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CUDA 6 and Beyond
MUMPS USERS DAYS JUNE 1ST / 2ND 2017 Programming heterogeneous architectures with libraries: A survey of NVIDIA linear algebra libraries François Courteille |Principal Solutions Architect, NVIDIA |[email protected] ACKNOWLEDGEMENTS Joe Eaton , NVIDIA Ty McKercher , NVIDIA Lung Sheng Chien , NVIDIA Nikolay Markovskiy , NVIDIA Stan Posey , NVIDIA Steve Rennich , NVIDIA Dave Miles , NVIDIA Peng Wang, NVIDIA Questions: [email protected] 2 AGENDA Prolegomena NVIDIA Solutions for Accelerated Linear Algebra Libraries performance on Pascal Rapid software development for heterogeneous architecture 3 PROLEGOMENA 124 NVIDIA Gaming VR AI & HPC Self-Driving Cars GPU Computing 5 ONE ARCHITECTURE BUILT FOR BOTH DATA SCIENCE & COMPUTATIONAL SCIENCE AlexNet Training Performance 70x Pascal [CELLR ANGE] 60x 16nm FinFET 50x 40x CoWoS HBM2 30x 20x [CELLR ANGE] NVLink 10x [CELLR [CELLR ANGE] ANGE] 0x 2013 2014 2015 2016 cuDNN Pascal & Volta NVIDIA DGX-1 NVIDIA DGX SATURNV 65x in 3 Years 6 7 8 8 9 NVLINK TO CPU IBM Power Systems Server S822LC (codename “Minsky”) DDR4 DDR4 2x IBM Power8+ CPUs and 4x P100 GPUs 115GB/s 80 GB/s per GPU bidirectional for peer traffic IB P8+ CPU P8+ CPU IB 80 GB/s per GPU bidirectional to CPU P100 P100 P100 P100 115 GB/s CPU Memory Bandwidth Direct Load/store access to CPU Memory High Speed Copy Engines for bulk data movement 1615 UNIFIED MEMORY ON PASCAL Large datasets, Simple programming, High performance CUDA 8 Enable Large Oversubscribe GPU memory Pascal Data Models Allocate up to system memory size CPU GPU Higher Demand -
The Molecular Sciences Software Institute
The Molecular Sciences Software Institute T. Daniel Crawford, Cecilia Clementi, Robert Harrison, Teresa Head-Gordon, Shantenu Jha*, Anna Krylov, Vijay Pande, and Theresa Windus http://molssi.org S2I2 HEP/CS Workshop at NCSA/UIUC 07 Dec, 2016 1 Outline • Space and Scope of Computational Molecular Sciences. • “State of the art and practice” • Intellectual drivers • Conceptualization Phase: Identifying the community and needs • Bio-molecular Simulations (BMS) Conceptualization • Quantum Mechanics/Chemistry (QM) Conceptualization • Execution Phase. • Structure and Governance Model • Resource Distribution • Work Plan 2 The Molecular Sciences Software Institute (MolSSI) • New project (as of August 1st, 2016) funded by the National Science Foundation. • Collaborative effort by Virginia Tech, Rice U., Stony Brook U., U.C. Berkeley, Stanford U., Rutgers U., U. Southern California, and Iowa State U. • Total budget of $19.42M for five years, potentially renewable to ten years. • Joint support from numerous NSF divisions: Advanced Cyberinfrastructure (ACI), Chemistry (CHE), Division of Materials Research (DMR), Office of Multidisciplinary Activities (OMA) • Designed to serve and enhance the software development efforts of the broad field of computational molecular science. 3 Computational Molecular Sciences (CMS) • The history of CMS – the sub-fields of quantum chemistry, computational materials science, and biomolecular simulation – reaches back decades to the genesis of computational science. • CMS is now a “full partner with experiment”. • For an impressive array of chemical, biochemical, and materials challenges, our community has developed simulations and models that directly impact: • Development of new chiral drugs; • Elucidation of the functionalities of biological macromolecules; • Development of more advanced materials for solar-energy storage, technology for CO2 sequestration, etc. -
IPS Signature Release Note V9.17.79
SOPHOS IPS Signature Update Release Notes Version : 9.17.79 Release Date : 19th January 2020 IPS Signature Update Release Information Upgrade Applicable on IPS Signature Release Version 9.17.78 CR250i, CR300i, CR500i-4P, CR500i-6P, CR500i-8P, CR500ia, CR500ia-RP, CR500ia1F, CR500ia10F, CR750ia, CR750ia1F, CR750ia10F, CR1000i-11P, CR1000i-12P, CR1000ia, CR1000ia10F, CR1500i-11P, CR1500i-12P, CR1500ia, CR1500ia10F Sophos Appliance Models CR25iNG, CR25iNG-6P, CR35iNG, CR50iNG, CR100iNG, CR200iNG/XP, CR300iNG/XP, CR500iNG- XP, CR750iNG-XP, CR2500iNG, CR25wiNG, CR25wiNG-6P, CR35wiNG, CRiV1C, CRiV2C, CRiV4C, CRiV8C, CRiV12C, XG85 to XG450, SG105 to SG650 Upgrade Information Upgrade type: Automatic Compatibility Annotations: None Introduction The Release Note document for IPS Signature Database Version 9.17.79 includes support for the new signatures. The following sections describe the release in detail. New IPS Signatures The Sophos Intrusion Prevention System shields the network from known attacks by matching the network traffic against the signatures in the IPS Signature Database. These signatures are developed to significantly increase detection performance and reduce the false alarms. Report false positives at [email protected], along with the application details. January 2020 Page 2 of 245 IPS Signature Update This IPS Release includes Two Thousand, Seven Hundred and Sixty Two(2762) signatures to address One Thousand, Nine Hundred and Thirty Eight(1938) vulnerabilities. New signatures are added for the following vulnerabilities: Name CVE–ID -
A Command-Line Interface for Analysis of Molecular Dynamics Simulations
taurenmd: A command-line interface for analysis of Molecular Dynamics simulations. João M.C. Teixeira1, 2 1 Previous, Biomolecular NMR Laboratory, Organic Chemistry Section, Inorganic and Organic Chemistry Department, University of Barcelona, Baldiri Reixac 10-12, Barcelona 08028, Spain 2 DOI: 10.21105/joss.02175 Current, Program in Molecular Medicine, Hospital for Sick Children, Toronto, Ontario M5G 0A4, Software Canada • Review Summary • Repository • Archive Molecular dynamics (MD) simulations of biological molecules have evolved drastically since its application was first demonstrated four decades ago (McCammon, Gelin, & Karplus, 1977) and, nowadays, simulation of systems comprising millions of atoms is possible due to the latest Editor: Richard Gowers advances in computation and data storage capacity – and the scientific community’s interest Reviewers: is growing (Hospital, Battistini, Soliva, Gelpí, & Orozco, 2019). Academic groups develop most of the MD methods and software for MD data handling and analysis. The MD analysis • @amritagos libraries developed solely for the latter scope nicely address the needs of manipulating raw data • @luthaf and calculating structural parameters, such as: MDAnalysis (Gowers et al., 2016; Michaud- Agrawal, Denning, Woolf, & Beckstein, 2011); (McGibbon et al., 2015); (Romo, Submitted: 03 March 2020 MDTraj LOOS Published: 02 June 2020 Leioatts, & Grossfield, 2014); and PyTraj (Hai Nguyen, 2016; Roe & Cheatham, 2013), each with its advantages and drawbacks inherent to their implementation strategies. This diversity License enriches the field with a panoply of strategies that the community can utilize. Authors of papers retain copyright and release the work The MD analysis software libraries widely distributed and adopted by the community share under a Creative Commons two main characteristics: 1) they are written in pure Python (Rossum, 1995), or provide a Attribution 4.0 International Python interface; and 2) they are libraries: highly versatile and powerful pieces of software that, License (CC BY 4.0). -
EPSRC Service Level Agreement with STFC for Computational Science Support
CoSeC Computational Science Centre for Research Communities EPSRC Service Level Agreement with STFC for Computational Science Support FY 2016/17 Report and Update on FY 2017/18 Work Plans This document contains the 2016/17 plans, 2016/17 summary reports, and 2017/18 plans for the programme in support of CCP and HEC communities delivered by STFC and funded by EPSRC through a Service Level Agreement. Notes in blue are in-year updates on progress to the tasks included in the 2016/17 plans. Text highlighted in yellow shows changes to the draft 2017/18 plans that we submitted in January 2017. Contents CCP5 – Computer Simulation of Condensed Phases .......................................................................... 4 CCP5 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 4 CCP5 – Summary Report (1 April 2016 – 31 March 2017) .................................................... 7 CCP5 –2017 / 18 Plans (1 April 2017 – 31 March 2018) ....................................................... 8 CCP9 – Electronic Structure of Solids .................................................................................................. 9 CCP9 – 2016 / 17 Plans (1 April 2016 – 31 March 2017) ...................................................... 9 CCP9 – Summary Report (1 April 2016 – 31 March 2017) .................................................. 11 CCP9 – 2017 / 18 Plans (1 April 2017 – 31 March 2018) .................................................... 12 CCP-mag – Computational Multiscale -
LARGE-SCALE COMPUTATION of PSEUDOSPECTRA USING ARPACK and EIGS∗ 1. Introduction. the Matrices in Many Eigenvalue Problems
SIAM J. SCI. COMPUT. c 2001 Society for Industrial and Applied Mathematics Vol. 23, No. 2, pp. 591–605 LARGE-SCALE COMPUTATION OF PSEUDOSPECTRA USING ARPACK AND EIGS∗ THOMAS G. WRIGHT† AND LLOYD N. TREFETHEN† Abstract. ARPACK and its Matlab counterpart, eigs, are software packages that calculate some eigenvalues of a large nonsymmetric matrix by Arnoldi iteration with implicit restarts. We show that at a small additional cost, which diminishes relatively as the matrix dimension increases, good estimates of pseudospectra in addition to eigenvalues can be obtained as a by-product. Thus in large- scale eigenvalue calculations it is feasible to obtain routinely not just eigenvalue approximations, but also information as to whether or not the eigenvalues are likely to be physically significant. Examples are presented for matrices with dimension up to 200,000. Key words. Arnoldi, ARPACK, eigenvalues, implicit restarting, pseudospectra AMS subject classifications. 65F15, 65F30, 65F50 PII. S106482750037322X 1. Introduction. The matrices in many eigenvalue problems are too large to allow direct computation of their full spectra, and two of the iterative tools available for computing a part of the spectrum are ARPACK [10, 11]and its Matlab counter- part, eigs.1 For nonsymmetric matrices, the mathematical basis of these packages is the Arnoldi iteration with implicit restarting [11, 23], which works by compressing the matrix to an “interesting” Hessenberg matrix, one which contains information about the eigenvalues and eigenvectors of interest. For general information on large-scale nonsymmetric matrix eigenvalue iterations, see [2, 21, 29, 31]. For some matrices, nonnormality (nonorthogonality of the eigenvectors) may be physically important [30]. -
Introduction to Scientific Computing
Introduction to Scientific Computing “ what you need to learn now to decide what you need to learn next” Bob Dowling University Computing Service [email protected] www-uxsup.csx.cam.ac.uk/courses/SciProg This course is the first in a set of courses designed to assist members of the University who need to program computers to help with their science. You do not need to attend all these courses. The purpose of this course is to teach you the minimum you need for any of them and to teach you which courses you need and which you don't. 1. Why this course exists 2. Common concepts and general good practice Coffee break 3. Selecting programming languages This afternoon is split into two one hour blocks separated by a coffee break. The first block will talk a little bit about computer programs in general and what constitutes “good practice” while writing them. It also describes some of the jargon and the other problems you will face. The second will talk about the types of programming language that there are and help you select the appropriate ones for your work. Throughout, the course will point to further sources of information. Why does this course exist? MPI e-Science grid computing Condor ? Basics C/Fortran Scripts ! I should explain why this course exists. A couple of years ago a member of the UCS conducted some usability interviews with people in the University about the usability of some high-end e-science software. The response he got back was not what we were expecting; it was “why are you wasting our time with this high level nonsense? Help us with the basics!” So we put together a set of courses and advice on the basics. -
Lawrence Berkeley National Laboratory Recent Work
Lawrence Berkeley National Laboratory Recent Work Title From NWChem to NWChemEx: Evolving with the Computational Chemistry Landscape. Permalink https://escholarship.org/uc/item/4sm897jh Journal Chemical reviews, 121(8) ISSN 0009-2665 Authors Kowalski, Karol Bair, Raymond Bauman, Nicholas P et al. Publication Date 2021-04-01 DOI 10.1021/acs.chemrev.0c00998 Peer reviewed eScholarship.org Powered by the California Digital Library University of California From NWChem to NWChemEx: Evolving with the computational chemistry landscape Karol Kowalski,y Raymond Bair,z Nicholas P. Bauman,y Jeffery S. Boschen,{ Eric J. Bylaska,y Jeff Daily,y Wibe A. de Jong,x Thom Dunning, Jr,y Niranjan Govind,y Robert J. Harrison,k Murat Keçeli,z Kristopher Keipert,? Sriram Krishnamoorthy,y Suraj Kumar,y Erdal Mutlu,y Bruce Palmer,y Ajay Panyala,y Bo Peng,y Ryan M. Richard,{ T. P. Straatsma,# Peter Sushko,y Edward F. Valeev,@ Marat Valiev,y Hubertus J. J. van Dam,4 Jonathan M. Waldrop,{ David B. Williams-Young,x Chao Yang,x Marcin Zalewski,y and Theresa L. Windus*,r yPacific Northwest National Laboratory, Richland, WA 99352 zArgonne National Laboratory, Lemont, IL 60439 {Ames Laboratory, Ames, IA 50011 xLawrence Berkeley National Laboratory, Berkeley, 94720 kInstitute for Advanced Computational Science, Stony Brook University, Stony Brook, NY 11794 ?NVIDIA Inc, previously Argonne National Laboratory, Lemont, IL 60439 #National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831-6373 @Department of Chemistry, Virginia Tech, Blacksburg, VA 24061 4Brookhaven National Laboratory, Upton, NY 11973 rDepartment of Chemistry, Iowa State University and Ames Laboratory, Ames, IA 50011 E-mail: [email protected] 1 Abstract Since the advent of the first computers, chemists have been at the forefront of using computers to understand and solve complex chemical problems. -
Measure to Fund Upgrades
In Sports... In Forum... Spartans upset On the bus in 10th-ranked SPARTAN San Jose: 49ers, win two of sights, sounds three games in and smells. weekend series. FORUM & OPINION See story on page 4. See column on page 2. 1'111,11,11yd for AIStith' tucr 1414 Volume 102, Number 49 Monday. April 18, 1994 Measure to fund upgrades New building By Laurel Anderson The money will be divided almost oumi, associate executive vice presi- The $901; ntillion to fund the pro- in the works siranan Daily Staff Writer equally among the three systems. dent of facilities development and jects comes In the state's selling of genti.t1 /bligation bonds to investors By Laurel Anderson A bond measure to fund Califor- The CSU's project list covers an operations. Sintruui Day St41 Writer nia college campus improvements estimated 80 projects at all 20 Bentley-Adler said, "In who earn interest on the money they will be on the June 1994 ballot. The campuses. 1990 when Proposition 143 loan to the state. The total cost to the A plan to construct and renovate campus projects include modernization and "The bond measure is the ALIFORNIA failed, that set us back a cou- state is $1.6 billion, because an addi- facilities will proceed if SJSU receives funding remodeling of facilities, updating only way to remodel and ple years in building and tional $700 million will be paid in from a $900 million bond measure. safety standards and replacing aging repair some of our facilities," TATE remodeling projects, so if interest. -
PLUMED User's Guide
PLUMED User’s Guide A portable plugin for free-energy calculations with molecular dynamics Version 1.3.0 – Nov 2011 Contents 1 Introduction 5 1.1 What is PLUMED?.........................5 1.2 Supported codes . .6 1.3 Features . .7 1.4 New in version 1.3 . .8 1.5 Restrictions . .9 1.6 The PLUMED package . .9 1.7 Online resources . 10 1.8 Credits . 11 1.9 Citing PLUMED ........................... 11 1.10 License . 11 2 Installation 13 2.1 Compiling PLUMED ......................... 13 2.1.1 Compiling the ACEMD plugin with PLUMED ...... 17 2.2 Including reconnaissance metadynamics . 18 2.3 Testing the installation . 19 2.4 Back to the original code . 21 2.5 The Python interface to PLUMED ................. 21 3 Running free-energy simulations 23 3.1 How to activate PLUMED ..................... 23 3.2 The input file . 25 3.3 A note on units . 27 3.4 Metadynamics . 27 3.4.1 Typical output . 27 3.4.2 Bias potential . 28 3.4.3 Well-tempered metadynamics . 29 1 3.4.4 Restarting a metadynamics run . 30 3.4.5 Using GRID ........................ 30 3.4.6 Multiple walkers . 35 3.4.7 Monitoring a collective variable without biasing it . 35 3.4.8 Defining an interval . 36 3.4.9 Inversion condition . 38 3.5 Running in parallel . 40 3.6 Replica exchange methods . 40 3.6.1 Parallel tempering metadynamics . 41 3.6.2 Bias exchange simulations . 42 3.7 Umbrella sampling . 45 3.8 Steered MD . 46 3.8.1 Steerplan . 46 3.9 Adiabatic Bias MD . -
Index Images Download 2006 News Crack Serial Warez Full 12 Contact
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A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms
A High Performance Implementation of Spectral Clustering on CPU-GPU Platforms Yu Jin Joseph F. JaJa Institute for Advanced Computer Studies Institute for Advanced Computer Studies Department of Electrical and Computer Engineering Department of Electrical and Computer Engineering University of Maryland, College Park, USA University of Maryland, College Park, USA Email: [email protected] Email: [email protected] Abstract—Spectral clustering is one of the most popular graph CPUs, further boost the overall performance and are able clustering algorithms, which achieves the best performance for to achieve very high performance on problems whose sizes many scientific and engineering applications. However, existing grow up to the capacity of CPU memory [6, 7, 8, 9, 10, implementations in commonly used software platforms such as Matlab and Python do not scale well for many of the emerging 11]. In this paper, we present a hybrid implementation of the Big Data applications. In this paper, we present a fast imple- spectral clustering algorithm which significantly outperforms mentation of the spectral clustering algorithm on a CPU-GPU the known implementations, most of which are purely based heterogeneous platform. Our implementation takes advantage on multi-core CPUs. of the computational power of the multi-core CPU and the There have been reported efforts on parallelizing the spec- massive multithreading and SIMD capabilities of GPUs. Given the input as data points in high dimensional space, we propose tral clustering algorithm. Zheng et al. [12] presented both a parallel scheme to build a sparse similarity graph represented CUDA and OpenMP implementations of spectral clustering. in a standard sparse representation format.