quantum mechanically derived parametrization. Hence, QM/SFAM is capable of delivering a high fidelity and complete automation. Furthermore, since SFAM parameters are generated for the whole system, our ansatz allows for a con- venient re-definition of the QM region during a molecular exploration. For this purpose, a local re-parametrization scheme is introduced, which efficiently generates additional clas- sical parameters on the fly when new covalent bonds are formed (or broken) and moved to the classical region. arXiv:2102.09355v1 [physics.chem-ph] 18 Feb 2021 ∗Corresponding author; e-mail: [email protected] 1 1 Introduction In contrast to most protocols of computational quantum chemistry that consider isolated molecules, chemical processes can take place in a vast variety of complex environments. -
The CECAM Electronic Structure Library and the Modular Software Development Paradigm
The CECAM electronic structure library and the modular software development paradigm Cite as: J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 Submitted: 06 May 2020 . Accepted: 08 June 2020 . Published Online: 13 July 2020 Micael J. T. Oliveira , Nick Papior , Yann Pouillon , Volker Blum , Emilio Artacho , Damien Caliste , Fabiano Corsetti , Stefano de Gironcoli , Alin M. Elena , Alberto García , Víctor M. García-Suárez , Luigi Genovese , William P. Huhn , Georg Huhs , Sebastian Kokott , Emine Küçükbenli , Ask H. Larsen , Alfio Lazzaro , Irina V. Lebedeva , Yingzhou Li , David López- Durán , Pablo López-Tarifa , Martin Lüders , Miguel A. L. Marques , Jan Minar , Stephan Mohr , Arash A. Mostofi , Alan O’Cais , Mike C. Payne, Thomas Ruh, Daniel G. A. Smith , José M. Soler , David A. Strubbe , Nicolas Tancogne-Dejean , Dominic Tildesley, Marc Torrent , and Victor Wen-zhe Yu COLLECTIONS Paper published as part of the special topic on Electronic Structure Software Note: This article is part of the JCP Special Topic on Electronic Structure Software. This paper was selected as Featured ARTICLES YOU MAY BE INTERESTED IN Recent developments in the PySCF program package The Journal of Chemical Physics 153, 024109 (2020); https://doi.org/10.1063/5.0006074 An open-source coding paradigm for electronic structure calculations Scilight 2020, 291101 (2020); https://doi.org/10.1063/10.0001593 Siesta: Recent developments and applications The Journal of Chemical Physics 152, 204108 (2020); https://doi.org/10.1063/5.0005077 J. Chem. Phys. 153, 024117 (2020); https://doi.org/10.1063/5.0012901 153, 024117 © 2020 Author(s). The Journal ARTICLE of Chemical Physics scitation.org/journal/jcp The CECAM electronic structure library and the modular software development paradigm Cite as: J. -
Improvements of Bigdft Code in Modern HPC Architectures
Available on-line at www.prace-ri.eu Partnership for Advanced Computing in Europe Improvements of BigDFT code in modern HPC architectures Luigi Genovesea;b;∗, Brice Videaua, Thierry Deutscha, Huan Tranc, Stefan Goedeckerc aLaboratoire de Simulation Atomistique, SP2M/INAC/CEA, 17 Av. des Martyrs, 38054 Grenoble, France bEuropean Synchrotron Radiation Facility, 6 rue Horowitz, BP 220, 38043 Grenoble, France cInstitut f¨urPhysik, Universit¨atBasel, Klingelbergstr.82, 4056 Basel, Switzerland Abstract Electronic structure calculations (DFT codes) are certainly among the disciplines for which an increasing of the computa- tional power correspond to an advancement in the scientific results. In this report, we present the ongoing advancements of DFT code that can run on massively parallel, hybrid and heterogeneous CPU-GPU clusters. This DFT code, named BigDFT, is delivered within the GNU-GPL license either in a stand-alone version or integrated in the ABINIT software package. Hybrid BigDFT routines were initially ported with NVidia's CUDA language, and recently more functionalities have been added with new routines writeen within Kronos' OpenCL standard. The formalism of this code is based on Daubechies wavelets, which is a systematic real-space based basis set. The properties of this basis set are well suited for an extension on a GPU-accelerated environment. In addition to focusing on the performances of the MPI and OpenMP parallelisation the BigDFT code, this presentation also relies of the usage of the GPU resources in a complex code with different kinds of operations. A discussion on the interest of present and expected performances of Hybrid architectures computation in the framework of electronic structure calculations is also adressed. -
The Empirical Pseudopotential Method
The empirical pseudopotential method Richard Tran PID: A1070602 1 Background In 1934, Fermi [1] proposed a method to describe high lying atomic states by replacing the complex effects of the core electrons with an effective potential described by the pseudo-wavefunctions of the valence electrons. Shortly after, this pseudopotential (PSP), was realized by Hellmann [2] who implemented it to describe the energy levels of alkali atoms. Despite his initial success, further exploration of the PSP and its application in efficiently solving the Hamiltonian of periodic systems did not reach its zenith until the middle of the twenti- eth century when Herring [3] proposed the orthogonalized plane wave (OPW) method to calculate the wave functions of metals and semiconductors. Using the OPW, Phillips and Kleinman [4] showed that the orthogonality of the va- lence electrons’ wave functions relative to that of the core electrons leads to a cancellation of the attractive and repulsive potentials of the core electrons (with the repulsive potentials being approximated either analytically or empirically), allowing for a rapid convergence of the OPW calculations. Subsequently in the 60s, Marvin Cohen expanded and improved upon the pseudopotential method by using the crystalline energy levels of several semicon- ductor materials [6, 7, 8] to empirically obtain the potentials needed to compute the atomic wave functions, developing what is now known as the empirical pseu- dopotential method (EPM). Although more advanced PSPs such as the ab-initio pseudopotentials exist, the EPM still provides an incredibly accurate method for calculating optical properties and band structures, especially for metals and semiconductors, with a less computationally taxing method. -
Kepler Gpus and NVIDIA's Life and Material Science
LIFE AND MATERIAL SCIENCES Mark Berger; [email protected] Founded 1993 Invented GPU 1999 – Computer Graphics Visual Computing, Supercomputing, Cloud & Mobile Computing NVIDIA - Core Technologies and Brands GPU Mobile Cloud ® ® GeForce Tegra GRID Quadro® , Tesla® Accelerated Computing Multi-core plus Many-cores GPU Accelerator CPU Optimized for Many Optimized for Parallel Tasks Serial Tasks 3-10X+ Comp Thruput 7X Memory Bandwidth 5x Energy Efficiency How GPU Acceleration Works Application Code Compute-Intensive Functions Rest of Sequential 5% of Code CPU Code GPU CPU + GPUs : Two Year Heart Beat 32 Volta Stacked DRAM 16 Maxwell Unified Virtual Memory 8 Kepler Dynamic Parallelism 4 Fermi 2 FP64 DP GFLOPS GFLOPS per DP Watt 1 Tesla 0.5 CUDA 2008 2010 2012 2014 Kepler Features Make GPU Coding Easier Hyper-Q Dynamic Parallelism Speedup Legacy MPI Apps Less Back-Forth, Simpler Code FERMI 1 Work Queue CPU Fermi GPU CPU Kepler GPU KEPLER 32 Concurrent Work Queues Developer Momentum Continues to Grow 100M 430M CUDA –Capable GPUs CUDA-Capable GPUs 150K 1.6M CUDA Downloads CUDA Downloads 1 50 Supercomputer Supercomputers 60 640 University Courses University Courses 4,000 37,000 Academic Papers Academic Papers 2008 2013 Explosive Growth of GPU Accelerated Apps # of Apps Top Scientific Apps 200 61% Increase Molecular AMBER LAMMPS CHARMM NAMD Dynamics GROMACS DL_POLY 150 Quantum QMCPACK Gaussian 40% Increase Quantum Espresso NWChem Chemistry GAMESS-US VASP CAM-SE 100 Climate & COSMO NIM GEOS-5 Weather WRF Chroma GTS 50 Physics Denovo ENZO GTC MILC ANSYS Mechanical ANSYS Fluent 0 CAE MSC Nastran OpenFOAM 2010 2011 2012 SIMULIA Abaqus LS-DYNA Accelerated, In Development NVIDIA GPU Life Science Focus Molecular Dynamics: All codes are available AMBER, CHARMM, DESMOND, DL_POLY, GROMACS, LAMMPS, NAMD Great multi-GPU performance GPU codes: ACEMD, HOOMD-Blue Focus: scaling to large numbers of GPUs Quantum Chemistry: key codes ported or optimizing Active GPU acceleration projects: VASP, NWChem, Gaussian, GAMESS, ABINIT, Quantum Espresso, BigDFT, CP2K, GPAW, etc. -
Density Functional Theory and Nuclear Quantum Effects
1 Grassmann manifold, gauge, and quantum chemistry Lin Lin Department of Mathematics, UC Berkeley; Lawrence Berkeley National Laboratory Flatiron Institute, April 2019 2 Stiefel manifold • × > ℂ • Stiefel manifold (1935): orthogonal vectors in × ℂ , = | = ∗ ∈ ℂ 3 Grassmann manifold • Grassmann manifold (1848): set of dimensional subspace in ℂ , = , / : dimensional unitary group (i.e. the set of unitary matrices in × ) ℂ • Any point in , can be viewed as a representation of a point in , 4 Example (in optimization) min ( ) . = ∗ • = , . invariant to the choice of basis Optimization on a Grassmann manifold. ∈ ⇒ • The representation is called the gauge in quantum physics / chemistry. Gauge invariant. 5 Example (in optimization) min ( ) . = ∗ • Otherwise, if ( ) is gauge dependent Optimization on a Stiefel manifold. ⇒ • [Edelman, Arias, Smith, SIMAX 1998] many works following 6 This talk: Quantum chemistry • Time-dependent density functional theory (TDDFT) • Kohn-Sham density functional theory (KSDFT) • Localization. • Gauge choice is the key • Used in community software packages: Quantum ESPRESSO, Wannier90, Octopus, PWDFT etc 7 Time-dependent density functional theory Parallel transport gauge 8 Time-dependent density functional theory [Runge-Gross, PRL 1984] (spin omitted, : number of electrons) , = , , , = 1, … , , , = ( ,) ( , ) ′ ∗ ′ � Hamiltonian =1 1 , = + ( ) + [ ] 2 − Δ 9 Density matrix • Key quantity throughout the talk × • (t) = , … , . : number of discretized grid points to represent each Ψ 1 ∈ ℂ = ∗ Ψ Ψ = 10 Gauge invariance • Unitary rotation = , = ∗ = Φ Ψ= ( ) = ∗ ∗ ∗ ∗ ΦΦ Ψ Ψ ΨΨ • Propagation of the density matrix, von Neumann equation (quantum Liouville equation) = , = , ( ) , − 11 Gauge choice affects the time step Extreme case (boring Hamiltonian) ≡ 0 Initial condition 0 = 0 0 Exact solution = (0) Small time step − = (0) (Formally) arbitrarily large time step 12 P v.s.