Chromium Renderserver: Scalable and Open Remote Rendering Infrastructure Brian Paul, Member, IEEE, Sean Ahern, Member, IEEE, E
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THINC: a Virtual and Remote Display Architecture for Desktop Computing and Mobile Devices
THINC: A Virtual and Remote Display Architecture for Desktop Computing and Mobile Devices Ricardo A. Baratto Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2011 c 2011 Ricardo A. Baratto This work may be used in accordance with Creative Commons, Attribution-NonCommercial-NoDerivs License. For more information about that license, see http://creativecommons.org/licenses/by-nc-nd/3.0/. For other uses, please contact the author. ABSTRACT THINC: A Virtual and Remote Display Architecture for Desktop Computing and Mobile Devices Ricardo A. Baratto THINC is a new virtual and remote display architecture for desktop computing. It has been designed to address the limitations and performance shortcomings of existing remote display technology, and to provide a building block around which novel desktop architectures can be built. THINC is architected around the notion of a virtual display device driver, a software-only component that behaves like a traditional device driver, but instead of managing specific hardware, enables desktop input and output to be intercepted, manipulated, and redirected at will. On top of this architecture, THINC introduces a simple, low-level, device-independent representation of display changes, and a number of novel optimizations and techniques to perform efficient interception and redirection of display output. This dissertation presents the design and implementation of THINC. It also intro- duces a number of novel systems which build upon THINC's architecture to provide new and improved desktop computing services. The contributions of this dissertation are as follows: • A high performance remote display system for LAN and WAN environments. -
Virtualgl / Turbovnc Survey Results Version 1, 3/17/2008 -- the Virtualgl Project
VirtualGL / TurboVNC Survey Results Version 1, 3/17/2008 -- The VirtualGL Project This report and all associated illustrations are licensed under the Creative Commons Attribution 3.0 License. Any works which contain material derived from this document must cite The VirtualGL Project as the source of the material and list the current URL for the VirtualGL web site. Between December, 2007 and March, 2008, a survey of the VirtualGL community was conducted to ascertain which features and platforms were of interest to current and future users of VirtualGL and TurboVNC. The larger purpose of this survey was to steer the future development of VirtualGL and TurboVNC based on user input. 1 Statistics 49 users responded to the survey, with 32 complete responses. When listing percentage breakdowns for each response to a question, this report computes the percentages relative to the total number of complete responses for that question. 2 Responses 2.1 Server Platform “Please select the server platform(s) that you currently use or plan to use with VirtualGL/TurboVNC” Platform Number of Respondees (%) Linux/x86 25 / 46 (54%) ● Enterprise Linux 3 (x86) 2 / 46 (4.3%) ● Enterprise Linux 4 (x86) 5 / 46 (11%) ● Enterprise Linux 5 (x86) 6 / 46 (13%) ● Fedora Core 4 (x86) 1 / 46 (2.2%) ● Fedora Core 7 (x86) 1 / 46 (2.2%) ● Fedora Core 8 (x86) 4 / 46 (8.7%) ● SuSE Linux Enterprise 9 (x86) 1 / 46 (2.2%) 1 Platform Number of Respondees (%) ● SuSE Linux Enterprise 10 (x86) 2 / 46 (4.3%) ● Ubuntu (x86) 7 / 46 (15%) ● Debian (x86) 5 / 46 (11%) ● Gentoo (x86) 1 / -
Parallel Particle Rendering: a Performance Comparison Between Chromium and Aura
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/221357191 Parallel Particle Rendering: a Performance Comparison between Chromium and Aura . Conference Paper · January 2006 DOI: 10.2312/EGPGV/EGPGV06/137-144 · Source: DBLP CITATIONS READS 4 29 3 authors, including: Michal Koutek Koninklijk Nederlands Meteorologisch Instituut 26 PUBLICATIONS 172 CITATIONS SEE PROFILE All content following this page was uploaded by Michal Koutek on 28 January 2015. The user has requested enhancement of the downloaded file. All in-text references underlined in blue are added to the original document and are linked to publications on ResearchGate, letting you access and read them immediately. Eurographics Symposium on Parallel Graphics and Visualization (2006) Alan Heirich, Bruno Raffin, and Luis Paulo dos Santos (Editors) Parallel particle rendering: a performance comparison between Chromium and Aura , Tom van der Schaaf1, Michal Koutek1 2 and Henri Bal1 1Faculty of Sciences - Vrije Universiteit, De Boelelaan 1081, 1081 HV Amsterdam, The Netherlands 2Faculty of Information Technology and Systems - Delft University of Technology Abstract In the fields of high performance computing and distributed rendering, there is a great need for a flexible and scalable architecture that supports coupling of parallel simulations to commodity visualization clusters. The most popular architecture that allows such flexibility, called Chromium, is a parallel implementation of OpenGL. It has sufficient performance on applications with static scenes, but in case of more dynamic content this approach often fails. We have developed Aura, a distributed scene graph library, which allows optimized performance for both static and more dynamic scenes. In this paper we compare the performance of Chromium and Aura. -
Supporting Distributed Visualization Services for High Performance Science and Engineering Applications – a Service Provider Perspective
9th IEEE/ACM International Symposium on Cluster Computing and the Grid Supporting distributed visualization services for high performance science and engineering applications – A service provider perspective Lakshmi Sastry*, Ronald Fowler, Srikanth Nagella and Jonathan Churchill e-Science Centre, Science & Technology Facilities Council, Introduction activities, the outcomes, the status and some suggestions as to the way forward. The Science & Technology Facilities Council is home to international Facilities such as the ISIS Workshops and tutorials Neutron Spallation Source, Central Laser Facility and Diamond Light Source, the National The take up of advanced visualization Grid Service including national super techniques within STFC scientists and their computers, Tier1 data service for CERN particle colleagues from the wider academia is quite physics experiment, the British Atmospheric limited despite decades of holding seminars and data Centre and the British Oceanographic Data surgeries to create awareness of the state of the Centre at the Space Science and Technology art. Visualization events generally tend to department. Together these Facilities generate attract practitioners in the field and an several Terabytes of data per month which needs occasional application domain expert. This is a to be handled, catalogued and provided access serious issue limiting the more widespread use to. In addition, the scientists within STFC of advanced visualization tools. In order to departments also develop complex simulations address this deficit, more recently, we have and undertake data analysis for their own begun an escalation of such events by holding experiments. Facilities also have strong ongoing show and tell “Other Peoples Business” to collaborations with UK academic and introduce exemplars from specific domains and commercial users through their involvement then the tools behind the exemplars, advertising with Collaborative Computational Programme, these events exclusively to scientists of various generating very large simulation datasets. -
Release 0.11 Todd Gamblin
Spack Documentation Release 0.11 Todd Gamblin Feb 07, 2018 Basics 1 Feature Overview 3 1.1 Simple package installation.......................................3 1.2 Custom versions & configurations....................................3 1.3 Customize dependencies.........................................4 1.4 Non-destructive installs.........................................4 1.5 Packages can peacefully coexist.....................................4 1.6 Creating packages is easy........................................4 2 Getting Started 7 2.1 Prerequisites...............................................7 2.2 Installation................................................7 2.3 Compiler configuration..........................................9 2.4 Vendor-Specific Compiler Configuration................................ 13 2.5 System Packages............................................. 16 2.6 Utilities Configuration.......................................... 18 2.7 GPG Signing............................................... 20 2.8 Spack on Cray.............................................. 21 3 Basic Usage 25 3.1 Listing available packages........................................ 25 3.2 Installing and uninstalling........................................ 42 3.3 Seeing installed packages........................................ 44 3.4 Specs & dependencies.......................................... 46 3.5 Virtual dependencies........................................... 50 3.6 Extensions & Python support...................................... 53 3.7 Filesystem requirements........................................ -
A Load-Balancing Strategy for Sort-First Distributed Rendering
A load-balancing strategy for sort-first distributed rendering Frederico Abraham, Waldemar Celes, Renato Cerqueira, Joao˜ Luiz Campos Tecgraf - Computer Science Department, PUC-Rio Rua Marquesˆ de Sao˜ Vicente 225, 22450-900 Rio de Janeiro, RJ, Brasil ffabraham,celes,rcerq,[email protected] Abstract such algorithms, when applied to complex scenes, still de- mand graphics power that a single processor may not be In this paper, we present a multi-threaded sort-first dis- able to deliver. Examples of such scenarios include scenes tributed rendering system. In order to achieve load balance described by a dense and highly tessellated geometry set, among the rendering nodes, we propose a new partition- sophisticated per-pixel lighting algorithms and volume ren- ing scheme based on the rendering time of the previous dering. frame. The proposed load-balancing algorithm is very sim- The purpose of our research is to investigate the use of ple to be implemented and works well for both geometry- PC-based clusters for improving the rendering performance and rasterization-bound models. We also propose a strat- of such complex scenes, delivering the frame rate usually egy to assign tiles to rendering nodes that effectively uses required by virtual-reality applications. This goal can be the available graphics resources, thus improving rendering achieved by combining the graphics power of a set of PCs performance. equipped with graphics accelerators. Although PC clusters have also been used for supporting high resolution multi- display rendering systems [2, 12, 14, 13], in this paper we focus on the use of a PC cluster for improving rendering 1. -
Vsim User Guide Release 10.1.0-R2780
VSim User Guide Release 10.1.0-r2780 Tech-X Corporation Mar 12, 2020 2 CONTENTS 1 Overview 1 1.1 What is VSimComposer?........................................1 1.2 VSim Capabilities............................................1 2 Starting VSimComposer 3 2.1 Running Locally.............................................3 2.2 Running VSimComposer On a Remote Computer System.......................4 2.3 Visualizing Remote Data.........................................5 2.4 Welcome Window............................................5 3 Creating or Opening a Simulation7 3.1 Starting a Simulation...........................................7 4 Menus and Menu Items 15 4.1 File Menu................................................. 15 4.2 Edit Menu................................................ 18 4.3 View Menu................................................ 21 4.4 Help Menu................................................ 21 4.5 Tools/VSimComposer Menu (Settings/Preferences)........................... 21 5 Simulation Concepts 31 5.1 Simulation Concepts Introduction.................................... 31 5.2 Grids................................................... 32 5.3 Geometries................................................ 36 5.4 Electric and Magnetic Fields....................................... 36 5.5 Particles................................................. 41 5.6 Reactions................................................. 43 5.7 Histories................................................. 44 6 Visual Setup 45 6.1 Setup Window for Visual-setup Simulations.............................. -
HOW to VISUALIZE YOUR GPU-ACCELERATED SIMULATION RESULTS Peter Messmer, NVIDIA
HOW TO VISUALIZE YOUR GPU-ACCELERATED SIMULATION RESULTS Peter Messmer, NVIDIA RANGE OF ANALYSIS AND VIZ TASKS . Analysis: Focus quantitative . Visualization: Focus qualitative . Monitoring, Steering TRADITIONAL HPC WORKFLOW Workstation Analysis, Setup Visualization Supercomputer Viz Cluster Dump, Checkpointing Visualization, Analysis File System TRADITIONAL WORKFLOW: CHALLENGES Lack of interactivity prevents “intuition” Workstation High-end viz Analysis, neglected due Setup Visualization to workflow complexity Supercomputer Viz Cluster Viz resources need I/O becomes main to scale with simulation Dump, simulation bottleneck Checkpointing Visualization, Analysis File System OUTLINE . Visualization applications . CUDA/OpenGL interop . Remote viz . Parallel viz . In-Situ viz High-level overview. Some parts platform dependent. Check with your sysadmin. VISUALIZATION APPLICATIONS NON-REPRESENTATIVE VIZ TOOLS SURVEY OF 25 HPC SITES Surveyed sites: LLNL LLNL- ORNL- AFRL- NASA- NERSC -OCF SCF LANL CCS DOD-ORC DSCR AFRL ARL ERDC NAVY MHPCC ORS CCAC NAS NASA-NCCS TACC CHPC RZG HLRN Julich CSCS CSC Hector Curie NON-REPRESENTATIVE VIZ TOOLS SURVEY OF 25 HPC SITES Surveyed sites: LLNL LLNL- ORNL- AFRL- NASA- NERSC -OCF SCF LANL CCS DOD-ORC DSCR AFRL ARL ERDC NAVY MHPCC ORS CCAC NAS NASA-NCCS TACC CHPC RZG HLRN Julich CSCS CSC Hector Curie VISIT . Scalar, vector and tensor field data features — Plots: contour, curve, mesh, pseudo-color, volume,.. — Operators: slice, iso-surface, threshold, binning,.. Quantitative and qualitative analysis/vis — Derived fields, dimension reduction, line-outs — Pick & query . Scalable architecture . Open source http://wci.llnl.gov/codes/visit/ VISIT . Cross-platform — Linux/Unix, OSX, Windows . Wide range of data formats — .vtk, .netcdf, .hdf5,.. Extensible — Plugin architecture . Embeddable . Python scriptable VISIT’S SCALABLE ARCHITECTURE . -
Cross-Segment Load Balancing in Parallel Rendering
EUROGRAPHICS Symposium on Parallel Graphics and Visualization (2011), pp. 1–10 N.N. and N.N. (Editors) Cross-Segment Load Balancing in Parallel Rendering Fatih Erol1, Stefan Eilemann1;2 and Renato Pajarola1 1Visualization and MultiMedia Lab, Department of Informatics, University of Zurich, Switzerland 2Eyescale Software GmbH, Switzerland Abstract With faster graphics hardware comes the possibility to realize even more complicated applications that require more detailed data and provide better presentation. The processors keep being challenged with bigger amount of data and higher resolution outputs, requiring more research in the parallel/distributed rendering domain. Optimiz- ing resource usage to improve throughput is one important topic, which we address in this article for multi-display applications, using the Equalizer parallel rendering framework. This paper introduces and analyzes cross-segment load balancing which efficiently assigns all available shared graphics resources to all display output segments with dynamical task partitioning to improve performance in parallel rendering. Categories and Subject Descriptors (according to ACM CCS): I.3.2 [Computer Graphics]: Graphics Systems— Distributed/network graphics; I.3.m [Computer Graphics]: Miscellaneous—Parallel Rendering Keywords: Dynamic load balancing, multi-display systems 1. Introduction of massive pixel amounts to the final display destinations at interactive frame rates. The graphics pipes (GPUs) driving As CPU and GPU processing power improves steadily, so the display array, however, are typically faced with highly and even more increasingly does the amount of data to uneven workloads as the vertex and fragment processing cost be processed and displayed interactively, which necessitates is often very concentrated in a few specific areas of the data new methods to improve performance of interactive massive and on the display screen. -
Package Name Software Description Project
A S T 1 Package Name Software Description Project URL 2 Autoconf An extensible package of M4 macros that produce shell scripts to automatically configure software source code packages https://www.gnu.org/software/autoconf/ 3 Automake www.gnu.org/software/automake 4 Libtool www.gnu.org/software/libtool 5 bamtools BamTools: a C++ API for reading/writing BAM files. https://github.com/pezmaster31/bamtools 6 Biopython (Python module) Biopython is a set of freely available tools for biological computation written in Python by an international team of developers www.biopython.org/ 7 blas The BLAS (Basic Linear Algebra Subprograms) are routines that provide standard building blocks for performing basic vector and matrix operations. http://www.netlib.org/blas/ 8 boost Boost provides free peer-reviewed portable C++ source libraries. http://www.boost.org 9 CMake Cross-platform, open-source build system. CMake is a family of tools designed to build, test and package software http://www.cmake.org/ 10 Cython (Python module) The Cython compiler for writing C extensions for the Python language https://www.python.org/ 11 Doxygen http://www.doxygen.org/ FFmpeg is the leading multimedia framework, able to decode, encode, transcode, mux, demux, stream, filter and play pretty much anything that humans and machines have created. It supports the most obscure ancient formats up to the cutting edge. No matter if they were designed by some standards 12 ffmpeg committee, the community or a corporation. https://www.ffmpeg.org FFTW is a C subroutine library for computing the discrete Fourier transform (DFT) in one or more dimensions, of arbitrary input size, and of both real and 13 fftw complex data (as well as of even/odd data, i.e. -
Upgrading and Performance Analysis of Thin Clients in Server Based Scientific Computing
Institutionen för Systemteknik Department of Electrical Engineering Examensarbete Upgrading and Performance Analysis of Thin Clients in Server Based Scientific Computing Master Thesis in ISY Communication System By Rizwan Azhar LiTH-ISY-EX - - 11/4388 - - SE Linköping 2011 Department of Electrical Engineering Linköpings Tekniska Högskola Linköpings universitet Linköpings universitet SE-581 83 Linköping, Sweden 581 83 Linköping, Sweden Upgrading and Performance Analysis of Thin Clients in Server Based Scientific Computing Master Thesis in ISY Communication System at Linköping Institute of Technology By Rizwan Azhar LiTH-ISY-EX - - 11/4388 - - SE Examiner: Dr. Lasse Alfredsson Advisor: Dr. Alexandr Malusek Supervisor: Dr. Peter Lundberg Presentation Date Department and Division 04-02-2011 Department of Electrical Engineering Publishing Date (Electronic version) Language Type of Publication ISBN (Licentiate thesis) X English Licentiate thesis ISRN: Other (specify below) X Degree thesis LiTH-ISY-EX - - 11/4388 - - SE Thesis C-level Thesis D-level Title of series (Licentiate thesis) 55 Report Number of Pages Other (specify below) Series number/ISSN (Licentiate thesis) URL, Electronic Version http://www.ep.liu.se Publication Title Upgrading and Performance Analysis of Thin Clients in Server Based Scientific Computing Author Rizwan Azhar Abstract Server Based Computing (SBC) technology allows applications to be deployed, managed, supported and executed on the server and not on the client; only the screen information is transmitted between the server and client. This architecture solves many fundamental problems with application deployment, technical support, data storage, hardware and software upgrades. This thesis is targeted at upgrading and evaluating performance of thin clients in scientific Server Based Computing (SBC). Performance of Linux based SBC was assessed via methods of both quantitative and qualitative research. -
Remote Visualization
Remote Visualization Ravi Chityala Mike Knox Nancy Rowe © 2011 Regents of the University of Minnesota. All rights reserved. Remote visualization • Motivation: Large data – Limited local storage – Lengthy data transfer time – Slow local graphics system • Data rendered where calculated • Challenges:Bandwidth – 1280 x 1024 pixels of 24 bits at 30 frames a second = 118 MBps • Latency of network and GPU © 2011 Regents of the University of Minnesota. All rights reserved. What is remote visualization at MSI? • Available on Jay and Koronis • Several ways to access • Experimental © 2011 Regents of the University of Minnesota. All rights reserved. Systems Jay • Nvidia Quadro FX5800 • 64G memory Koronis (SGI Altix UV Constellation); NIH graphics1 graphics[2-4] • Nvidia Quadro FX1800 • Nvidia Quadro FX5800 • 256G memory • 48G memory © 2011 Regents of the University of Minnesota. All rights reserved. Access to Systems Jay • Reservation • qsub -I -q jay • www.msi.umn.edu/hardware/itasca/jay.html Koronis NIH www.msi.umn.edu/hardware/koronis © 2011 Regents of the University of Minnesota. All rights reserved. Systems System Location of Single Aggregate temporary Read/Write Read/Write storage speed speed Jay /lustre ~400MB/s ~10GB/s ~400MB/s ~13.5GB/s Koronis /scratch ~1.5GB/s ~4 - 14GB/s ~1.5GB/s ~7- 16GB/s (highly variable) © 2011 Regents of the University of Minnesota. All rights reserved. Ways to access • KVM • Teradici • VirtualGL • Client/Server © 2011 Regents of the University of Minnesota. All rights reserved. KVM • Keyboard, Video and Mouse • Directly runs software • Installed in SDVL 575 © 2011 Regents of the University of Minnesota. All rights reserved. Teradici card • PCoIP • Host rendering • Just sends pixels • Installed in SDVL 575 © 2011 Regents of the University of Minnesota.