Popular GPU-Accelerated Applications
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Supporting Information
Electronic Supplementary Material (ESI) for RSC Advances. This journal is © The Royal Society of Chemistry 2020 Supporting Information How to Select Ionic Liquids as Extracting Agent Systematically? Special Case Study for Extractive Denitrification Process Shurong Gaoa,b,c,*, Jiaxin Jina,b, Masroor Abroc, Ruozhen Songc, Miao Hed, Xiaochun Chenc,* a State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing, 102206, China b Research Center of Engineering Thermophysics, North China Electric Power University, Beijing, 102206, China c Beijing Key Laboratory of Membrane Science and Technology & College of Chemical Engineering, Beijing University of Chemical Technology, Beijing 100029, PR China d Office of Laboratory Safety Administration, Beijing University of Technology, Beijing 100124, China * Corresponding author, Tel./Fax: +86-10-6443-3570, E-mail: [email protected], [email protected] 1 COSMO-RS Computation COSMOtherm allows for simple and efficient processing of large numbers of compounds, i.e., a database of molecular COSMO files; e.g. the COSMObase database. COSMObase is a database of molecular COSMO files available from COSMOlogic GmbH & Co KG. Currently COSMObase consists of over 2000 compounds including a large number of industrial solvents plus a wide variety of common organic compounds. All compounds in COSMObase are indexed by their Chemical Abstracts / Registry Number (CAS/RN), by a trivial name and additionally by their sum formula and molecular weight, allowing a simple identification of the compounds. We obtained the anions and cations of different ILs and the molecular structure of typical N-compounds directly from the COSMObase database in this manuscript. -
CESMIX: Center for the Exascale Simulation of Materials in Extreme Environments
CESMIX: Center for the Exascale Simulation of Materials in Extreme Environments Project Overview Youssef Marzouk MIT PSAAP-3 Team 18 August 2020 The CESMIX team • Our team integrates expertise in quantum chemistry, atomistic simulation, materials science; hypersonic flow; validation & uncertainty quantification; numerical algorithms; parallel computing, programming languages, compilers, and software performance engineering 1 Project objectives • Exascale simulation of materials in extreme environments • In particular: ultrahigh temperature ceramics in hypersonic flows – Complex materials, e.g., metal diborides – Extreme aerothermal and chemical loading – Predict materials degradation and damage (oxidation, melting, ablation), capturing the central role of surfaces and interfaces • New predictive simulation paradigms and new CS tools for the exascale 2 Broad relevance • Intense current interest in reentry vehicles and hypersonic flight – A national priority! – Materials technologies are a key limiting factor • Material properties are of cross-cutting importance: – Oxidation rates – Thermo-mechanical properties: thermal expansion, creep, fracture – Melting and ablation – Void formation • New systems being proposed and fabricated (e.g., metallic high-entropy alloys) • May have relevance to materials aging • Yet extreme environments are largely inaccessible in the laboratory – Predictive simulation is an essential path… 3 Demonstration problem: specifics • Aerosurfaces of a hypersonic vehicle… • Hafnium diboride (HfB2) promises necessary temperature -
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. -
Benchmarking and Application of Density Functional Methods In
BENCHMARKING AND APPLICATION OF DENSITY FUNCTIONAL METHODS IN COMPUTATIONAL CHEMISTRY by BRIAN N. PAPAS (Under Direction the of Henry F. Schaefer III) ABSTRACT Density Functional methods were applied to systems of chemical interest. First, the effects of integration grid quadrature choice upon energy precision were documented. This was done through application of DFT theory as implemented in five standard computational chemistry programs to a subset of the G2/97 test set of molecules. Subsequently, the neutral hydrogen-loss radicals of naphthalene, anthracene, tetracene, and pentacene and their anions where characterized using five standard DFT treatments. The global and local electron affinities were computed for the twelve radicals. The results for the 1- naphthalenyl and 2-naphthalenyl radicals were compared to experiment, and it was found that B3LYP appears to be the most reliable functional for this type of system. For the larger systems the predicted site specific adiabatic electron affinities of the radicals are 1.51 eV (1-anthracenyl), 1.46 eV (2-anthracenyl), 1.68 eV (9-anthracenyl); 1.61 eV (1-tetracenyl), 1.56 eV (2-tetracenyl), 1.82 eV (12-tetracenyl); 1.93 eV (14-pentacenyl), 2.01 eV (13-pentacenyl), 1.68 eV (1-pentacenyl), and 1.63 eV (2-pentacenyl). The global minimum for each radical does not have the same hydrogen removed as the global minimum for the analogous anion. With this in mind, the global (or most preferred site) adiabatic electron affinities are 1.37 eV (naphthalenyl), 1.64 eV (anthracenyl), 1.81 eV (tetracenyl), and 1.97 eV (pentacenyl). In later work, ten (scandium through zinc) homonuclear transition metal trimers were studied using one DFT 2 functional. -
A Summary of ERCAP Survey of the Users of Top Chemistry Codes
A survey of codes and algorithms used in NERSC chemical science allocations Lin-Wang Wang NERSC System Architecture Team Lawrence Berkeley National Laboratory We have analyzed the codes and their usages in the NERSC allocations in chemical science category. This is done mainly based on the ERCAP NERSC allocation data. While the MPP hours are based on actually ERCAP award for each account, the time spent on each code within an account is estimated based on the user’s estimated partition (if the user provided such estimation), or based on an equal partition among the codes within an account (if the user did not provide the partition estimation). Everything is based on 2007 allocation, before the computer time of Franklin machine is allocated. Besides the ERCAP data analysis, we have also conducted a direct user survey via email for a few most heavily used codes. We have received responses from 10 users. The user survey not only provide us with the code usage for MPP hours, more importantly, it provides us with information on how the users use their codes, e.g., on which machine, on how many processors, and how long are their simulations? We have the following observations based on our analysis. (1) There are 48 accounts under chemistry category. This is only second to the material science category. The total MPP allocation for these 48 accounts is 7.7 million hours. This is about 12% of the 66.7 MPP hours annually available for the whole NERSC facility (not accounting Franklin). The allocation is very tight. The majority of the accounts are only awarded less than half of what they requested for. -
Porting the DFT Code CASTEP to Gpgpus
Porting the DFT code CASTEP to GPGPUs Toni Collis [email protected] EPCC, University of Edinburgh CASTEP and GPGPUs Outline • Why are we interested in CASTEP and Density Functional Theory codes. • Brief introduction to CASTEP underlying computational problems. • The OpenACC implementation http://www.nu-fuse.com CASTEP: a DFT code • CASTEP is a commercial and academic software package • Capable of Density Functional Theory (DFT) and plane wave basis set calculations. • Calculates the structure and motions of materials by the use of electronic structure (atom positions are dictated by their electrons). • Modern CASTEP is a re-write of the original serial code, developed by Universities of York, Durham, St. Andrews, Cambridge and Rutherford Labs http://www.nu-fuse.com CASTEP: a DFT code • DFT/ab initio software packages are one of the largest users of HECToR (UK national supercomputing service, based at University of Edinburgh). • Codes such as CASTEP, VASP and CP2K. All involve solving a Hamiltonian to explain the electronic structure. • DFT codes are becoming more complex and with more functionality. http://www.nu-fuse.com HECToR • UK National HPC Service • Currently 30- cabinet Cray XE6 system – 90,112 cores • Each node has – 2×16-core AMD Opterons (2.3GHz Interlagos) – 32 GB memory • Peak of over 800 TF and 90 TB of memory http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) Ab initio codes (VASP, CP2K, CASTEP, ONETEP, NWChem, Quantum Espresso, GAMESS-US, SIESTA, GAMESS-UK, MOLPRO) GS2NEMO ChemShell 2%2% SENGA2% 3% UM Others 4% 34% MITgcm 4% CASTEP 4% GROMACS 6% DL_POLY CP2K VASP 5% 8% 19% http://www.nu-fuse.com HECToR usage statistics Phase 3 statistics (Nov 2011 - Apr 2013) 35% of the Chemistry software on HECToR is using DFT methods. -
The Molpro Quantum Chemistry Package
The Molpro Quantum Chemistry package Hans-Joachim Werner,1, a) Peter J. Knowles,2, b) Frederick R. Manby,3, c) Joshua A. Black,1, d) Klaus Doll,1, e) Andreas Heßelmann,1, f) Daniel Kats,4, g) Andreas K¨ohn,1, h) Tatiana Korona,5, i) David A. Kreplin,1, j) Qianli Ma,1, k) Thomas F. Miller, III,6, l) Alexander Mitrushchenkov,7, m) Kirk A. Peterson,8, n) Iakov Polyak,2, o) 1, p) 2, q) Guntram Rauhut, and Marat Sibaev 1)Institut f¨ur Theoretische Chemie, Universit¨at Stuttgart, Pfaffenwaldring 55, 70569 Stuttgart, Germany 2)School of Chemistry, Cardiff University, Main Building, Park Place, Cardiff CF10 3AT, United Kingdom 3)School of Chemistry, University of Bristol, Cantock’s Close, Bristol BS8 1TS, United Kingdom 4)Max-Planck Institute for Solid State Research, Heisenbergstraße 1, 70569 Stuttgart, Germany 5)Faculty of Chemistry, University of Warsaw, L. Pasteura 1 St., 02-093 Warsaw, Poland 6)Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, United States 7)MSME, Univ Gustave Eiffel, UPEC, CNRS, F-77454, Marne-la- Vall´ee, France 8)Washington State University, Department of Chemistry, Pullman, WA 99164-4630 1 Molpro is a general purpose quantum chemistry software package with a long devel- opment history. It was originally focused on accurate wavefunction calculations for small molecules, but now has many additional distinctive capabilities that include, inter alia, local correlation approximations combined with explicit correlation, highly efficient implementations of single-reference correlation methods, robust and efficient multireference methods for large molecules, projection embedding and anharmonic vibrational spectra. -
POLISH SUPERCOMPUTING CENTERS (WCSS – Wrocław, ICM – Warsaw, CYFRONET - Kraków, TASK – Gdańsk, PCSS – Poznań)
POLISH SUPERCOMPUTING CENTERS (WCSS – Wrocław, ICM – Warsaw, CYFRONET - Kraków, TASK – Gdańsk, PCSS – Poznań) Provide free access to supercomputers and licensed software for all academic users in form of computational grants. In order to obtain access Your supervisor has to write grant application and acknowledge use of supercomputer resources in published papers. 1) Wrocław Networking and Supercomputing Center Wrocławskie Centrum Sieciowo-Superkomputerowe https://www.wcss.pl/ • Klaster Bem - najnowszy superkomputer WCSS, posiadający 22 tyś. rdzeni obliczeniowych o łącznej mocy 860 TFLOPS. Klaster liczy 720 węzłów obliczeniowych 24-rdzeniowych (Intel Xeon E5-2670 v3 2.3 GHz, Haswell) oraz 192 węzły obliczeniowe 28-rdzeniowe (Intel Xeon E5- 2697 v3 2.6 GHz, Haswell). Posiada 74,6 TB pamięci RAM (64, 128 lub 512 GB/węzeł) (złóż wniosek o grant) • Klaster kampusowy - klaster włączony w struktury projektu PLATON U3, świadczący usługi aplikacji na żądanie. Klaster liczy 48 węzłów, w tym 16 wyposażonych w karty NVIDIA Quadro FX 580. 8688 rdzeni obliczeniowych 46 TB pamięci RAM 8 PB przestrzeni dyskowej • Available software Abaqus ⋅ ABINIT ⋅ ADF ⋅ Amber ⋅ ANSYS [ ANSYS CFD: Fluent, CFX, ICEM; Mechanical ] ⋅ AutoDock ⋅ BAGEL ⋅ Beast ⋅ Biovia [ Materials Studio, Discovery Studio ] ⋅ Cfour ⋅ Comsol ⋅ CP2K ⋅ CPMD ⋅ CRYSTAL09 ⋅ Dalton ⋅ DIRAC ⋅ FDS-SMV ⋅ GAMESS ⋅ Gaussian ⋅ Gromacs ⋅ IDL ⋅ Lumerical [ FDTD, MODE ] ⋅ Maple ⋅ Mathcad ⋅ Mathematica⋅ Matlab ⋅ Molcas ⋅ Molden ⋅ Molpro ⋅ MOPAC ⋅ NAMD ⋅ NBO ⋅ NWChem ⋅ OpenFOAM ⋅ OpenMolcas ⋅ Orca ⋅ Quantum -
Düzce University Journal of Science & Technology
Düzce University Journal of Science & Technology, 9 (2021) 1227-1241 Düzce University Journal of Science & Technology Research Article Detonation Parameters of the Pentaerythritol Tetranitrate and Some Structures Descriptors in Different Solvents - Computational Study Cihat HİLAL a,*, Müşerref ÖNAL b , Mehmet Erman MERT c a,b Department of Chemistry, Faculty of Sciences, Ankara University, Ankara, TURKEY c Advanced Technology Research and Application Center, Adana Alparslan Türkeş Science and Technology University, Adana, TURKEY * Corresponding author’s e-mail address: [email protected] DOI: 10.29130/dubited.896332 ABSTRACT Pentaerythritol tetranitrate (PETN, C5H8N4O12) is a relatively stable explosive nitrate ester molecule. It has been widely used in various military and public industrial productions. In this study, the solubility tendency of PETN in different organic solvents was investigated theoretically. Several physicochemical parameters of PETN such as density, detonation pressure, temperature, rate and products of detonation reaction were investigated using the B3LYP functional and basic set of polarization functions (d, p) containing 6-31G**. The obtained results have been compared with the literature values. Furthermore, the stability and reactivity of PETN in acetone, diethyl ether, ethanol, tetrahydrofuran, toluene and methylene chloride were examined. Results revealed toluene is a good solvent to increase the explosive properties of PETN. Keywords: Density Functional Theory, Organic Solvents, PETN Pentaeritritol Tetranitratın -
Ab Initio Theoretical Chemical Kinetics
Ab Initio Theoretical Chemi ca l Kine tics CEFRC Summer School Princeton June, 2010 Stephen J. Klippenstein Ab Initio Theoretical Chemical Kinetics Stephen J. Klippenstein James A. Miller Lawrence B. Harding Yuri Georgievskii Outline 1. Overview of Ab Initio Transition State Theory a) Transition State Theory (TST) b) Introduction to Electronic Structure Theory c) Multireference Electronic Structure Theory 2. Ab Initio TST for Different Classes of Reaction a) Tight TST for Reactions with Barriers b) TST for Radical-Radical Reactions c) Multiple Transition States and Dynamics 3. Pressure Dependent Kinetics a) Single Well Reactions b) Multiple Well Time Dependent Master Equation c) Examples Review Articles The Current Status of Transition State Theory Donald G. Truhlar, Bruce C. Garrett, and Stephen J. Klippenstein J. Phys. Chem. (Centennial Issue) 100, 12771-12800 (1996). RRKM Theory and Its Implementation Stephen J. Klippenstein, in Comprehensive Chemical Kinetics, 39, 55, (2003). Modeling the Kinetics of Bimolecular Reactions Antonio Fernandez Ramos, James A. Miller, Stephen J. Klippenstein, and Donald G. Truhlar, Chem. Rev., 106, 4518-4584 (2006). Predictive Theory for Hydrogen Atom-Hydrocarbon Radical Association Kinetics Lawrence B. Harding, Yuri Georgievskii, and Stephen J. Klippenstein J. Phys. Chem. A, feature article, 109, 4646-4656 (2005). Predictive Theory for the Association Kinetics of Two Alkyl Radicals Stephen J. Klippenstein, Yuri Georgievskii, and Lawrence B. Harding Phys. Chem. Chem. Phys., invited article, 8, 1133-1147 (2006). Master Equation Methods in Gas Phase Chemical Kinetics James A. Miller and Stephen J. Klippenstein J. Phys. Chem. A, feature article, 110, 10528-10544 (2006). Ab Initio Methods for Reactive Potential Surfaces Lawrence B. -
1 Advances in Electronic Structure Theory: Gamess a Decade Later Mark S. Gordon and Michael W. Schmidt Department of Chemistry A
ADVANCES IN ELECTRONIC STRUCTURE THEORY: GAMESS A DECADE LATER MARK S. GORDON AND MICHAEL W. SCHMIDT DEPARTMENT OF CHEMISTRY AND AMES LABORATORY, IOWA STATE UNIVERSITY, AMES, IA 50011 Abstract. Recent developments in advanced quantum chemistry and quantum chemistry interfaced with model potentials are discussed, with the primary focus on new implementations in the GAMESS electronic structure suite of programs. Applications to solvent effects and surface science are discussed. 1. Introduction The past decade has seen an extraordinary growth in novel new electronic structure methods and creative implementations of these methods. Concurrently, there have been important advances in “middleware”, software that enables the implementation of efficient electronic structure algorithms. Combined with continuing improvements in computer and interconnect hardware, these advances have extended both the accuracy of computations and the sizes of molecular systems to which such methods may be applied. The great majority of the new computational chemistry algorithms have found their way into one or more broadly distributed electronic structure packages. Since this work focuses on new features of the GAMESS (General Atomic and Molecular Electronic Structure System1) suite of codes, it is important at the outset to recognize the many other packages that offer both similar and complementary features. These include ACES2 CADPAC3, DALTON4, GAMESS-UK5, HYPERCHEM6, JAGUAR7, MOLCAS8 MOLPRO9, NWChem10, PQS11, PSI312, Q-Chem13, SPARTAN14, TURBOMOLE15, and UT-Chem16. Some1,3,4,10 of these packages are distributed at no cost to all, or to academic users, while others are commercial packages, but all are generally available to users without restriction or constraint. Indeed, the developers of these codes frequently collaborate to share features, a practice that clearly benefits all of their users. -
Openmolcas: from Source Code to Insight Ignacio Fdez
Article Cite This: J. Chem. Theory Comput. XXXX, XXX, XXX−XXX pubs.acs.org/JCTC OpenMolcas: From Source Code to Insight Ignacio Fdez. Galvan,́ 1,2 Morgane Vacher,1 Ali Alavi,3 Celestino Angeli,4 Francesco Aquilante,5 Jochen Autschbach,6 Jie J. Bao,7 Sergey I. Bokarev,8 Nikolay A. Bogdanov,3 Rebecca K. Carlson,7,9 Liviu F. Chibotaru,10 Joel Creutzberg,11,12 Nike Dattani,13 Mickael̈ G. Delcey,1 Sijia S. Dong,7,14 Andreas Dreuw,15 Leon Freitag,16 Luis Manuel Frutos,17 Laura Gagliardi,7 Fredé rić Gendron,6 Angelo Giussani,18,19 Leticia Gonzalez,́ 20 Gilbert Grell,8 Meiyuan Guo,1,21 Chad E. Hoyer,7,22 Marcus Johansson,12 Sebastian Keller,16 Stefan Knecht,16 Goran Kovacevič ,́ 23 Erik Kallman,̈ 1 Giovanni Li Manni,3 Marcus Lundberg,1 Yingjin Ma,16 Sebastian Mai,20 Joaõ Pedro Malhado,24 Per Åke Malmqvist,12 Philipp Marquetand,20 Stefanie A. Mewes,15,25 Jesper Norell,11 Massimo Olivucci,26,27,28 Markus Oppel,20 Quan Manh Phung,29 Kristine Pierloot,29 Felix Plasser,30 Markus Reiher,16 Andrew M. Sand,7,31 Igor Schapiro,32 Prachi Sharma,7 Christopher J. Stein,16,33 Lasse Kragh Sørensen,1,34 Donald G. Truhlar,7 Mihkel Ugandi,1,35 Liviu Ungur,36 Alessio Valentini,37 Steven Vancoillie,12 Valera Veryazov,12 Oskar Weser,3 Tomasz A. Wesołowski,5 Per-Olof Widmark,12 Sebastian Wouters,38 Alexander Zech,5 J. Patrick Zobel,12 and Roland Lindh*,2,39 1Department of Chemistry − Ångström Laboratory, Uppsala University, P.O.