Reproducibility in Density Functional Theory Calculations of Solids
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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 -
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). -
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. -
Solid-State NMR Techniques for the Structural Characterization of Cyclic Aggregates Based on Borane–Phosphane Frustrated Lewis Pairs
molecules Review Solid-State NMR Techniques for the Structural Characterization of Cyclic Aggregates Based on Borane–Phosphane Frustrated Lewis Pairs Robert Knitsch 1, Melanie Brinkkötter 1, Thomas Wiegand 2, Gerald Kehr 3 , Gerhard Erker 3, Michael Ryan Hansen 1 and Hellmut Eckert 1,4,* 1 Institut für Physikalische Chemie, WWU Münster, 48149 Münster, Germany; [email protected] (R.K.); [email protected] (M.B.); [email protected] (M.R.H.) 2 Laboratorium für Physikalische Chemie, ETH Zürich, 8093 Zürich, Switzerland; [email protected] 3 Organisch-Chemisches Institut, WWU Münster, 48149 Münster, Germany; [email protected] (G.K.); [email protected] (G.E.) 4 Instituto de Física de Sao Carlos, Universidad de Sao Paulo, Sao Carlos SP 13566-590, Brazil * Correspondence: [email protected] Academic Editor: Mattias Edén Received: 20 February 2020; Accepted: 17 March 2020; Published: 19 March 2020 Abstract: Modern solid-state NMR techniques offer a wide range of opportunities for the structural characterization of frustrated Lewis pairs (FLPs), their aggregates, and the products of cooperative addition reactions at their two Lewis centers. This information is extremely valuable for materials that elude structural characterization by X-ray diffraction because of their nanocrystalline or amorphous character, (pseudo-)polymorphism, or other types of disordering phenomena inherent in the solid state. Aside from simple chemical shift measurements using single-pulse or cross-polarization/magic-angle spinning NMR detection techniques, the availability of advanced multidimensional and double-resonance NMR methods greatly deepened the informational content of these experiments. In particular, methods quantifying the magnetic dipole–dipole interaction strengths and indirect spin–spin interactions prove useful for the measurement of intermolecular association, connectivity, assessment of FLP–ligand distributions, and the stereochemistry of adducts. -
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. -
TBTK: a Quantum Mechanics Software Development Kit
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Copenhagen University Research Information System TBTK A quantum mechanics software development kit Bjornson, Kristofer Published in: SoftwareX DOI: 10.1016/j.softx.2019.02.005 Publication date: 2019 Document version Publisher's PDF, also known as Version of record Document license: CC BY Citation for published version (APA): Bjornson, K. (2019). TBTK: A quantum mechanics software development kit. SoftwareX, 9, 205-210. https://doi.org/10.1016/j.softx.2019.02.005 Download date: 09. apr.. 2020 SoftwareX 9 (2019) 205–210 Contents lists available at ScienceDirect SoftwareX journal homepage: www.elsevier.com/locate/softx Original software publication TBTK: A quantum mechanics software development kit Kristofer Björnson Niels Bohr Institute, University of Copenhagen, Juliane Maries Vej 30, DK–2100, Copenhagen, Denmark article info a b s t r a c t Article history: TBTK is a software development kit for quantum mechanical calculations and is designed to enable the Received 7 August 2018 development of applications that investigate problems formulated on second-quantized form. It also Received in revised form 21 February 2019 enables method developers to create solvers for tight-binding, DFT, DMFT, quantum transport, etc., that Accepted 22 February 2019 can be easily integrated with each other. Both through the development of completely new solvers, Keywords: as well as front and back ends to already well established packages. TBTK provides data structures Quantum mechanics tailored for second-quantization that will encourage reusability and enable scalability for quantum SDK mechanical calculations. C++ ' 2019 The Author. -
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. -
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. -
An Overview on the Libxc Library of Density Functional Approximations
An overview on the Libxc library of density functional approximations Susi Lehtola Molecular Sciences Software Institute at Virginia Tech 2 June 2021 Outline Why Libxc? Recap on DFT What is Libxc? Using Libxc A look under the hood Wrapup GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 2/28 Why Libxc? There are many approximations for the exchange-correlation functional. But, most programs I ... only implement a handful (sometimes 5, typically 10-15) I ... and the implementations may be buggy / non-standard GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 3/28 Why Libxc, cont'd This leads to issues with reproducibility I chemists and physicists do not traditionally use the same functionals! Outdated(?) stereotype: B3LYP vs PBE I how to reproduce a calculation performed with another code? GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 4/28 Why Libxc, cont'd The issue is compounded by the need for backwards and forwards compatibility: how can one I reproduce old calculations from the literature done with a now-obsolete functional (possibly with a program that is proprietary / no longer available)? I use a newly developed functional in an old program? GPAW 2021: Users' and Developers' Meeting Susi Lehtola Why Libxc? 5/28 Why Libxc, cont'd A standard implementation is beneficial! I no need to keep reinventing (and rebuilding) the wheel I use same collection of density functionals in all programs I new functionals only need to be implemented in one place I broken/buggy functionals only need to be fixed in one place I same implementation can be used across numerical approaches, e.g. -
Introduction to DFT and the Plane-Wave Pseudopotential Method
Introduction to DFT and the plane-wave pseudopotential method Keith Refson STFC Rutherford Appleton Laboratory Chilton, Didcot, OXON OX11 0QX 23 Apr 2014 Parallel Materials Modelling Packages @ EPCC 1 / 55 Introduction Synopsis Motivation Some ab initio codes Quantum-mechanical approaches Density Functional Theory Electronic Structure of Condensed Phases Total-energy calculations Introduction Basis sets Plane-waves and Pseudopotentials How to solve the equations Parallel Materials Modelling Packages @ EPCC 2 / 55 Synopsis Introduction A guided tour inside the “black box” of ab-initio simulation. Synopsis • Motivation • The rise of quantum-mechanical simulations. Some ab initio codes Wavefunction-based theory • Density-functional theory (DFT) Quantum-mechanical • approaches Quantum theory in periodic boundaries • Plane-wave and other basis sets Density Functional • Theory SCF solvers • Molecular Dynamics Electronic Structure of Condensed Phases Recommended Reading and Further Study Total-energy calculations • Basis sets Jorge Kohanoff Electronic Structure Calculations for Solids and Molecules, Plane-waves and Theory and Computational Methods, Cambridge, ISBN-13: 9780521815918 Pseudopotentials • Dominik Marx, J¨urg Hutter Ab Initio Molecular Dynamics: Basic Theory and How to solve the Advanced Methods Cambridge University Press, ISBN: 0521898633 equations • Richard M. Martin Electronic Structure: Basic Theory and Practical Methods: Basic Theory and Practical Density Functional Approaches Vol 1 Cambridge University Press, ISBN: 0521782856 -
Electronic Structure Study of Copper-Containing Perovskites
Electronic Structure Study of Copper-containing Perovskites Mark Robert Michel University College London A thesis submitted to University College London in partial fulfilment of the requirements for the degree of Doctor of Philosophy, February 2010. 1 I, Mark Robert Michel, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis. Mark Robert Michel 2 Abstract This thesis concerns the computational study of copper containing perovskites using electronic structure methods. We discuss an extensive set of results obtained using hybrid exchange functionals within Density Functional Theory (DFT), in which we vary systematically the amount of exact (Hartree-Fock, HF) exchange employed. The method has enabled us to obtain accurate results on a range of systems, particularly in materials containing strongly correlated ions, such as Cu2+. This is possible because the HF exchange corrects, at least qualitatively, the spurious self-interaction error present in DFT. The materials investigated include two families of perovskite-structured oxides, of potential interest for technological applications due to the very large dielectric constant or for Multi-Ferroic behaviour. The latter materials exhibit simultaneously ferroelectric and ferromagnetic properties, a rare combination, which is however highly desirable for memory device applications. The results obtained using hybrid exchange functionals are highly encouraging. Initial studies were made on bulk materials such as CaCu3Ti4O12 (CCTO) which is well characterised by experiment. The inclusion of HF exchange improved, in a systematic way, both structural and electronic results with respect to experiment. The confidence gained in the study of known compounds has enabled us to explore new compositions predictively. -
Pyscf: the Python-Based Simulations of Chemistry Framework
PySCF: The Python-based Simulations of Chemistry Framework Qiming Sun∗1, Timothy C. Berkelbach2, Nick S. Blunt3,4, George H. Booth5, Sheng Guo1,6, Zhendong Li1, Junzi Liu7, James D. McClain1,6, Elvira R. Sayfutyarova1,6, Sandeep Sharma8, Sebastian Wouters9, and Garnet Kin-Lic Chany1 1Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena CA 91125, USA 2Department of Chemistry and James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA 3Chemical Science Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA 4Department of Chemistry, University of California, Berkeley, California 94720, USA 5Department of Physics, King's College London, Strand, London WC2R 2LS, United Kingdom 6Department of Chemistry, Princeton University, Princeton, New Jersey 08544, USA 7Institute of Chemistry Chinese Academy of Sciences, Beijing 100190, P. R. China 8Department of Chemistry and Biochemistry, University of Colorado Boulder, Boulder, CO 80302, USA 9Brantsandpatents, Pauline van Pottelsberghelaan 24, 9051 Sint-Denijs-Westrem, Belgium arXiv:1701.08223v2 [physics.chem-ph] 2 Mar 2017 Abstract PySCF is a general-purpose electronic structure platform designed from the ground up to emphasize code simplicity, so as to facilitate new method development and enable flexible ∗[email protected] [email protected] 1 computational workflows. The package provides a wide range of tools to support simulations of finite-size systems, extended systems with periodic boundary conditions, low-dimensional periodic systems, and custom Hamiltonians, using mean-field and post-mean-field methods with standard Gaussian basis functions. To ensure ease of extensibility, PySCF uses the Python language to implement almost all of its features, while computationally critical paths are implemented with heavily optimized C routines.