On the Feasibility of FPGA Acceleration of Molecular Dynamics Simulations
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Open Babel Documentation Release 2.3.1
Open Babel Documentation Release 2.3.1 Geoffrey R Hutchison Chris Morley Craig James Chris Swain Hans De Winter Tim Vandermeersch Noel M O’Boyle (Ed.) December 05, 2011 Contents 1 Introduction 3 1.1 Goals of the Open Babel project ..................................... 3 1.2 Frequently Asked Questions ....................................... 4 1.3 Thanks .................................................. 7 2 Install Open Babel 9 2.1 Install a binary package ......................................... 9 2.2 Compiling Open Babel .......................................... 9 3 obabel and babel - Convert, Filter and Manipulate Chemical Data 17 3.1 Synopsis ................................................. 17 3.2 Options .................................................. 17 3.3 Examples ................................................. 19 3.4 Differences between babel and obabel .................................. 21 3.5 Format Options .............................................. 22 3.6 Append property values to the title .................................... 22 3.7 Filtering molecules from a multimolecule file .............................. 22 3.8 Substructure and similarity searching .................................. 25 3.9 Sorting molecules ............................................ 25 3.10 Remove duplicate molecules ....................................... 25 3.11 Aliases for chemical groups ....................................... 26 4 The Open Babel GUI 29 4.1 Basic operation .............................................. 29 4.2 Options ................................................. -
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. -
A Superscalar Out-Of-Order X86 Soft Processor for FPGA
A Superscalar Out-of-Order x86 Soft Processor for FPGA Henry Wong University of Toronto, Intel [email protected] June 5, 2019 Stanford University EE380 1 Hi! ● CPU architect, Intel Hillsboro ● Ph.D., University of Toronto ● Today: x86 OoO processor for FPGA (Ph.D. work) – Motivation – High-level design and results – Microarchitecture details and some circuits 2 FPGA: Field-Programmable Gate Array ● Is a digital circuit (logic gates and wires) ● Is field-programmable (at power-on, not in the fab) ● Pre-fab everything you’ll ever need – 20x area, 20x delay cost – Circuit building blocks are somewhat bigger than logic gates 6-LUT6-LUT 6-LUT6-LUT 3 6-LUT 6-LUT FPGA: Field-Programmable Gate Array ● Is a digital circuit (logic gates and wires) ● Is field-programmable (at power-on, not in the fab) ● Pre-fab everything you’ll ever need – 20x area, 20x delay cost – Circuit building blocks are somewhat bigger than logic gates 6-LUT 6-LUT 6-LUT 6-LUT 4 6-LUT 6-LUT FPGA Soft Processors ● FPGA systems often have software components – Often running on a soft processor ● Need more performance? – Parallel code and hardware accelerators need effort – Less effort if soft processors got faster 5 FPGA Soft Processors ● FPGA systems often have software components – Often running on a soft processor ● Need more performance? – Parallel code and hardware accelerators need effort – Less effort if soft processors got faster 6 FPGA Soft Processors ● FPGA systems often have software components – Often running on a soft processor ● Need more performance? – Parallel -
An Explicit-Solvent Conformation Search Method Using Open Software
An explicit-solvent conformation search method using open software Kari Gaalswyk and Christopher N. Rowley Department of Chemistry, Memorial University of Newfoundland, St. John’s, Newfoundland and Labrador, Canada ABSTRACT Computer modeling is a popular tool to identify the most-probable conformers of a molecule. Although the solvent can have a large effect on the stability of a conformation, many popular conformational search methods are only capable of describing molecules in the gas phase or with an implicit solvent model. We have developed a work-flow for performing a conformation search on explicitly-solvated molecules using open source software. This method uses replica exchange molecular dynamics (REMD) to sample the conformational states of the molecule efficiently. Cluster analysis is used to identify the most probable conformations from the simulated trajectory. This work-flow was tested on drug molecules a-amanitin and cabergoline to illustrate its capabilities and effectiveness. The preferred conformations of these molecules in gas phase, implicit solvent, and explicit solvent are significantly different. Subjects Biophysics, Pharmacology, Computational Science Keywords Conformation search, Explicit solvent, Cluster analysis, Replica exchange molecular dynamics INTRODUCTION Many molecules can exist in multiple conformational isomers. Conformational isomers have the same chemical bonds, but differ in their 3D geometry because they hold different torsional angles (Crippen & Havel, 1988). The conformation of a molecule can affect Submitted 5 April 2016 chemical reactivity, molecular binding, and biological activity (Struthers, Rivier & Accepted 6May2016 Published 31 May 2016 Hagler, 1985; Copeland, 2011). Conformations differ in stability because they experience different steric, electrostatic, and solute-solvent interactions. The probability, p,ofa Corresponding author Christopher N. -
Intel® Quartus® Prime Design Suite Version 18.1 Update Release Notes
Intel® Quartus® Prime Design Suite Version 18.1 Update Release Notes Updated for Intel® Quartus® Prime Design Suite: 18.1.1 Standard Edition Subscribe RN-01080-18.1.1.0 | 2019.04.17 Send Feedback Latest document on the web: PDF | HTML Contents Contents 1. Intel® Quartus® Prime Design Suite Version 18.1 Update Release Notes........................ 3 2. Issues Addressed in Update 1......................................................................................... 4 2.1. Intel Quartus Prime Pro Edition Software.................................................................. 4 2.2. Intel Quartus Prime Standard Edition Software.......................................................... 7 2.3. IP and IP Cores..................................................................................................... 8 2.4. DSP Builder for Intel FPGAs...................................................................................12 2.5. Intel High Level Synthesis Compiler........................................................................12 2.6. Intel FPGA SDK for OpenCL*................................................................................. 13 3. Issues Addressed in Update 2....................................................................................... 15 3.1. Intel Quartus Prime Pro Edition Software.................................................................15 3.2. IP and IP Cores................................................................................................... 15 3.3. Intel FPGA SDK for OpenCL.................................................................................. -
Python Tools in Computational Chemistry (And Biology)
Python Tools in Computational Chemistry (and Biology) Andrew Dalke Dalke Scientific, AB Göteborg, Sweden EuroSciPy, 26-27 July, 2008 “Why does ‘import numpy’ take 0.4 seconds? Does it need to import 228 libraries?” - My first Numpy-discussion post (paraphrased) Your use case isn't so typical and so suffers on the import time end of the balance. - Response from Robert Kern (Others did complain. Import time down to 0.28s.) 52,000 structures PDB doubles every 2½ years HEADER PHOTORECEPTOR 23-MAY-90 1BRD 1BRD 2 COMPND BACTERIORHODOPSIN 1BRD 3 SOURCE (HALOBACTERIUM $HALOBIUM) 1BRD 4 EXPDTA ELECTRON DIFFRACTION 1BRD 5 AUTHOR R.HENDERSON,J.M.BALDWIN,T.A.CESKA,F.ZEMLIN,E.BECKMANN, 1BRD 6 AUTHOR 2 K.H.DOWNING 1BRD 7 REVDAT 3 15-JAN-93 1BRDB 1 SEQRES 1BRDB 1 REVDAT 2 15-JUL-91 1BRDA 1 REMARK 1BRDA 1 .. ATOM 54 N PRO 8 20.397 -15.569 -13.739 1.00 20.00 1BRD 136 ATOM 55 CA PRO 8 21.592 -15.444 -12.900 1.00 20.00 1BRD 137 ATOM 56 C PRO 8 21.359 -15.206 -11.424 1.00 20.00 1BRD 138 ATOM 57 O PRO 8 21.904 -15.930 -10.563 1.00 20.00 1BRD 139 ATOM 58 CB PRO 8 22.367 -14.319 -13.591 1.00 20.00 1BRD 140 ATOM 59 CG PRO 8 22.089 -14.564 -15.053 1.00 20.00 1BRD 141 ATOM 60 CD PRO 8 20.647 -15.054 -15.103 1.00 20.00 1BRD 142 ATOM 61 N GLU 9 20.562 -14.211 -11.095 1.00 20.00 1BRD 143 ATOM 62 CA GLU 9 20.192 -13.808 -9.737 1.00 20.00 1BRD 144 ATOM 63 C GLU 9 19.567 -14.935 -8.932 1.00 20.00 1BRD 145 ATOM 64 O GLU 9 19.815 -15.104 -7.724 1.00 20.00 1BRD 146 ATOM 65 CB GLU 9 19.248 -12.591 -9.820 1.00 99.00 1 1BRD 147 ATOM 66 CG GLU 9 19.902 -11.351 -10.387 1.00 99.00 1 1BRD 148 ATOM 67 CD GLU 9 19.243 -10.169 -10.980 1.00 99.00 1 1BRD 149 ATOM 68 OE1 GLU 9 18.323 -10.191 -11.782 1.00 99.00 1 1BRD 150 ATOM 69 OE2 GLU 9 19.760 -9.089 -10.597 1.00 99.00 1 1BRD 151 ATOM 70 N TRP 10 18.764 -15.737 -9.597 1.00 20.00 1BRD 152 ATOM 71 CA TRP 10 18.034 -16.884 -9.090 1.00 20.00 1BRD 153 ATOM 72 C TRP 10 18.843 -17.908 -8.318 1.00 20.00 1BRD 154 ATOM 73 O TRP 10 18.376 -18.310 -7.230 1.00 20.00 1BRD 155 . -
Computational Chemistry for Chemistry Educators Shawn C
Volume 1, Issue 1 Journal Of Computational Science Education Computational Chemistry for Chemistry Educators Shawn C. Sendlinger Clyde R. Metz North Carolina Central University College of Charleston Department of Chemistry Department of Chemistry and Biochemistry 1801 Fayetteville Street, 66 George Street, Durham, NC 27707 Charleston, SC 29424 919-530-6297 843-953-8097 [email protected] [email protected] ABSTRACT 1. INTRODUCTION In this paper we describe an ongoing project where the goal is to The majority of today’s students are technologically savvy and are develop competence and confidence among chemistry faculty so often more comfortable using computers than the faculty who they are able to utilize computational chemistry as an effective teach them. In order to harness the student’s interest in teaching tool. Advances in hardware and software have made technology and begin to use it as an educational tool, most faculty research-grade tools readily available to the academic community. members require some level of instruction and training. Because Training is required so that faculty can take full advantage of this chemistry research increasingly utilizes computation as an technology, begin to transform the educational landscape, and important tool, our approach to chemistry education should reflect attract more students to the study of science. this. The ability of computer technology to visualize and manipulate objects on an atomic scale can be a powerful tool to increase both student interest in chemistry as well as their level of Categories and Subject Descriptors understanding. Computational Chemistry for Chemistry Educators (CCCE) is a project that seeks to provide faculty the J.2 [Physical Sciences and Engineering]: Chemistry necessary knowledge, experience, and technology access so that they can begin to design and incorporate computational approaches in the courses they teach. -
Intel® Industrial Iot Workshop Security for Industrial Platforms
Intel® Industrial IoT workshop Security for industrial platforms Gopi K. Agrawal Security Architect IOTG Technical Sales & Marketing Intel Corporation Legal © 2018 Intel Corporation No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel disclaims all express and implied warranties, including without limitation, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement, as well as any warranty arising from course of performance, course of dealing, or usage in trade. This document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps. Intel technologies' features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. Check with your system manufacturer or retailer or learn more at www.intel.com. Intel, the Intel logo, are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest Intel product specifications and roadmaps No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Performance varies depending on system configuration. No computer system can be absolutely secure. -
Preparing and Analyzing Large Molecular Simulations With
Preparing and Analyzing Large Molecular Simulations with VMD John Stone, Senior Research Programmer NIH Center for Macromolecular Modeling and Bioinformatics, University of Illinois at Urbana-Champaign VMD Tutorials Home Page • http://www.ks.uiuc.edu/Training/Tutorials/ – Main VMD tutorial – VMD images and movies tutorial – QwikMD simulation preparation and analysis plugin – Structure check – VMD quantum chemistry visualization tutorial – Visualization and analysis of CPMD data with VMD – Parameterizing small molecules using ffTK Overview • Introduction • Data model • Visualization • Scripting and analytical features • Trajectory analysis and visualization • High fidelity ray tracing • Plugins and user-extensibility • Large system analysis and visualization VMD – “Visual Molecular Dynamics” • 100,000 active users worldwide • Visualization and analysis of: – Molecular dynamics simulations – Lattice cell simulations – Quantum chemistry calculations – Cryo-EM densities, volumetric data • User extensible scripting and plugins • http://www.ks.uiuc.edu/Research/vmd/ Cell-Scale Modeling MD Simulation VMD – “Visual Molecular Dynamics” • Unique capabilities: • Visualization and analysis of: – Trajectories are fundamental to VMD – Molecular dynamics simulations – Support for very large systems, – “Particle” systems and whole cells now reaching billions of particles – Cryo-EM densities, volumetric data – Extensive GPU acceleration – Quantum chemistry calculations – Parallel analysis/visualization with MPI – Sequence information MD Simulations Cell-Scale -
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. -
Demystifying Internet of Things Security Successful Iot Device/Edge and Platform Security Deployment — Sunil Cheruvu Anil Kumar Ned Smith David M
Demystifying Internet of Things Security Successful IoT Device/Edge and Platform Security Deployment — Sunil Cheruvu Anil Kumar Ned Smith David M. Wheeler Demystifying Internet of Things Security Successful IoT Device/Edge and Platform Security Deployment Sunil Cheruvu Anil Kumar Ned Smith David M. Wheeler Demystifying Internet of Things Security: Successful IoT Device/Edge and Platform Security Deployment Sunil Cheruvu Anil Kumar Chandler, AZ, USA Chandler, AZ, USA Ned Smith David M. Wheeler Beaverton, OR, USA Gilbert, AZ, USA ISBN-13 (pbk): 978-1-4842-2895-1 ISBN-13 (electronic): 978-1-4842-2896-8 https://doi.org/10.1007/978-1-4842-2896-8 Copyright © 2020 by The Editor(s) (if applicable) and The Author(s) This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. Open Access This book is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this book are included in the book’s Creative Commons license, unless indicated otherwise in a credit line to the material. -
(10) Patent No.: US 9037807 B2
US009037807B2 (12) United States Patent (10) Patent No.: US 9,037,807 B2 Vorbach (45) Date of Patent: May 19, 2015 (54) PROCESSOR ARRANGEMENT ON A CHIP Sep. 17, 2001 (DE) .................................. 101 45 792 INCLUDING DATA PROCESSING, MEMORY, Sep. 17, 2001 (DE) ... ... 101 45795 AND INTERFACE ELEMENTS Sep. 19, 2001 (DE) .................................. 101 46132 Sep. 30, 2001 (WO). ... PCT/EPO1/11299 (75) Inventor: Martin Vorbach, Munich (DE) Oct. 8, 2001 (WO) ....................... PCT/EPO1/11593 Nov. 5, 2001 (DE) .................................. 101 54. 259 (73) Assignee: srecinologies AG, Nov. 5, 2001 (DE) ... ... 101 54 260 Dec. 14, 2001 (EP) ..................................... O1129923 (*) Notice: Subject to any disclaimer, the term of this Jan. 18, 2002 (EP) ..................................... O2OO1331 patent is extended or adjusted under 35 Jan. 19, 2002 (DE). 102 O2 044 U.S.C. 154(b) by 0 days. Jan. 20, 2002 (DE) 102 O2 175 Feb. 15, 2002 (DE) 102 O2 653 (21) Appl. No.: 12/944,068 Feb. 18, 2002 (DE) ... ... 102 O6856 Feb. 18, 2002 (DE) ... ... 102 O6857 (22) Filed: Nov. 11, 2010 Feb. 21, 2002 (DE) ... ... 102 O7 224 Feb. 21, 2002 (DE) ... ... 102 O7 225 (65) Prior Publication Data Feb. 21, 2002 (DE) .................................. 102 O7 226 US 2011 FOO60942 A1 Mar. 10, 2011 (51) Int. Cl. O O G06F 3/4 (2006.01) Related U.S. Application Data G06F II/20 (2006.01) (60) Division of application No. 12/496.012, filed on Jul. 1, G06F 3/16 (2006.01) 2009, now abandoned, which is a continuation of G06F 12/00 (2006.01) application No. 10/471.061, filed as application No.