DPX®-S425 Is a Ground Breaking Computer Platform from Advantech-Innocore
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Release Notes for X11R6.8.2 the X.Orgfoundation the Xfree86 Project, Inc
Release Notes for X11R6.8.2 The X.OrgFoundation The XFree86 Project, Inc. 9February 2005 Abstract These release notes contains information about features and their status in the X.Org Foundation X11R6.8.2 release. It is based on the XFree86 4.4RC2 RELNOTES docu- ment published by The XFree86™ Project, Inc. Thereare significant updates and dif- ferences in the X.Orgrelease as noted below. 1. Introduction to the X11R6.8.2 Release The release numbering is based on the original MIT X numbering system. X11refers to the ver- sion of the network protocol that the X Window system is based on: Version 11was first released in 1988 and has been stable for 15 years, with only upwardcompatible additions to the coreX protocol, a recordofstability envied in computing. Formal releases of X started with X version 9 from MIT;the first commercial X products werebased on X version 10. The MIT X Consortium and its successors, the X Consortium, the Open Group X Project Team, and the X.OrgGroup released versions X11R3 through X11R6.6, beforethe founding of the X.OrgFoundation. Therewill be futuremaintenance releases in the X11R6.8.x series. However,efforts arewell underway to split the X distribution into its modular components to allow for easier maintenance and independent updates. We expect a transitional period while both X11R6.8 releases arebeing fielded and the modular release completed and deployed while both will be available as different consumers of X technology have different constraints on deployment. Wehave not yet decided how the modular X releases will be numbered. We encourage you to submit bug fixes and enhancements to bugzilla.freedesktop.orgusing the xorgproduct, and discussions on this server take place on <[email protected]>. -
Stephen Clarke-Willson
Contact Stephen Clarke-Willson www.linkedin.com/in/drstephencw Programmer, Producer, Executive (LinkedIn) Sammamish www.arena.net (Company) www.above-the-garage.com (Personal) Summary above-the-garage.com/blog (Blog) Software technology development leadership and management. Top Skills Specialties: Technical / Product team building and management; Systems Programming System Architecture experience as first, second and third level manager in fast growing Game Development companies; systems programming, systems architecture, technology development. Publications Guild Wars Microservices and 24/7 Uptime Guild Wars 2 - Scaling from one to Experience millions Applying Game Design To Virtual NCSOFT Environments VP of Technology Nano-Plasm March 2019 - Present (1 year 7 months) Bellevue, WA ArenaNet LLC 13 years 6 months Programmer in the role of Studio Technical Director May 2013 - March 2019 (5 years 11 months) Bellevue, WA Lead engineering staff (about 100 members) for "gaming as a service" with continuous high volume content creation and delivery at MMO scale. Translate business objectives into innovative yet achievable technical challenges. Created technical unit with flat reporting structure and peer input reviews. Programmer in the roles of Server Programmer / Server Team Lead October 2005 - April 2013 (7 years 7 months) Developing multi-threaded, restartable, dynamically updatable, high performance, internet-resilient, bug free server code for the MMO Guild Wars (1 and 2). Above the Garage Productions Programmer / Owner Page 1 of 4 May 2004 - October 2005 (1 year 6 months) Game Technology Developer, Above the Garage Productions Developed downloadable music system "DirectSong.com" (from payment system [PayPal] to delivery system to embedded music player using Microsoft WMA technology). -
Reviving the Development of Openchrome
Reviving the Development of OpenChrome Kevin Brace OpenChrome Project Maintainer / Developer XDC2017 September 21st, 2017 Outline ● About Me ● My Personal Story Behind OpenChrome ● Background on VIA Chrome Hardware ● The History of OpenChrome Project ● Past Releases ● Observations about Standby Resume ● Developmental Philosophy ● Developmental Challenges ● Strategies for Further Development ● Future Plans 09/21/2017 XDC2017 2 About Me ● EE (Electrical Engineering) background (B.S.E.E.) who specialized in digital design / computer architecture in college (pretty much the only undergraduate student “still” doing this stuff where I attended college) ● Graduated recently ● First time conference presenter ● Very experienced with Xilinx FPGA (Spartan-II through 7 Series FPGA) ● Fluent in Verilog / VHDL design and verification ● Interest / design experience with external communication interfaces (PCI / PCIe) and external memory interfaces (SDRAM / DDR3 SDRAM) ● Developed a simple DMA engine for PCI I/F validation w/Windows WDM (Windows Driver Model) kernel device driver ● Almost all the knowledge I have is self taught (university engineering classes were not very useful) 09/21/2017 XDC2017 3 Motivations Behind My Work ● General difficulty in obtaining meaningful employment in the digital hardware design field (too many students in the field, difficulty obtaining internship, etc.) ● Collects and repairs abandoned computer hardware (It’s like rescuing puppies!) ● Owns 100+ desktop computers and 20+ laptop computers (mostly abandoned old stuff I -
Intel® G31 Express Chipset Product Brief
Product Brief Intel® G31 Express Chipset Intel® G31 Express Chipset Flexibility and scalability for essential computing The Intel® G31 Express Chipset supports Intel’s upcoming 45nm processors and enables Windows Vista* premium experience for value conscious consumers. The Intel G31 Express Chipset Desktop PC platforms, combined with either the Intel® Core™2 Duo or the Intel® Core™2 Quad processor, deliver new technologies and innovating capabilities for all consumers. With a 1333MHz system bus, DDR2 memory technology and support for Windows Vista Premium, the Intel G31 Express chipset enables scalability and performance for essential computing. Support for 45nm processor technology and Intel® Fast Memory Access (Intel® FMA) provide increased system performance for today’s computing needs. The Intel G31 Express Chipset enables a Intel® I/O Controller Hub (Intel® ICH7/R) balanced platform for everyday computing needs. The Intel G31 Express Chipset elevates storage performance with Serial Intel® Viiv™ processor technology ATA (SATA) and enhancements to Intel® Matrix Storage Technology2. Intel® Viiv™ processor technology1 is a set of PC technologies designed This chipset has four integrated SATA ports for transfer rates up to 3 for the enjoyment of digital entertainment in the home. The Intel G31 Gb/s (300 MB/s) to SATA hard drives or optical devices. Support for RAID Express Chipset supports Intel Viiv processor technology with either the 0, 1, 5 and 10 allows for different RAID capabilities that address specific Intel® ICH7R or ICH7DH SKUs. needs and usages. For example, critical data can be stored on one array designed for high reliability, while performance-intensive applications like Faster System Performance games can reside on a separate array designed for maximum The Intel® G31 Express Chipset Graphics Memory Controller Hub (GMCH) performance. -
4010, 237 8514, 226 80486, 280 82786, 227, 280 a AA. See Anti-Aliasing (AA) Abacus, 16 Accelerated Graphics Port (AGP), 219 Acce
Index 4010, 237 AIB. See Add-in board (AIB) 8514, 226 Air traffic control system, 303 80486, 280 Akeley, Kurt, 242 82786, 227, 280 Akkadian, 16 Algebra, 26 Alias Research, 169 Alienware, 186 A Alioscopy, 389 AA. See Anti-aliasing (AA) All-In-One computer, 352 Abacus, 16 All-points addressable (APA), 221 Accelerated Graphics Port (AGP), 219 Alpha channel, 328 AccelGraphics, 166, 273 Alpha Processor, 164 Accel-KKR, 170 ALT-256, 223 ACM. See Association for Computing Altair 680b, 181 Machinery (ACM) Alto, 158 Acorn, 156 AMD, 232, 257, 277, 410, 411 ACRTC. See Advanced CRT Controller AMD 2901 bit-slice, 318 (ACRTC) American national Standards Institute (ANSI), ACS, 158 239 Action Graphics, 164, 273 Anaglyph, 376 Acumos, 253 Anaglyph glasses, 385 A.D., 15 Analog computer, 140 Adage, 315 Anamorphic distortion, 377 Adage AGT-30, 317 Anatomic and Symbolic Mapper Engine Adams Associates, 102 (ASME), 110 Adams, Charles W., 81, 148 Anderson, Bob, 321 Add-in board (AIB), 217, 363 AN/FSQ-7, 302 Additive color, 328 Anisotropic filtering (AF), 65 Adobe, 280 ANSI. See American national Standards Adobe RGB, 328 Institute (ANSI) Advanced CRT Controller (ACRTC), 226 Anti-aliasing (AA), 63 Advanced Remote Display Station (ARDS), ANTIC graphics co-processor, 279 322 Antikythera device, 127 Advanced Visual Systems (AVS), 164 APA. See All-points addressable (APA) AED 512, 333 Apalatequi, 42 AF. See Anisotropic filtering (AF) Aperture grille, 326 AGP. See Accelerated Graphics Port (AGP) API. See Application program interface Ahiska, Yavuz, 260 standard (API) AI. -
Multiprocessing Contents
Multiprocessing Contents 1 Multiprocessing 1 1.1 Pre-history .............................................. 1 1.2 Key topics ............................................... 1 1.2.1 Processor symmetry ...................................... 1 1.2.2 Instruction and data streams ................................. 1 1.2.3 Processor coupling ...................................... 2 1.2.4 Multiprocessor Communication Architecture ......................... 2 1.3 Flynn’s taxonomy ........................................... 2 1.3.1 SISD multiprocessing ..................................... 2 1.3.2 SIMD multiprocessing .................................... 2 1.3.3 MISD multiprocessing .................................... 3 1.3.4 MIMD multiprocessing .................................... 3 1.4 See also ................................................ 3 1.5 References ............................................... 3 2 Computer multitasking 5 2.1 Multiprogramming .......................................... 5 2.2 Cooperative multitasking ....................................... 6 2.3 Preemptive multitasking ....................................... 6 2.4 Real time ............................................... 7 2.5 Multithreading ............................................ 7 2.6 Memory protection .......................................... 7 2.7 Memory swapping .......................................... 7 2.8 Programming ............................................. 7 2.9 See also ................................................ 8 2.10 References ............................................. -
Manycore GPU Architectures and Programming, Part 1
Lecture 19: Manycore GPU Architectures and Programming, Part 1 Concurrent and Mul=core Programming CSE 436/536, [email protected] www.secs.oakland.edu/~yan 1 Topics (Part 2) • Parallel architectures and hardware – Parallel computer architectures – Memory hierarchy and cache coherency • Manycore GPU architectures and programming – GPUs architectures – CUDA programming – Introduc?on to offloading model in OpenMP and OpenACC • Programming on large scale systems (Chapter 6) – MPI (point to point and collec=ves) – Introduc?on to PGAS languages, UPC and Chapel • Parallel algorithms (Chapter 8,9 &10) – Dense matrix, and sorng 2 Manycore GPU Architectures and Programming: Outline • Introduc?on – GPU architectures, GPGPUs, and CUDA • GPU Execuon model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruc?on intrinsic and library • Performance, profiling, debugging, and error handling • Direc?ve-based high-level programming model – OpenACC and OpenMP 3 Computer Graphics GPU: Graphics Processing Unit 4 Graphics Processing Unit (GPU) Image: h[p://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 5 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient compung • Unlocking poten?als of complex apps • Enabling Deeper scien?fic discovery 6 What is GPU Today? • It is a processor op?mized for 2D/3D graphics, video, visual compu?ng, and display. • It is highly parallel, highly multhreaded mulprocessor op?mized for visual -
A Programming Model and Processor Architecture for Heterogeneous Multicore Computers
A PROGRAMMING MODEL AND PROCESSOR ARCHITECTURE FOR HETEROGENEOUS MULTICORE COMPUTERS A DISSERTATION SUBMITTED TO THE DEPARTMENT OF ELECTRICAL ENGINEERING AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Michael D. Linderman February 2009 c Copyright by Michael D. Linderman 2009 All Rights Reserved ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Professor Teresa H. Meng) Principal Adviser I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Professor Mark Horowitz) I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy. (Professor Krishna V. Shenoy) Approved for the University Committee on Graduate Studies. iii Abstract Heterogeneous multicore computers, those systems that integrate specialized accelerators into and alongside multicore general-purpose processors (GPPs), provide the scalable performance needed by computationally demanding information processing (informatics) applications. However, these systems often feature instruction sets and functionality that significantly differ from GPPs and for which there is often little or no sophisticated compiler support. Consequently developing applica- tions for these systems is difficult and developer productivity is low. This thesis presents Merge, a general-purpose programming model for heterogeneous multicore systems. The Merge programming model enables the programmer to leverage different processor- specific or application domain-specific toolchains to create software modules specialized for differ- ent hardware configurations; and provides language mechanisms to enable the automatic mapping of processor-agnostic applications to these processor-specific modules. -
University of Klagenfurt Digital Signal Processor (DSP) MD SARWAR
University of Klagenfurt Digital Signal Processor (DSP) GROUP MEMBERS : MD SARWAR ZAHAN (MATRIX:1461419) BASHIRU OTOKITI (MATRIX:1361474) Topic: “GPU Processing” Proc. IEEE 96(5), 2008 AGENDA Introduction GPU Algorithm About GPU CPU VS GPU Short History Application of GPU GPU Pipeline, Architecture Conclusion WHAT IS GPU ? A graphics processing unit (GPU) is a dedicated processor that performs rapid mathematical calculations for rendering high quality video and images . The Abstract goal of a GPU is to enable a representation of a 3D world as realistically as possible. SHORT HISTORY OF GPU 2010 to 1970s 1980s 1990s 2000 to 2010 present ARCADE SYSTEM BOARDS S3 GRAPHICS NVIDIA & AUDI NEC 7220 NVIDIA 3D GRAPHICS RENDERING PIPELINE Image: 3D GRAPHICS RENDERING PIPELINE Vertex Processing: Process and transform individual vertices. Rasterization: Convert each primitive into a set of fragments. Fragment Processing: Process individual fragments. Output Merging: Combine the fragments of all primitives into color-pixel for the display. GPU ARCHITECTURE Image: NVidia GeForce 6800 Series GPU Board Host (CPU). 6 parallel vertex processors (receive data from the host). Image: NVidia GeForce 6800 GPU Architecture triangle setup stage (takes care of primitive assembly). rasterizer stage which produces the fragments. 16 processors (computes the output colors of each fragment). GPU COMPUTING Parallelism is the future of computing. GPU has moved from a fixed-function into full-fledged parallel programmable processor. GPU follow a single program multiple-data (SPMD) programming model. Image: SPMD Model SPMD Tasks are split up and run simultaneously on multiple processors with different input for faster results. GPU SOFTWARE ENVIRONMENTS Famous languages for GPU programming: NVIDIA’s (CUDA) OpenCL HLSL Cg GPU PERFORMANCE EVALUATION Image: GPU Performance Scan performance on CPU, graphics-based GPU (using OpenGL), and direct-compute GPU (using CUDA). -
Virtual Texturing
Virtual Texturing DIPLOMARBEIT zur Erlangung des akademischen Grades Diplom-Ingenieur im Rahmen des Studiums Computergraphik/Digitale Bildverarbeitung eingereicht von Albert Julian Mayer Matrikelnummer 0126505 an der Fakultät für Informatik der Technischen Universität Wien Betreuung Betreuer: Associate Prof. Dipl.-Ing. Dipl.-Ing. Dr.techn. Michael Wimmer Wien, 14.10.2010 (Unterschrift Verfasser/) (Unterschrift Betreuer) Technische Universität Wien A-1040 Wien Karlsplatz 13 Tel. +43-1-58801-0 www.tuwien.ac.at • • • Abstract Virtual texturing (as presented by Mittring in ’Advanced Virtual Texture Topics’ and in distinction to clipmap-style systems, to which this term is also applied) is a solution to the problem of real-time rendering of scenes with vast amounts of texture data which does not fit into graphics or main memory. Virtual texturing works by preprocessing the aggregate texture data into equally-sized tiles and determining the necessary tiles for rendering before each frame. These tiles are then streamed to the graphics card and rendering is performed with a special virtual texturing fragment shader that does texture coordinate adjustments to sample from the tile storage texture. A thorough description of virtual texturing and related topics is given, along with an examination of specific challenges including preprocessing, visible tile determination, texture filtering, tile importance metrics and many more. Tile determination in view space is examined in detail and an implementation for compressing the resulting buffer in OpenCL is presented. Rendering with correct texture filtering from a texture which contains de-correlated texture tiles is attained by using tile borders with specific coordi- nate adjustment and gradient correction in the fragment shader. -
Lecture: Manycore GPU Architectures and Programming, Part 1
Lecture: Manycore GPU Architectures and Programming, Part 1 CSCE 569 Parallel Computing Department of Computer Science and Engineering Yonghong Yan [email protected] https://passlab.github.io/CSCE569/ 1 Manycore GPU Architectures and Programming: Outline • Introduction – GPU architectures, GPGPUs, and CUDA • GPU Execution model • CUDA Programming model • Working with Memory in CUDA – Global memory, shared and constant memory • Streams and concurrency • CUDA instruction intrinsic and library • Performance, profiling, debugging, and error handling • Directive-based high-level programming model – OpenACC and OpenMP 2 Computer Graphics GPU: Graphics Processing Unit 3 Graphics Processing Unit (GPU) Image: http://www.ntu.edu.sg/home/ehchua/programming/opengl/CG_BasicsTheory.html 4 Graphics Processing Unit (GPU) • Enriching user visual experience • Delivering energy-efficient computing • Unlocking potentials of complex apps • Enabling Deeper scientific discovery 5 What is GPU Today? • It is a processor optimized for 2D/3D graphics, video, visual computing, and display. • It is highly parallel, highly multithreaded multiprocessor optimized for visual computing. • It provide real-time visual interaction with computed objects via graphics images, and video. • It serves as both a programmable graphics processor and a scalable parallel computing platform. – Heterogeneous systems: combine a GPU with a CPU • It is called as Many-core 6 Graphics Processing Units (GPUs): Brief History GPU Computing General-purpose computing on graphics processing units (GPGPUs) GPUs with programmable shading Nvidia GeForce GE 3 (2001) with programmable shading DirectX graphics API OpenGL graphics API Hardware-accelerated 3D graphics S3 graphics cards- single chip 2D accelerator Atari 8-bit computer IBM PC Professional Playstation text/graphics chip Graphics Controller card 1970 1980 1990 2000 2010 Source of information http://en.wikipedia.org/wiki/Graphics_Processing_Unit 7 NVIDIA Products • NVIDIA Corp. -
Graphical Process Unit a New Era
Nov 2014 (Volume 1 Issue 6) JETIR (ISSN-2349-5162) Graphical Process Unit A New Era Santosh Kumar, Shashi Bhushan Jha, Rupesh Kumar Singh Students Computer Science and Engineering Dronacharya College of Engineering, Greater Noida, India Abstract - Now in present days every computer is come with G.P.U (graphical process unit). The graphics processing unit (G.P.U) has become an essential part of today's mainstream computing systems. Over the past 6 years, there has been a marked increase in the performance and potentiality of G.P.U. The modern G.P.Us is not only a powerful graphics engine but also a deeply parallel programmable processor showing peak arithmetic and memory bandwidth that substantially outpaces its CPU counterpart. The G.P.U's speedy increase in both programmability and capability has spawned a research community that has successfully mapped a broad area of computationally demanding, mixed problems to the G.P.U. This effort in general-purpose computing on the G.P.Us, also known as G.P.U computing, has positioned the G.P.U as a compelling alternative to traditional microprocessors in high-performance computer systems of the future. We illustrate the history, hardware, and programming model for G.P.U computing, abstract the state of the art in tools and techniques, and present 4 G.P.U computing successes in games physics and computational physics that deliver order-of- magnitude performance gains over optimized CPU applications. Index Terms - G.P.U, History of G.P.U, Future of G.P.U, Problems in G.P.U, eG.P.U, Integrated graphics ________________________________________________________________________________________________________ I.