SIMPLE Code: Optical Properties with Optimal Basis Functions
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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). -
D7.2 Second Report on the Activity of the High-Level Support Services (Second Year)
Ref. Ares(2020)7219471 - 30/11/2020 HORIZON2020 European Centre of Excellence Deliverable D7.2 Second report on the activity of the High-Level Support services (second year) D7.2 Second report on the activity of the High-Level Support services (second year) Mariella Ippolito, Francisco Ramirez, Giovanni Pizzi, and Elsa Passaro Due date of deliverable: 30/11/2020 Actual submission date: 30/11/2020 Final version: 30/11/2020 Lead beneficiary: CINECA (participant number 8) Dissemination level: PU - Public www.max-centre.eu 1 HORIZON2020 European Centre of Excellence Deliverable D7.2 Second report on the activity of the High-Level Support services (second year) 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.: D7.2 Authors: M. Ippolito, F. Ramirez, G. Pizzi, and E. Passaro To be cited as: M. Ippolito et al., (2020): Second report on the activity of the High-Level Support services (second year). Deliverable D7.2 of the H2020 project MaX (final version as of 30/11/2020). EC grant agreement no: 824143, CINECA, Bologna, Italy. Disclaimer: This document’s contents are not intended to replace consultation of any applicable legal sources or the necessary advice of a legal expert, where appropriate. All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. -
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. -
Arxiv:2005.05756V2
“This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in Oliveira, M.J.T. [et al.]. The CECAM electronic structure library and the modular software development paradigm. "The Journal of Chemical Physics", 12020, vol. 153, núm. 2, and may be found at https://aip.scitation.org/doi/10.1063/5.0012901. The CECAM Electronic Structure Library and the modular software development paradigm Micael J. T. Oliveira,1, a) Nick Papior,2, b) Yann Pouillon,3, 4, c) Volker Blum,5, 6 Emilio Artacho,7, 8, 9 Damien Caliste,10 Fabiano Corsetti,11, 12 Stefano de Gironcoli,13 Alin M. Elena,14 Alberto Garc´ıa,15 V´ıctor M. Garc´ıa-Su´arez,16 Luigi Genovese,10 William P. Huhn,5 Georg Huhs,17 Sebastian Kokott,18 Emine K¨u¸c¨ukbenli,13, 19 Ask H. Larsen,20, 4 Alfio Lazzaro,21 Irina V. Lebedeva,22 Yingzhou Li,23 David L´opez-Dur´an,22 Pablo L´opez-Tarifa,24 Martin L¨uders,1, 14 Miguel A. L. Marques,25 Jan Minar,26 Stephan Mohr,17 Arash A. Mostofi,11 Alan O'Cais,27 Mike C. Payne,9 Thomas Ruh,28 Daniel G. A. Smith,29 Jos´eM. Soler,30 David A. Strubbe,31 Nicolas Tancogne-Dejean,1 Dominic Tildesley,32 Marc Torrent,33, 34 and Victor Wen-zhe Yu5 1)Max Planck Institute for the Structure and Dynamics of Matter, D-22761 Hamburg, Germany 2)DTU Computing Center, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark 3)Departamento CITIMAC, Universidad de Cantabria, Santander, Spain 4)Simune Atomistics, 20018 San Sebasti´an,Spain 5)Department of Mechanical Engineering and Materials Science, Duke University, Durham, NC 27708, USA 6)Department of Chemistry, Duke University, Durham, NC 27708, USA 7)CIC Nanogune BRTA and DIPC, 20018 San Sebasti´an,Spain 8)Ikerbasque, Basque Foundation for Science, 48011 Bilbao, Spain 9)Theory of Condensed Matter, Cavendish Laboratory, University of Cambridge, Cambridge CB3 0HE, United Kingdom 10)Department of Physics, IRIG, Univ. -
CHEM:4480 Introduction to Molecular Modeling
CHEM:4480:001 Introduction to Molecular Modeling Fall 2018 Instructor Dr. Sara E. Mason Office: W339 CB, Phone: (319) 335-2761, Email: [email protected] Lecture 11:00 AM - 12:15 PM, Tuesday/Thursday. Primary Location: W228 CB. Please note that we will often meet in one of the Chemistry Computer Labs. Notice about class being held outside of the primary location will be posted to the course ICON site by 24 hours prior to the start of class. Office Tuesday 12:30-2 PM (EXCEPT for DoC faculty meeting days 9/4, 10/2, 10/9, 11/6, Hour and 12/4), Thursday 12:30-2, or by appointment Department Dr. James Gloer DEO Administrative Office: E331 CB Administrative Phone: (319) 335-1350 Administrative Email: [email protected] Text Instead of a course text, assigned readings will be given throughout the semester in forms such as journal articles or library reserves. Website http://icon.uiowa.edu Course To develop practical skills associated with chemical modeling based on quantum me- Objectives chanics and computational chemistry such as working with shell commands, math- ematical software, and modeling packages. We will also work to develop a basic understanding of quantum mechanics, approximate methods, and electronic struc- ture. Technical computing, pseudopotential generation, density functional theory software, (along with other optional packages) will be used hands-on to gain experi- ence in data manipulation and modeling. We will use both a traditional classroom setting and hands-on learning in computer labs to achieve course goals. Course Topics • Introduction: It’s a quantum world! • Dirac notation and matrix mechanics (a.k.a. -
Trends in Atomistic Simulation Software Usage [1.3]
A LiveCoMS Perpetual Review Trends in atomistic simulation software usage [1.3] Leopold Talirz1,2,3*, Luca M. Ghiringhelli4, Berend Smit1,3 1Laboratory of Molecular Simulation (LSMO), Institut des Sciences et Ingenierie Chimiques, Valais, École Polytechnique Fédérale de Lausanne, CH-1951 Sion, Switzerland; 2Theory and Simulation of Materials (THEOS), Faculté des Sciences et Techniques de l’Ingénieur, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 3National Centre for Computational Design and Discovery of Novel Materials (MARVEL), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; 4The NOMAD Laboratory at the Fritz Haber Institute of the Max Planck Society and Humboldt University, Berlin, Germany This LiveCoMS document is Abstract Driven by the unprecedented computational power available to scientific research, the maintained online on GitHub at https: use of computers in solid-state physics, chemistry and materials science has been on a continuous //github.com/ltalirz/ rise. This review focuses on the software used for the simulation of matter at the atomic scale. We livecoms-atomistic-software; provide a comprehensive overview of major codes in the field, and analyze how citations to these to provide feedback, suggestions, or help codes in the academic literature have evolved since 2010. An interactive version of the underlying improve it, please visit the data set is available at https://atomistic.software. GitHub repository and participate via the issue tracker. This version dated August *For correspondence: 30, 2021 [email protected] (LT) 1 Introduction Gaussian [2], were already released in the 1970s, followed Scientists today have unprecedented access to computa- by force-field codes, such as GROMOS [3], and periodic tional power. -
(DFT) and Its Application to Defects in Semiconductors
Introduction to DFT and its Application to Defects in Semiconductors Noa Marom Physics and Engineering Physics Tulane University New Orleans The Future: Computer-Aided Materials Design • Can access the space of materials not experimentally known • Can scan through structures and compositions faster than is possible experimentally • Unbiased search can yield unintuitive solutions • Can accelerate the discovery and deployment of new materials Accurate electronic structure methods Efficient search algorithms Dirac’s Challenge “The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble. It therefore becomes desirable that approximate practical methods of applying quantum mechanics should be developed, which can lead to an P. A. M. Dirac explanation of the main features of Physics Nobel complex atomic systems ... ” Prize, 1933 -P. A. M. Dirac, 1929 The Many (Many, Many) Body Problem Schrödinger’s Equation: The physical laws are completely known But… There are as many electrons in a penny as stars in the known universe! Electronic Structure Methods for Materials Properties Structure Ionization potential (IP) Absorption Mechanical Electron Affinity (EA) spectrum properties Fundamental gap Optical gap Vibrational Defect/dopant charge Exciton binding spectrum transition levels energy Ground State Charged Excitation Neutral Excitation DFT -
Package Name Software Description Project
A S T 1 Package Name Software Description Project URL 2 Autoconf An extensible package of M4 macros that produce shell scripts to automatically configure software source code packages https://www.gnu.org/software/autoconf/ 3 Automake www.gnu.org/software/automake 4 Libtool www.gnu.org/software/libtool 5 bamtools BamTools: a C++ API for reading/writing BAM files. https://github.com/pezmaster31/bamtools 6 Biopython (Python module) Biopython is a set of freely available tools for biological computation written in Python by an international team of developers www.biopython.org/ 7 blas The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. http://www.netlib.org/blas/ 8 boost Boost provides free peer-reviewed portable C++ source libraries. http://www.boost.org 9 CMake Cross-platform, open-source build system. CMake is a family of tools designed to build, test and package software http://www.cmake.org/ 10 Cython (Python module) The Cython compiler for writing C extensions for the Python language https://www.python.org/ 11 Doxygen http://www.doxygen.org/ FFmpeg is the leading multimedia framework, able to decode, encode, transcode, mux, demux, stream, filter and play pretty much anything that humans and machines have created. It supports the most obscure ancient formats up to the cutting edge. No matter if they were designed by some standards 12 ffmpeg committee, the community or a corporation. https://www.ffmpeg.org FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and 13 fftw complex data (as well as of even/odd data, i.e. -
Virt&L-Comm.3.2012.1
Virt&l-Comm.3.2012.1 A MODERN APPROACH TO AB INITIO COMPUTING IN CHEMISTRY, MOLECULAR AND MATERIALS SCIENCE AND TECHNOLOGIES ANTONIO LAGANA’, DEPARTMENT OF CHEMISTRY, UNIVERSITY OF PERUGIA, PERUGIA (IT)* ABSTRACT In this document we examine the present situation of Ab initio computing in Chemistry and Molecular and Materials Science and Technologies applications. To this end we give a short survey of the most popular quantum chemistry and quantum (as well as classical and semiclassical) molecular dynamics programs and packages. We then examine the need to move to higher complexity multiscale computational applications and the related need to adopt for them on the platform side cloud and grid computing. On this ground we examine also the need for reorganizing. The design of a possible roadmap to establishing a Chemistry Virtual Research Community is then sketched and some examples of Chemistry and Molecular and Materials Science and Technologies prototype applications exploiting the synergy between competences and distributed platforms are illustrated for these applications the middleware and work habits into cooperative schemes and virtual research communities (part of the first draft of this paper has been incorporated in the white paper issued by the Computational Chemistry Division of EUCHEMS in August 2012) INTRODUCTION The computational chemistry (CC) community is made of individuals (academics, affiliated to research institutions and operators of chemistry related companies) carrying out computational research in Chemistry, Molecular and Materials Science and Technology (CMMST). It is to a large extent registered into the CC division (DCC) of the European Chemistry and Molecular Science (EUCHEMS) Society and is connected to other chemistry related organizations operating in Chemical Engineering, Biochemistry, Chemometrics, Omics-sciences, Medicinal chemistry, Forensic chemistry, Food chemistry, etc. -
Author's Personal Copy
Author's personal copy Computer Physics Communications 183 (2012) 2272–2281 Contents lists available at SciVerse ScienceDirect Computer Physics Communications journal homepage: www.elsevier.com/locate/cpc Libxc: A library of exchange and correlation functionals for density functional theory✩ Miguel A.L. Marques a,b,∗, Micael J.T. Oliveira c, Tobias Burnus d a Université de Lyon, F-69000 Lyon, France b LPMCN, CNRS, UMR 5586, Université Lyon 1, F-69622 Villeurbanne, France c Center for Computational Physics, University of Coimbra, Rua Larga, 3004-516 Coimbra, Portugal d Peter Grünberg Institut and Institute for Advanced Simulation, Forschungszentrum Jülich, and Jülich Aachen Research Alliance, 52425 Jülich, Germany a r t i c l e i n f o a b s t r a c t Article history: The central quantity of density functional theory is the so-called exchange–correlation functional. This Received 8 March 2012 quantity encompasses all non-trivial many-body effects of the ground-state and has to be approximated Received in revised form in any practical application of the theory. For the past 50 years, hundreds of such approximations have 7 May 2012 appeared, with many successfully persisting in the electronic structure community and literature. Here, Accepted 8 May 2012 we present a library that contains routines to evaluate many of these functionals (around 180) and their Available online 19 May 2012 derivatives. Keywords: Program summary Density functional theory Program title: LIBXC Density functionals Catalogue identifier: AEMU_v1_0 Local density approximation Generalized gradient approximation Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEMU_v1_0.html Hybrid functionals Program obtainable from: CPC Program Library, Queen’s University, Belfast, N.