High- Performance Computing Ground-Breaking Knowledge and Technology
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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. -
Curriculum Vitae Bruce M
Curriculum Vitae Bruce M. Boghosian Professor and Chair of Mathematics Tufts University 211 Bromfield-Pearson Hall, Tufts University Medford, Massachusetts 02155, U.S.A. [email protected] (617) 627-3054 (direct), (617) 627-3966 (fax) Employment History • Tufts University, Medford, MA: Professor and Chair, Department of Mathematics (2000 – present, promoted to rank of Professor in 2003, promoted to Chair in 2006); Adjunct Professor, Department of Computer Science (2003 – present). • Boston University, Boston, MA: Research Associate Professor, Center for Computational Science and Depart- ment of Physics (1994 – 2003). • Thinking Machines Corporation, Cambridge, MA: Senior Scientist, Mathematical Sciences Research Group (1986 – 1994). • Lawrence Livermore National Laboratory, Livermore, CA: Physicist, Plasma Theory Group (1978 – 1986). Visiting Positions • Ecole´ Normale Superieure´ , Paris, France: Visiting Researcher (7 April – 7 May 2008). • Peking University, Beijing, China: Visiting Professor, School of Engineering, gave half-semester course enti- tled “Topological Fluid Dynamics” (5 November – 12 December 2007). • University College London: EPSRC Visiting Fellow, Centre for Computational Science, Department of Chem- istry (2002-present). • University of California, Berkeley: Visiting Professor, Department of Physics (1996 – 1997). • International Centre for Theoretical Physics, Trieste, Italy: Visiting Scientist, Condensed Matter Division (Summer, 1996). • Schlumberger Cambridge Research Centre, Cambridge, UK: Consultant (1994 -
The Impact of Density Functional Theory on Materials Research
www.mrs.org/bulletin functional. This functional (i.e., a function whose argument is another function) de- scribes the complex kinetic and energetic interactions of an electron with other elec- Toward Computational trons. Although the form of this functional that would make the reformulation of the many-body Schrödinger equation exact is Materials Design: unknown, approximate functionals have proven highly successful in describing many material properties. Efficient algorithms devised for solving The Impact of the Kohn–Sham equations have been imple- mented in increasingly sophisticated codes, tremendously boosting the application of DFT methods. New doors are opening to in- Density Functional novative research on materials across phys- ics, chemistry, materials science, surface science, and nanotechnology, and extend- ing even to earth sciences and molecular Theory on Materials biology. The impact of this fascinating de- velopment has not been restricted to aca- demia, as DFT techniques also find application in many different areas of in- Research dustrial research. The development is so fast that many current applications could Jürgen Hafner, Christopher Wolverton, and not have been realized three years ago and were hardly dreamed of five years ago. Gerbrand Ceder, Guest Editors The articles collected in this issue of MRS Bulletin present a few of these suc- cess stories. However, even if the compu- Abstract tational tools necessary for performing The development of modern materials science has led to a growing need to complex quantum-mechanical calcula- tions relevant to real materials problems understand the phenomena determining the properties of materials and processes on are now readily available, designing a an atomistic level. -
Practice: Quantum ESPRESSO I
MODULE 2: QUANTUM MECHANICS Practice: Quantum ESPRESSO I. What is Quantum ESPRESSO? 2 DFT software PW-DFT, PP, US-PP, PAW FREE http://www.quantum-espresso.org PW-DFT, PP, PAW FREE http://www.abinit.org DFT PW, PP, Car-Parrinello FREE http://www.cpmd.org DFT PP, US-PP, PAW $3000 [moderate accuracy, fast] http://www.vasp.at DFT full-potential linearized augmented $500 plane-wave (FLAPW) [accurate, slow] http://www.wien2k.at Hartree-Fock, higher order correlated $3000 electron approaches http://www.gaussian.com 3 Quantum ESPRESSO 4 Quantum ESPRESSO Quantum ESPRESSO is an integrated suite of Open- Source computer codes for electronic-structure calculations and materials modeling at the nanoscale. It is based on density-functional theory, plane waves, and pseudopotentials. Core set of codes, plugins for more advanced tasks and third party packages Open initiative coordinated by the Quantum ESPRESSO Foundation, across Italy. Contributed to by developers across the world Regular hands-on workshops in Trieste, Italy Open-source code: FREE (unlike VASP...) 5 Performance Small jobs (a few atoms) can be run on single node Includes determining convergence parameters, lattice constants Can use OpenMP parallelization on multicore machines Large jobs (~10’s to ~100’s atoms) can run in parallel using MPI to 1000’s of cores Includes molecular dynamics, large geometry relaxation, phonons Parallel performance tied to BLAS/LAPACK (linear algebra routines) and 3D FFT (fast Fourier transform) New GPU-enabled version available 6 Usability Documented online: -
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. -
Author List Poster # Day Title
CSE19 Poster Board Assignments Alphabetical by Presenter Author List Poster # Day Session Title (italics indicates presenter) Joshua R. Abrams , University of Arizona, U.S.; José Celaya‐Alcalá, Minisymposterium: Analysis of Equity Markets ‐ A Graph Theory Wednesday PP204 Brown University, U.S. 205 Approach Scott Aiton , Donna Calhoun, and Grady B. Wright, Boise State Minisymposterium: A Massively Parallel Solver for Poisson’s Equation Wednesday PP202 University, U.S. 197 on Block Structured Cartesian Grids Nissrine Akkari , Fabien Casenave, and Christian Rey, SAFRAN, Tuesday PP1 Design Exploration of Fuel Injectors Based on Reduced Order Modeling France; Vincent R. Moureau, CORIA, France 1 Said Algarni , King Fahd University of Petroleum and Minerals, Pad\'e Time Stepping Method of Rational Form for Reaction Diffusion Wednesday PP2 Saudi Arabia 2 Equations Jeffery M. Allen , Justin Chang, Francois Usseglio‐Viretta, and Parallel Implementation of a Monolithic Li‐ion Battery Model using Tuesday PP1 Peter Graf, National Renewable Energy Laboratory, U.S. 3 Fenics Matea Alvarado , University of California, Merced, U.S.; Johannes Minisymposterium: Modeling the Chemistry and Hydrodynamics of Wednesday PP201 P. Blaschke, Lawrence Berkeley National Laboratory, U.S. 189 Micro‐swimmers Minisymposterium: Modelling In‐crib Drying of eAr Maize‐ A Case Mercy Amankway, Montana State University 136 Tuesday PP102 Study of Sunyani‐West District Ilona Ambartsumyan , Tan Bui‐Thanh, Omar Ghattas, and Eldar An Edge‐preserving Method for Joint Bayesian Inversion with Non‐ Tuesday PP1 Khattatov, University of Texas at Austin, U.S. 4 Gaussian Priors Sparse Approximate Matrix Multiplication in a Fully Recursive Anton G. Artemov , Uppsala University, Sweden Wednesday PP2 5 Distributed Task‐based Parallel Framework Ibrahim H. -
Final Training and Dissemination Report
D3.6 Final Training and Dissemination Report Grant agreement no. 675451 CompBioMed Research and Innovation Action H2020-EINFRA-2015-1 Topic: Centres of Excellence for Computing Applications D3.6 Final Training and Dissemination Report Work Package: 3 Due date of deliverable: Month 36 Actual submission date: 30 September 2019 Start date of project: 01 October 2016 Duration: 36 months Lead beneficiary for this deliverable: UvA Contributors: CBK, UCL Project co-funded by the European Commission within the H2020 Programme (2014-2020) Dissemination Level PU Public YES CO Confidential, only for members of the consortium (including the Commission Services) CI Classified, as referred to in Commission Decision 2001/844/EC 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. The user, therefore, uses the information at its sole risk and liability. For the avoidance of all doubts, the European Commission has no liability in respect of this document, which is merely representing the authors’ view. PU Page 1 Version 0.3 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Grant Agreement No 675451 D3.6 Final Training and Dissemination Report Table of Contents 1 Version Log .......................................................................................................................... -
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. -
Quantum Chemistry (QC) on Gpus Feb
Quantum Chemistry (QC) on GPUs Feb. 2, 2017 Overview of Life & Material Accelerated Apps MD: All key codes are GPU-accelerated QC: All key codes are ported or optimizing Great multi-GPU performance Focus on using GPU-accelerated math libraries, OpenACC directives Focus on dense (up to 16) GPU nodes &/or large # of GPU nodes GPU-accelerated and available today: ACEMD*, AMBER (PMEMD)*, BAND, CHARMM, DESMOND, ESPResso, ABINIT, ACES III, ADF, BigDFT, CP2K, GAMESS, GAMESS- Folding@Home, GPUgrid.net, GROMACS, HALMD, HOOMD-Blue*, UK, GPAW, LATTE, LSDalton, LSMS, MOLCAS, MOPAC2012, LAMMPS, Lattice Microbes*, mdcore, MELD, miniMD, NAMD, NWChem, OCTOPUS*, PEtot, QUICK, Q-Chem, QMCPack, OpenMM, PolyFTS, SOP-GPU* & more Quantum Espresso/PWscf, QUICK, TeraChem* Active GPU acceleration projects: CASTEP, GAMESS, Gaussian, ONETEP, Quantum Supercharger Library*, VASP & more green* = application where >90% of the workload is on GPU 2 MD vs. QC on GPUs “Classical” Molecular Dynamics Quantum Chemistry (MO, PW, DFT, Semi-Emp) Simulates positions of atoms over time; Calculates electronic properties; chemical-biological or ground state, excited states, spectral properties, chemical-material behaviors making/breaking bonds, physical properties Forces calculated from simple empirical formulas Forces derived from electron wave function (bond rearrangement generally forbidden) (bond rearrangement OK, e.g., bond energies) Up to millions of atoms Up to a few thousand atoms Solvent included without difficulty Generally in a vacuum but if needed, solvent treated classically -
CPMD Tutorial Car–Parrinello Molecular Dynamics
CPMD Tutorial Car–Parrinello Molecular Dynamics Ari P Seitsonen CNRS & Universit´ePierre et Marie Curie, Paris CSC, October 2006 Introduction CPMD — concept 1 • Car-Parrinello Molecular Dynamics – Roberto Car & Michele Parrinello, Physical Review Letters 55, 2471 (1985) — 20+1 years of Car-Parrinello method! Roberto Car Michele Parrinello CPMD — concept 2 • CPMD code — http://www.cpmd.org/; based on the original code of Roberto Car and Michele Parrinello CPMD What is it, what is it not? • Computer code for performing static, dynamic simulations and analysis of electronic structure • About 200’000 lines of code — FORTRAN77 with a flavour towards F90 • (Freely) available via http://www.cpmd.org/ with source code • Most suitable for dynamical simulations of condensed systems or large molecules — less for small molecules or bulk properties of small crystals • Computationally highly optimised for vector and scalar supercomputers, shared and distributed memory machines and combinations thereof (SMP) Capabilities • Solution of electronic and ionic structure • XC-functional: LDA, GGA, meta-GGA, hybrid • Molecular dynamics in NVE, NVT, NPT ensembles • General constraints (MD and geometry optimisation) • Metadynamics ‡ • Free energy functional ‡ • Path integrals ‡ • QM/MM ‡ • Wannier functions • Response properties: TDDFT, NMR, Raman, IR, . • Norm conserving and ultra-soft pseudo potentials ‡ : Will not be considered during this course CPMD: Examples Amorphous Si CPMD: Examples Amorphous Si CPMD: Examples Liquid water: Solvation and transport of -
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. -
Computational Biomedicine: a Challenge for the 21St Century
Computational Biomedicine: A Challenge for the 21st Century Peter V Coveney Centre for Computational Science University College London [email protected] NSR-RPI workshop, Monday September 12 2011 Overview • The UCL Centre for Computational Science • Computational Biomedicine Case Studies • Medical Data Storage and Security issues • The Virtual Physiological Human initiative • EU e-infrastructure projects addressing large scale biomedical data processing 2 Centre for Computational Science: “Advancing science through computers” Computation/computational science/computer science is central to modern science. Computational infrastructure both empowers and circumscribes much of what is scientifically achievable. serial P. V. Coveney and R. R. Highfield, Frontiers of Complexity (1995) parallel grid computing Internet Web Grid 3 Centre for Computational Science Advancing science through computers • Computational Science • Algorithms, code development & implementation • High performance, data-intensive & distributed computing • Visualisation & computational steering • Condensed matter physics & chemistry, materials & life sciences • Translational medicine, e-Health & VPH – “data deluge”4 Overview • The UCL Centre for Computational Science • Computational Biomedicine Case Studies • Medical Data Storage and Security issues • The Virtual Physiological Human initiative • EU e-infrastructure projects addressing large scale biomedical data processing 5 Case study I : Grid Enabled Neurosurgical Imaging Using Simulation The GENIUS project aims to model large scale patient specific cerebral blood flow in clinically relevant time frames Objectives: • To study cerebral blood flow using patient-specific image-based models. • To provide insights into the cerebral blood flow & anomalies. • To develop tools and policies by means of which users can better exploit the ability to reserve and co-reserve HPC resources. • To develop interfaces which permit users to easily deploy and monitor simulations across multiple computational resources.