NVIDIA GPU Accelerated Applications Catalog
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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 -
Popular GPU-Accelerated Applications
LIFE & MATERIALS SCIENCES GPU-ACCELERATED APPLICATIONS | CATALOG | AUG 12 LIFE & MATERIALS SCIENCES APPLICATIONS CATALOG Application Description Supported Features Expected Multi-GPU Release Status Speed Up* Support Bioinformatics BarraCUDA Sequence mapping software Alignment of short sequencing 6-10x Yes Available now reads Version 0.6.2 CUDASW++ Open source software for Smith-Waterman Parallel search of Smith- 10-50x Yes Available now protein database searches on GPUs Waterman database Version 2.0.8 CUSHAW Parallelized short read aligner Parallel, accurate long read 10x Yes Available now aligner - gapped alignments to Version 1.0.40 large genomes CATALOG GPU-BLAST Local search with fast k-tuple heuristic Protein alignment according to 3-4x Single Only Available now blastp, multi cpu threads Version 2.2.26 GPU-HMMER Parallelized local and global search with Parallel local and global search 60-100x Yes Available now profile Hidden Markov models of Hidden Markov Models Version 2.3.2 mCUDA-MEME Ultrafast scalable motif discovery algorithm Scalable motif discovery 4-10x Yes Available now based on MEME algorithm based on MEME Version 3.0.12 MUMmerGPU A high-throughput DNA sequence alignment Aligns multiple query sequences 3-10x Yes Available now LIFE & MATERIALS& LIFE SCIENCES APPLICATIONS program against reference sequence in Version 2 parallel SeqNFind A GPU Accelerated Sequence Analysis Toolset HW & SW for reference 400x Yes Available now assembly, blast, SW, HMM, de novo assembly UGENE Opensource Smith-Waterman for SSE/CUDA, Fast short -
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. -
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 -
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. -
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. -
VMD User's Guide
VMD User’s Guide Version 1.9.4a48 October 13, 2020 NIH Biomedical Research Center for Macromolecular Modeling and Bioinformatics Theoretical and Computational Biophysics Group1 Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign 405 N. Mathews Urbana, IL 61801 http://www.ks.uiuc.edu/Research/vmd/ Description The VMD User’s Guide describes how to run and use the molecular visualization and analysis program VMD. This guide documents the user interfaces displaying and grapically manipulating molecules, and describes how to use the scripting interfaces for analysis and to customize the behavior of VMD. VMD development is supported by the National Institutes of Health grant numbers NIH P41- GM104601. 1http://www.ks.uiuc.edu/ Contents 1 Introduction 11 1.1 Contactingtheauthors. ....... 12 1.2 RegisteringVMD.................................. 12 1.3 CitationReference ............................... ...... 12 1.4 Acknowledgments................................. ..... 13 1.5 Copyright and Disclaimer Notices . .......... 13 1.6 For information on our other software . .......... 15 2 Hardware and Software Requirements 17 2.1 Basic Hardware and Software Requirements . ........... 17 2.2 Multi-core CPUs and GPU Acceleration . ......... 17 2.3 Parallel Computing on Clusters and Supercomputers . .............. 18 3 Tutorials 19 3.1 RapidIntroductiontoVMD. ...... 19 3.2 Viewing a molecule: Myoglobin . ........ 19 3.3 RenderinganImage ................................ 21 3.4 AQuickAnimation................................. 21 3.5 An Introduction to Atom Selection . ......... 22 3.6 ComparingTwoStructures . ...... 22 3.7 SomeNiceRepresenations . ....... 23 3.8 Savingyourwork.................................. 24 3.9 Tracking Script Command Versions of the GUI Actions . ............ 24 4 Loading A Molecule 26 4.1 Notes on common molecular file formats . ......... 26 4.2 Whathappenswhenafileisloaded? . ....... 27 4.3 Babelinterface ................................. -
GPGPU Progress in Computational Chemistry
GPGPU Progress in Computational Chemistry Mark Berger, Life and Material Sciences Alliances Manager [email protected] Agenda • A Little about NVIDIA • Update on GPUs in Computational Chemistry • A Couple of Examples • Upcoming Events 2 PARALLEL PERSONAL VISUALIZATION COMPUTING COMPUTING c 3 NVIDIA and HPC Evolution of GPUs Public, based in Santa Clara, CA | ~$4B revenue | ~5,500 employees Founded in 1999 with primary business in semiconductor industry Products for graphics in workstations, notebooks, mobile devices, etc. Began R&D of GPUs for HPC in 2004, released first Tesla and CUDA in 2007 Development of GPUs as a co-processing accelerator for x86 CPUs HPC Evolution of GPUs 2004: Began strategic investments in GPU as HPC co-processor 2006: G80 first GPU with built-in compute features, 128 cores; CUDA SDK Beta 2007: Tesla 8-series based on G80, 128 cores – CUDA 1.0, 1.1 2008: Tesla 10-series based on GT 200, 240 cores – CUDA 2.0, 2.3 3 Years With 3 Generations 2009: Tesla 20-series, code named “Fermi” up to 512 cores – CUDA SDK 3.0, 3.2 4 Tesla Data Center & Workstation GPU Solutions Tesla M-series GPUs Tesla C-series GPUs M2090 | M2070 | M2050 C2070 | C2050 Integrated CPU-GPU Workstations Servers & Blades 2 to 4 Tesla GPUs M2090 M2070 M2050 C2070 C2050 Cores 512 448 448 448 448 Memory 6 GB 6 GB 3 GB 6 GB 3 GB Memory bandwidth 177.6 GB/s 150 GB/s 148.8 GB/s 148.8 GB/s 148.8 GB/s (ECC off) Single 1030 1030 1331 1030 1030 Peak Precision Perf 515 515 Double Gflops 665 515 515 Precision 5 GPUs are Disruptive Speed Throughput Insight Days -
Recent Advances in Computing Infrastructure at Penn State and a Proposal for Integrating Computational Science in Undergraduate Education
Recent Advances in Computing Infrastructure at Penn State and A Proposal for Integrating Computational Science in Undergraduate Education a presentation for the HPC User Forum 2013 Fall Conference of Korean Society of Computational Science & Engineering Sponsored by National Institute of Supercomputing & Networking Seoul, South Korea October 1, 2013 Vijay K. Agarwala Senior Director, Research Computing and CI Information Technology Services The Pennsylvania State University University Park, PA 16802 USA What is missing in today's undergraduate education? • Lack of comprehensive training in computational science principles in many majors except Computer Science – Object Oriented Programming and Discrete Mathematics – Data Structures and Algorithm Analysis – Mathematical Methods and Numerical Analysis – Parallel Programming and Numerical Algorithms • A typical BS/BA graduate is not well-prepared for a computationally intensive graduate program • Computational proficiency is being acquired on-the-job, either in graduate school or at place of employment What could universities add or change? • An integrated 5-year program, either a BS/MS or dual BS/BA – Keep the general education requirements intact. The tradition of a liberal arts education is highly valued. – The discipline-specific part of undergraduate curricula cannot be shortened either – there is more to be taught than ever before in all majors – Add 8-10 classes to teach core principles of computational science and also extend the computational aspects of a discipline – Intent is -
User's Guide Terachem V1.9
User’s Guide TeraChem v1.9 PetaChem, LLC 26040 Elena Road Los Altos Hills, CA 94022 http://www.petachem.com August 16, 2017 TeraChem User’s Guide Contents 1 Introduction 3 1.1 Obtaining TeraChem.........................................4 1.2 Citing TeraChem...........................................4 1.3 Acknowledgements..........................................5 2 Getting started 6 2.1 System requirements.........................................6 2.2 Installation..............................................6 2.3 Environment Variables........................................7 2.4 Licensing...............................................8 2.4.1 Node-locked Licenses.....................................8 2.4.2 IP-Checkout Licenses....................................9 2.4.3 HTTP Forwarding......................................9 2.5 Command Line Options....................................... 10 2.6 Running sample jobs......................................... 12 3 TeraChem I/O file formats 17 3.1 Input files............................................... 17 3.2 Atomic coordinates.......................................... 17 3.3 Basis set file format......................................... 17 3.3.1 Mixing basis sets - Custom Basis File Method....................... 20 3.3.2 Mixing Basis Sets - Multibasis Method........................... 21 3.4 Fixing Atoms in Molecular Dynamics................................ 21 3.5 Ab Initio Steered Molecular Dynamics (AISMD)......................... 22 3.6 Specifying Values of the Hubbard Parameter for DFT+U................... -
Gpu-Accelerated Applications Gpu‑Accelerated Applications
GPU-ACCELERATED APPLICATIONS GPU-ACCELERATED APPLICATIONS Accelerated computing has revolutionized a broad range of industries with over five hundred applications optimized for GPUs to help you accelerate your work. CONTENTS 1 Computational Finance 2 Climate, Weather and Ocean Modeling 2 Data Science & Analytics 4 Deep Learning and Machine Learning 8 Defense and Intelligence 9 Manufacturing/AEC: CAD and CAE COMPUTATIONAL FLUID DYNAMICS COMPUTATIONAL STRUCTURAL MECHANICS DESIGN AND VISUALIZATION ELECTRONIC DESIGN AUTOMATION 17 Media & Entertainment ANIMATION, MODELING AND RENDERING COLOR CORRECTION AND GRAIN MANAGEMENT COMPOSITING, FINISHING AND EFFECTS EDITING ENCODING AND DIGITAL DISTRIBUTION ON-AIR GRAPHICS ON-SET, REVIEW AND STEREO TOOLS WEATHER GRAPHICS 24 Medical Imaging 27 Oil and Gas 28 Research: Higher Education and Supercomputing COMPUTATIONAL CHEMISTRY AND BIOLOGY NUMERICAL ANALYTICS PHYSICS SCIENTIFIC VISUALIZATION 39 Safety and Security 42 Tools and Management Computational Finance APPLICATION NAME COMPANY/DEVELOPER PRODUCT DESCRIPTION SUPPORTED FEATURES GPU SCALING Accelerated Elsen Secure, accessible, and accelerated back- • Web-like API with Native bindings for Multi-GPU Computing Engine testing, scenario analysis, risk analytics Python, R, Scala, C Single Node and real-time trading designed for easy • Custom models and data streams are integration and rapid development. easy to add Adaptiv Analytics SunGard A flexible and extensible engine for fast • Existing models code in C# supported Multi-GPU calculations of a wide variety of pricing transparently, with minimal code Single Node and risk measures on a broad range of changes asset classes and derivatives. • Supports multiple backends including CUDA and OpenCL • Switches transparently between multiple GPUs and CPUS depending on the deal support and load factors.