Performance Analysis of the AMD EPYC Rome Processors
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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 -
Olaseni Sode
Olaseni Sode Work Address: 5735 S. Ellis Ave. [email protected] Searle 231 +1 (314) 856-2373 The University of Chicago Chicago, IL 60615 Home Address: 1119 E Hyde Park Blvd [email protected] Apt. 3E +1 (314) 856-2373 Chicago, IL 60615 http://www.olaseniosode.com Education University of Illinois at Urbana-Champaign Urbana, IL Ph.D. in Chemistry September 2012 – Supervisor: Professor So Hirata – Thesis title: A Theoretical Study of Molecular Crystals – Transferred from the University of Florida in 2010 with Prof. Hirata Morehouse College Atlanta, GA B.S. & B.A. in Chemistry and French December 2006 – Studied at the Université de Nantes, Nantes, France (2004-2005) Research Experience The University of Chicago Chicago, IL Postdoctoral Research – research advisor: Dr. Gregory A. Voth 2012–present – Studied proposed proton transport minimum free-energy pathways in [FeFe]-hydrogenase via molecular dynamics simulations and the multistate empirical valence bond technique. – Collaborated to develop the multiscale string method to accelerate refinement of chemical reaction pathways of adenosine triphosphate (ATP) hydrolysis in globular and filamentous actin proteins. University of California, San Diego La Jolla, CA Visiting Scholar – research advisor: Dr. Francesco Paesani 2014, 2015 – Developed and implemented a reactive molecular dynamics scheme for condensed phase systems of the hydrated hydronium ions. University of Illinois at Urbana-Champaign Urbana, IL Doctoral Research – research advisor: Dr. So Hirata 2010-2012 – Characterized the vibrational structure of extended systems including infrared and Raman-active vibrations, as well as phonon dispersions and densities of states in the first Brillouin zone. – Developed zero-point vibrational energy analysis scheme for molecular clusters using a fragmented, local basis scheme. -
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. -
Pysimm: a Python Package for Simulation of Molecular Systems
SoftwareX 6 (2016) 7–12 Contents lists available at ScienceDirect SoftwareX journal homepage: www.elsevier.com/locate/softx pysimm: A python package for simulation of molecular systems Michael E. Fortunato a, Coray M. Colina a,b,∗ a Department of Chemistry, University of Florida, Gainesville, FL 32611, United States b Department of Materials Science and Engineering and Nuclear Engineering, University of Florida, Gainesville, FL 32611, United States article info a b s t r a c t Article history: In this work, we present pysimm, a python package designed to facilitate structure generation, simulation, Received 15 August 2016 and modification of molecular systems. pysimm provides a collection of simulation tools and smooth Received in revised form integration with highly optimized third party software. Abstraction layers enable a standardized 1 December 2016 methodology to assign various force field models to molecular systems and perform simple simulations. Accepted 5 December 2016 These features have allowed pysimm to aid the rapid development of new applications specifically in the area of amorphous polymer simulations. Keywords: ' 2016 The Authors. Published by Elsevier B.V. Amorphous polymers Molecular simulation This is an open access article under the CC BY license Python (http://creativecommons.org/licenses/by/4.0/). Code metadata Current code version v0.1 Permanent link to code/repository used for this code version https://github.com/ElsevierSoftwareX/SOFTX-D-16-00070 Legal Code License MIT Code versioning system used git Software code languages, tools, and services used python2.7 Compilation requirements, operating environments & Linux dependencies If available Link to developer documentation/manual http://pysimm.org/documentation/ Support email for questions [email protected] 1. -
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. -
GROMACS: Fast, Flexible, and Free
GROMACS: Fast, Flexible, and Free DAVID VAN DER SPOEL,1 ERIK LINDAHL,2 BERK HESS,3 GERRIT GROENHOF,4 ALAN E. MARK,4 HERMAN J. C. BERENDSEN4 1Department of Cell and Molecular Biology, Uppsala University, Husargatan 3, Box 596, S-75124 Uppsala, Sweden 2Stockholm Bioinformatics Center, SCFAB, Stockholm University, SE-10691 Stockholm, Sweden 3Max-Planck Institut fu¨r Polymerforschung, Ackermannweg 10, D-55128 Mainz, Germany 4Groningen Biomolecular Sciences and Biotechnology Institute, University of Groningen, Nijenborgh 4, NL-9747 AG Groningen, The Netherlands Received 12 February 2005; Accepted 18 March 2005 DOI 10.1002/jcc.20291 Published online in Wiley InterScience (www.interscience.wiley.com). Abstract: This article describes the software suite GROMACS (Groningen MAchine for Chemical Simulation) that was developed at the University of Groningen, The Netherlands, in the early 1990s. The software, written in ANSI C, originates from a parallel hardware project, and is well suited for parallelization on processor clusters. By careful optimization of neighbor searching and of inner loop performance, GROMACS is a very fast program for molecular dynamics simulation. It does not have a force field of its own, but is compatible with GROMOS, OPLS, AMBER, and ENCAD force fields. In addition, it can handle polarizable shell models and flexible constraints. The program is versatile, as force routines can be added by the user, tabulated functions can be specified, and analyses can be easily customized. Nonequilibrium dynamics and free energy determinations are incorporated. Interfaces with popular quantum-chemical packages (MOPAC, GAMES-UK, GAUSSIAN) are provided to perform mixed MM/QM simula- tions. The package includes about 100 utility and analysis programs. -
IBM Power System POWER8 Facts and Features
IBM Power Systems IBM Power System POWER8 Facts and Features April 29, 2014 IBM Power Systems™ servers and IBM BladeCenter® blade servers using IBM POWER7® and POWER7+® processors are described in a separate Facts and Features report dated July 2013 (POB03022-USEN-28). IBM Power Systems™ servers and IBM BladeCenter® blade servers using IBM POWER6® and POWER6+™ processors are described in a separate Facts and Features report dated April 2010 (POB03004-USEN-14). 1 IBM Power Systems Table of Contents IBM Power System S812L 4 IBM Power System S822 and IBM Power System S822L 5 IBM Power System S814 and IBM Power System S824 6 System Unit Details 7 Server I/O Drawers & Attachment 8 Physical Planning Characteristics 9 Warranty / Installation 10 Power Systems Software Support 11 Performance Notes & More Information 12 These notes apply to the description tables for the pages which follow: Y Standard / Supported Optional Optionally Available / Supported N/A or - Not Available / Supported or Not Applicable SOD Statement of General Direction announced SLES SUSE Linux Enterprise Server RHEL Red Hat Enterprise Linux a One x8 PCIe slots must contain a 4-port 1Gb Ethernet LAN available for client use b Use of expanded function storage backplane uses one PCIe slot Backplane provides dual high performance SAS controllers with 1.8 GB write cache expanded up to 7.2 GB with c compression plus Easy Tier function plus two SAS ports for running an EXP24S drawer d Full benchmark results are located at ibm.com/systems/power/hardware/reports/system_perf.html e Option is supported on IBM i only through VIOS. -
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 -
Real-Time Pymol Visualization for Rosetta and Pyrosetta
Real-Time PyMOL Visualization for Rosetta and PyRosetta Evan H. Baugh1, Sergey Lyskov1, Brian D. Weitzner1, Jeffrey J. Gray1,2* 1 Department of Chemical and Biomolecular Engineering, The Johns Hopkins University, Baltimore, Maryland, United States of America, 2 Program in Molecular Biophysics, The Johns Hopkins University, Baltimore, Maryland, United States of America Abstract Computational structure prediction and design of proteins and protein-protein complexes have long been inaccessible to those not directly involved in the field. A key missing component has been the ability to visualize the progress of calculations to better understand them. Rosetta is one simulation suite that would benefit from a robust real-time visualization solution. Several tools exist for the sole purpose of visualizing biomolecules; one of the most popular tools, PyMOL (Schro¨dinger), is a powerful, highly extensible, user friendly, and attractive package. Integrating Rosetta and PyMOL directly has many technical and logistical obstacles inhibiting usage. To circumvent these issues, we developed a novel solution based on transmitting biomolecular structure and energy information via UDP sockets. Rosetta and PyMOL run as separate processes, thereby avoiding many technical obstacles while visualizing information on-demand in real-time. When Rosetta detects changes in the structure of a protein, new coordinates are sent over a UDP network socket to a PyMOL instance running a UDP socket listener. PyMOL then interprets and displays the molecule. This implementation also allows remote execution of Rosetta. When combined with PyRosetta, this visualization solution provides an interactive environment for protein structure prediction and design. Citation: Baugh EH, Lyskov S, Weitzner BD, Gray JJ (2011) Real-Time PyMOL Visualization for Rosetta and PyRosetta. -
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. -
A Study on Cheminformatics and Its Applications on Modern Drug Discovery
Available online at www.sciencedirect.com Procedia Engineering 38 ( 2012 ) 1264 – 1275 Internatio na l Conference on Modeling Optimisatio n and Computing (ICMOC 2012) A Study on Cheminformatics and its Applications on Modern Drug Discovery B.Firdaus Begama and Dr. J.Satheesh Kumarb aResearch Scholar, Bharathiar University, Coimbatore, India, [email protected] bAssistant Professor, Bharathiar University, Coimbatore, India, [email protected] Abstract Discovering drugs to a disease is still a challenging task for medical researchers due to the complex structures of biomolecules which are responsible for disease such as AIDS, Cancer, Autism, Alzimear etc. Design and development of new efficient anti-drugs for the disease without any side effects are becoming mandatory in the recent history of human life cycle due to changes in various factors which includes food habit, environmental and migration in human life style. Cheminformaticds deals with discovering drugs based in modern drug discovery techniques which in turn rectifies complex issues in traditional drug discovery system. Cheminformatics tools, helps medical chemist for better understanding of complex structures of chemical compounds. Cheminformatics is a new emerging interdisciplinary field which primarily aims to discover Novel Chemical Entities [NCE] which ultimately results in design of new molecule [chemical data]. It also plays an important role for collecting, storing and analysing the chemical data. This paper focuses on cheminformatics and its applications on drug discovery and modern drug discovery techniques which helps chemist and medical researchers for finding solution to the complex disease. © 2012 Published by Elsevier Ltd. Selection and/or peer-review under responsibility of Noorul Islam Centre for Higher Education. -
Chem Compute Quickstart
Chem Compute Quickstart Chem Compute is maintained by Mark Perri at Sonoma State University and hosted on Jetstream at Indiana University. The Chem Compute URL is https://chemcompute.org/. We will use Chem Compute as the frontend for running electronic structure calculations with The General Atomic and Molecular Electronic Structure System, GAMESS (http://www.msg.ameslab.gov/gamess/). Chem Compute also provides access to other computational chemistry resources including PSI4 and the molecular dynamics packages TINKER and NAMD, though we will not be using those resource at this time. Follow this link, https://chemcompute.org/gamess/submit, to directly access the Chem Compute GAMESS guided submission interface. If you are a returning Chem Computer user, please log in now. If your University is part of the InCommon Federation you can log in without registering by clicking Login then "Log In with Google or your University" – select your University from the dropdown list. Otherwise if this is your first time working with Chem Compute, please register as a Chem Compute user by clicking the “Register” link in the top-right corner of the page. This gives you access to all of the computational resources available at ChemCompute.org and will allow you to maintain copies of your calculations in your user “Dashboard” that you can refer to later. Registering also helps track usage and obtain the resources needed to continue providing its service. When logged in with the GAMESS-Submit tabs selected, an instruction section appears on the left side of the page with instructions for several different kinds of calculations.