GPGPU Computing. Yong Cao
|
|
|
- Cornelia Logan
- 7 years ago
- Views:
Transcription
1 GPGPU Computing Yong Cao
2 Why Graphics Card? It s powerful! A quiet trend Copyright 2009 by Yong Cao
3 Why Graphics Card? It s powerful! Processor Processing Units FLOPs per Unit Clock Speed Processing Power High End Qual-Core CPU 16 (4 per core) MHz 96 GFLOPs NVIDIA GTX MHz 1063 GFLOPs ATI Radeon HD MHz 1200 GFLOPs Copyright 2009 by Yong Cao
4 Why Graphics Card? It s powerful! Die size 576 mm Billion Transistors Memory width: 512bit Bandwidth 142GB/s Max board Power:236 W GTX285: 183 W (55nm) Copyright 2009 by Yong Cao
5 Why Graphics Card? It s cheap and everywhere. E.g. NIVIDA sold 100 Million high-end GPGPU devices. A 128-core Geforce 9800GTX (648 GFLOPs) is $129 on Newegg.com. Copyright 2009 by Yong Cao
6 Why Graphics Card NOW? Before: f Two years ago, everyone is using Graphics API (Cg, GLSL, HLSL) for GPGPU programming. Restrict random-read (using Texture), NOT be able to random-write. (No pointer!) Copyright 2009 by Yong Cao
7 Why Graphics Card NOW? Now: NIVIDA released CUDA two years ago, since then Thousands d of CUDA software engineers New job title CUDA programmer Around 200 CUDA based technical publications! Why? Standard C language Support Pointer! Random read and write on GPU memory. Work with C++, Fortran Copyright 2009 by Yong Cao
8 Where s GPU in the system Copyright 2009 by Yong Cao
9 NVIDIA GPU Architecture t Lots of ALUs Lots of Control Focus on Graphics Applicants (Data parallel) Copyright 2009 by Yong Cao
10 NVIDIA GT200 Architecture t Thread Processing Cluster Atomic Memory Access Control Copyright 2009 by Yong Cao
11 NVIDIA GT200 Architecture t Multi-Processor Copyright 2009 by Yong Cao
12 Execution Mode Graphics Host Input Assembler Vtx Thread Issue Geom Thread Issue Setup / Rstr / ZCull Pixel Thread Issue SP TF L1 SP SP SP SP SP SP SP SP SP SP SP SP SP SP SP TF TF TF TF TF TF TF L1 L1 L1 L1 L1 L1 L1 Thread Pr rocessor L2 L2 L2 L2 L2 L2 FB FB FB FB FB FB NVIDIA G80 GPU Copyright 2009 by Yong Cao, Referencing UIUC ECE498AL Course Notes 13
13 Execution Mode General Computing Host Input Assembler Thread Execution Manager Parallel Data Cache Parallel Data Cache Parallel Data Cache Parallel Data Cache Parallel Data Cache Parallel Data Cache Parallel Data Cache Parallel Data Cache Texture Texture Texture Texture Texture Texture Texture Texture Load/store Load/store Load/store Load/store Load/store Load/store Global Memory NVIDIA G80 GPU 14 Copyright 2009 by Yong Cao, Referencing UIUC ECE498AL Course Notes
14 GPGPU from Graphics Point of View Graphics Processing Pipeline (on GPU) Fixed function pipeline Programmable pipeline with Shaders GPU Processing Model Stream computing model
15 3D Graphics Applications Demos.
16 GPU Fundamentals: The Graphics Pipeline Graphics State Application Vertices (3 3D) Transform & Light Xformed, Lit Ve ertices (2D) Assemble Primitives Screenspace tr iangles (2D) Rasterize Fragments (pre e-pixels) Fragment Processing Final Pixels (Co olor, Depth) Video Memory (Textures) CPU GPU Render-to-texture A simplified graphics pipeline Copyright 2009 by Yong Cao, Referencing SIGGRAPH 2005 Course Notes from David Luebke
17 Programmable Graphics Pipeline - Shaders Graphics State Application Vertices (3 3D) Vertex Processor Transform Xformed, Lit Ve ertices (2D) Assemble Primitives Screenspace tr iangles (2D) Fragments (pre e-pixels) Fragment Final Pixels (Co olor, Depth) Rasterizee Shade Video Processor Memory (Textures) CPU GPU Render-to-texture Programmable vertex processor! Programmable fragment processor! Copyright 2009 by Yong Cao, Referencing SIGGRAPH 2005 Course Notes from David Luebke
18 GPU Pipeline: Transform Vertex processor (multiple in parallel) Transform from world space to image space Compute per-vertex lighting Copyright 2009 by Yong Cao, Referencing SIGGRAPH 2005 Course Notes from David Luebke
19 GPU Pipeline: Rasterize Rasterizer Convert geometric rep. (vertex) to image rep. (fragment) Fragment = image fragment Pixel + associated data: color, depth, stencil, etc. Interpolate per-vertex quantities across pixels Copyright 2009 by Yong Cao, Referencing SIGGRAPH 2005 Course Notes from David Luebke
20 Pixel / Fragment Processor Fragment processors (multiple in parallel) Compute a color for each pixel Optionally read colors from textures (images) Copyright 2009 by Yong Cao, Referencing SIGGRAPH 2005 Course Notes from David Luebke
21 GPU Programming g Model: Stream Stream Programming g Model Streams: An array of data units Kernels: Stream Kernel Take streams as input, produce streams at output Perform computation on streams Kernels can be linked together Stream
22 Why Streams? GPGPU Computing Ample computation by exposing parallelism Stream expose data parallelism Multiple stream elements can be processed in parallel Pipeline (task) parallelism li Multiple tasks can be processed in parallel Efficient communication Producer-consumer locality Predictable memory access pattern Optimize for throughput of all elements, not latency of one Processing many elements at once allows latency hiding
23 Reading Material NVIDIA CUDA Programming g Guide 2.0, Chapter One develop html and looking for documentation NVIDIA Geforce GTX 280 Technical Brief PU_Technical_Brief.pdf Copyright 2009 by Yong Cao
Graphics Cards and Graphics Processing Units. Ben Johnstone Russ Martin November 15, 2011
Graphics Cards and Graphics Processing Units Ben Johnstone Russ Martin November 15, 2011 Contents Graphics Processing Units (GPUs) Graphics Pipeline Architectures 8800-GTX200 Fermi Cayman Performance Analysis
Computer Graphics Hardware An Overview
Computer Graphics Hardware An Overview Graphics System Monitor Input devices CPU/Memory GPU Raster Graphics System Raster: An array of picture elements Based on raster-scan TV technology The screen (and
The Evolution of Computer Graphics. SVP, Content & Technology, NVIDIA
The Evolution of Computer Graphics Tony Tamasi SVP, Content & Technology, NVIDIA Graphics Make great images intricate shapes complex optical effects seamless motion Make them fast invent clever techniques
Real-Time Realistic Rendering. Michael Doggett Docent Department of Computer Science Lund university
Real-Time Realistic Rendering Michael Doggett Docent Department of Computer Science Lund university 30-5-2011 Visually realistic goal force[d] us to completely rethink the entire rendering process. Cook
Introduction to GPGPU. Tiziano Diamanti t.diamanti@cineca.it
t.diamanti@cineca.it Agenda From GPUs to GPGPUs GPGPU architecture CUDA programming model Perspective projection Vectors that connect the vanishing point to every point of the 3D model will intersecate
GPU Architecture. Michael Doggett ATI
GPU Architecture Michael Doggett ATI GPU Architecture RADEON X1800/X1900 Microsoft s XBOX360 Xenos GPU GPU research areas ATI - Driving the Visual Experience Everywhere Products from cell phones to super
Introduction to GPU Programming Languages
CSC 391/691: GPU Programming Fall 2011 Introduction to GPU Programming Languages Copyright 2011 Samuel S. Cho http://www.umiacs.umd.edu/ research/gpu/facilities.html Maryland CPU/GPU Cluster Infrastructure
Introduction to GP-GPUs. Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1
Introduction to GP-GPUs Advanced Computer Architectures, Cristina Silvano, Politecnico di Milano 1 GPU Architectures: How do we reach here? NVIDIA Fermi, 512 Processing Elements (PEs) 2 What Can It Do?
L20: GPU Architecture and Models
L20: GPU Architecture and Models scribe(s): Abdul Khalifa 20.1 Overview GPUs (Graphics Processing Units) are large parallel structure of processing cores capable of rendering graphics efficiently on displays.
Lecture 3: Modern GPUs A Hardware Perspective Mohamed Zahran (aka Z) mzahran@cs.nyu.edu http://www.mzahran.com
CSCI-GA.3033-012 Graphics Processing Units (GPUs): Architecture and Programming Lecture 3: Modern GPUs A Hardware Perspective Mohamed Zahran (aka Z) mzahran@cs.nyu.edu http://www.mzahran.com Modern GPU
NVIDIA CUDA Software and GPU Parallel Computing Architecture. David B. Kirk, Chief Scientist
NVIDIA CUDA Software and GPU Parallel Computing Architecture David B. Kirk, Chief Scientist Outline Applications of GPU Computing CUDA Programming Model Overview Programming in CUDA The Basics How to Get
Introduction GPU Hardware GPU Computing Today GPU Computing Example Outlook Summary. GPU Computing. Numerical Simulation - from Models to Software
GPU Computing Numerical Simulation - from Models to Software Andreas Barthels JASS 2009, Course 2, St. Petersburg, Russia Prof. Dr. Sergey Y. Slavyanov St. Petersburg State University Prof. Dr. Thomas
NVIDIA GeForce GTX 580 GPU Datasheet
NVIDIA GeForce GTX 580 GPU Datasheet NVIDIA GeForce GTX 580 GPU Datasheet 3D Graphics Full Microsoft DirectX 11 Shader Model 5.0 support: o NVIDIA PolyMorph Engine with distributed HW tessellation engines
Radeon HD 2900 and Geometry Generation. Michael Doggett
Radeon HD 2900 and Geometry Generation Michael Doggett September 11, 2007 Overview Introduction to 3D Graphics Radeon 2900 Starting Point Requirements Top level Pipeline Blocks from top to bottom Command
GPUs Under the Hood. Prof. Aaron Lanterman School of Electrical and Computer Engineering Georgia Institute of Technology
GPUs Under the Hood Prof. Aaron Lanterman School of Electrical and Computer Engineering Georgia Institute of Technology Bandwidth Gravity of modern computer systems The bandwidth between key components
GPU(Graphics Processing Unit) with a Focus on Nvidia GeForce 6 Series. By: Binesh Tuladhar Clay Smith
GPU(Graphics Processing Unit) with a Focus on Nvidia GeForce 6 Series By: Binesh Tuladhar Clay Smith Overview History of GPU s GPU Definition Classical Graphics Pipeline Geforce 6 Series Architecture Vertex
Radeon GPU Architecture and the Radeon 4800 series. Michael Doggett Graphics Architecture Group June 27, 2008
Radeon GPU Architecture and the series Michael Doggett Graphics Architecture Group June 27, 2008 Graphics Processing Units Introduction GPU research 2 GPU Evolution GPU started as a triangle rasterizer
Introduction to GPU Architecture
Introduction to GPU Architecture Ofer Rosenberg, PMTS SW, OpenCL Dev. Team AMD Based on From Shader Code to a Teraflop: How GPU Shader Cores Work, By Kayvon Fatahalian, Stanford University Content 1. Three
GPGPU for Real-Time Data Analytics: Introduction. Nanyang Technological University, Singapore 2
GPGPU for Real-Time Data Analytics: Introduction Bingsheng He 1, Huynh Phung Huynh 2, Rick Siow Mong Goh 2 1 Nanyang Technological University, Singapore 2 A*STAR Institute of High Performance Computing,
Lecture 11: Multi-Core and GPU. Multithreading. Integration of multiple processor cores on a single chip.
Lecture 11: Multi-Core and GPU Multi-core computers Multithreading GPUs General Purpose GPUs Zebo Peng, IDA, LiTH 1 Multi-Core System Integration of multiple processor cores on a single chip. To provide
QCD as a Video Game?
QCD as a Video Game? Sándor D. Katz Eötvös University Budapest in collaboration with Győző Egri, Zoltán Fodor, Christian Hoelbling Dániel Nógrádi, Kálmán Szabó Outline 1. Introduction 2. GPU architecture
Recent Advances and Future Trends in Graphics Hardware. Michael Doggett Architect November 23, 2005
Recent Advances and Future Trends in Graphics Hardware Michael Doggett Architect November 23, 2005 Overview XBOX360 GPU : Xenos Rendering performance GPU architecture Unified shader Memory Export Texture/Vertex
GPGPU: General-Purpose Computation on GPUs
GPGPU: General-Purpose Computation on GPUs Randy Fernando NVIDIA Developer Technology Group (Original Slides Courtesy of Mark Harris) Why GPGPU? The GPU has evolved into an extremely flexible and powerful
GPU Computing with CUDA Lecture 2 - CUDA Memories. Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile
GPU Computing with CUDA Lecture 2 - CUDA Memories Christopher Cooper Boston University August, 2011 UTFSM, Valparaíso, Chile 1 Outline of lecture Recap of Lecture 1 Warp scheduling CUDA Memory hierarchy
General Purpose Computation on Graphics Processors (GPGPU) Mike Houston, Stanford University
General Purpose Computation on Graphics Processors (GPGPU) Mike Houston, Stanford University A little about me http://graphics.stanford.edu/~mhouston Education: UC San Diego, Computer Science BS Stanford
An Introduction to Modern GPU Architecture Ashu Rege Director of Developer Technology
An Introduction to Modern GPU Architecture Ashu Rege Director of Developer Technology Agenda Evolution of GPUs Computing Revolution Stream Processing Architecture details of modern GPUs Evolution of GPUs
Next Generation GPU Architecture Code-named Fermi
Next Generation GPU Architecture Code-named Fermi The Soul of a Supercomputer in the Body of a GPU Why is NVIDIA at Super Computing? Graphics is a throughput problem paint every pixel within frame time
Data Parallel Computing on Graphics Hardware. Ian Buck Stanford University
Data Parallel Computing on Graphics Hardware Ian Buck Stanford University Brook General purpose Streaming language DARPA Polymorphous Computing Architectures Stanford - Smart Memories UT Austin - TRIPS
NVIDIA GeForce GTX 750 Ti
Whitepaper NVIDIA GeForce GTX 750 Ti Featuring First-Generation Maxwell GPU Technology, Designed for Extreme Performance per Watt V1.1 Table of Contents Table of Contents... 1 Introduction... 3 The Soul
GPU Architectures. A CPU Perspective. Data Parallelism: What is it, and how to exploit it? Workload characteristics
GPU Architectures A CPU Perspective Derek Hower AMD Research 5/21/2013 Goals Data Parallelism: What is it, and how to exploit it? Workload characteristics Execution Models / GPU Architectures MIMD (SPMD),
CUDA programming on NVIDIA GPUs
p. 1/21 on NVIDIA GPUs Mike Giles mike.giles@maths.ox.ac.uk Oxford University Mathematical Institute Oxford-Man Institute for Quantitative Finance Oxford eresearch Centre p. 2/21 Overview hardware view
Texture Cache Approximation on GPUs
Texture Cache Approximation on GPUs Mark Sutherland Joshua San Miguel Natalie Enright Jerger {suther68,enright}@ece.utoronto.ca, joshua.sanmiguel@mail.utoronto.ca 1 Our Contribution GPU Core Cache Cache
GPUs for Scientific Computing
GPUs for Scientific Computing p. 1/16 GPUs for Scientific Computing Mike Giles mike.giles@maths.ox.ac.uk Oxford-Man Institute of Quantitative Finance Oxford University Mathematical Institute Oxford e-research
Low power GPUs a view from the industry. Edvard Sørgård
Low power GPUs a view from the industry Edvard Sørgård 1 ARM in Trondheim Graphics technology design centre From 2006 acquisition of Falanx Microsystems AS Origin of the ARM Mali GPUs Main activities today
CSE 564: Visualization. GPU Programming (First Steps) GPU Generations. Klaus Mueller. Computer Science Department Stony Brook University
GPU Generations CSE 564: Visualization GPU Programming (First Steps) Klaus Mueller Computer Science Department Stony Brook University For the labs, 4th generation is desirable Graphics Hardware Pipeline
Introduction to GPU hardware and to CUDA
Introduction to GPU hardware and to CUDA Philip Blakely Laboratory for Scientific Computing, University of Cambridge Philip Blakely (LSC) GPU introduction 1 / 37 Course outline Introduction to GPU hardware
ATI Radeon 4800 series Graphics. Michael Doggett Graphics Architecture Group Graphics Product Group
ATI Radeon 4800 series Graphics Michael Doggett Graphics Architecture Group Graphics Product Group Graphics Processing Units ATI Radeon HD 4870 AMD Stream Computing Next Generation GPUs 2 Radeon 4800 series
Console Architecture. By: Peter Hood & Adelia Wong
Console Architecture By: Peter Hood & Adelia Wong Overview Gaming console timeline and evolution Overview of the original xbox architecture Console architecture of the xbox360 Future of the xbox series
GPU System Architecture. Alan Gray EPCC The University of Edinburgh
GPU System Architecture EPCC The University of Edinburgh Outline Why do we want/need accelerators such as GPUs? GPU-CPU comparison Architectural reasons for GPU performance advantages GPU accelerated systems
Programming models for heterogeneous computing. Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga
Programming models for heterogeneous computing Manuel Ujaldón Nvidia CUDA Fellow and A/Prof. Computer Architecture Department University of Malaga Talk outline [30 slides] 1. Introduction [5 slides] 2.
GPU Hardware and Programming Models. Jeremy Appleyard, September 2015
GPU Hardware and Programming Models Jeremy Appleyard, September 2015 A brief history of GPUs In this talk Hardware Overview Programming Models Ask questions at any point! 2 A Brief History of GPUs 3 Once
1. INTRODUCTION Graphics 2
1. INTRODUCTION Graphics 2 06-02408 Level 3 10 credits in Semester 2 Professor Aleš Leonardis Slides by Professor Ela Claridge What is computer graphics? The art of 3D graphics is the art of fooling the
OpenGL Performance Tuning
OpenGL Performance Tuning Evan Hart ATI Pipeline slides courtesy John Spitzer - NVIDIA Overview What to look for in tuning How it relates to the graphics pipeline Modern areas of interest Vertex Buffer
Shader Model 3.0. Ashu Rege. NVIDIA Developer Technology Group
Shader Model 3.0 Ashu Rege NVIDIA Developer Technology Group Talk Outline Quick Intro GeForce 6 Series (NV4X family) New Vertex Shader Features Vertex Texture Fetch Longer Programs and Dynamic Flow Control
Overview. Lecture 1: an introduction to CUDA. Hardware view. Hardware view. hardware view software view CUDA programming
Overview Lecture 1: an introduction to CUDA Mike Giles mike.giles@maths.ox.ac.uk hardware view software view Oxford University Mathematical Institute Oxford e-research Centre Lecture 1 p. 1 Lecture 1 p.
This Unit: Putting It All Together. CIS 501 Computer Architecture. Sources. What is Computer Architecture?
This Unit: Putting It All Together CIS 501 Computer Architecture Unit 11: Putting It All Together: Anatomy of the XBox 360 Game Console Slides originally developed by Amir Roth with contributions by Milo
E6895 Advanced Big Data Analytics Lecture 14:! NVIDIA GPU Examples and GPU on ios devices
E6895 Advanced Big Data Analytics Lecture 14: NVIDIA GPU Examples and GPU on ios devices Ching-Yung Lin, Ph.D. Adjunct Professor, Dept. of Electrical Engineering and Computer Science IBM Chief Scientist,
LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR
LBM BASED FLOW SIMULATION USING GPU COMPUTING PROCESSOR Frédéric Kuznik, frederic.kuznik@insa lyon.fr 1 Framework Introduction Hardware architecture CUDA overview Implementation details A simple case:
A Crash Course on Programmable Graphics Hardware
A Crash Course on Programmable Graphics Hardware Li-Yi Wei Abstract Recent years have witnessed tremendous growth for programmable graphics hardware (GPU), both in terms of performance and functionality.
The Future Of Animation Is Games
The Future Of Animation Is Games 王 銓 彰 Next Media Animation, Media Lab, Director cwang@1-apple.com.tw The Graphics Hardware Revolution ( 繪 圖 硬 體 革 命 ) : GPU-based Graphics Hardware Multi-core (20 Cores
Introduction to GPU Computing
Matthis Hauschild Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Technische Aspekte Multimodaler Systeme December 4, 2014 M. Hauschild - 1 Table of Contents 1. Architecture
AMD GPU Architecture. OpenCL Tutorial, PPAM 2009. Dominik Behr September 13th, 2009
AMD GPU Architecture OpenCL Tutorial, PPAM 2009 Dominik Behr September 13th, 2009 Overview AMD GPU architecture How OpenCL maps on GPU and CPU How to optimize for AMD GPUs and CPUs in OpenCL 2 AMD GPU
Why You Need the EVGA e-geforce 6800 GS
Why You Need the EVGA e-geforce 6800 GS GeForce 6800 GS Profile NVIDIA s announcement of a new GPU product hailing from the now legendary GeForce 6 series adds new fire to the lineup in the form of the
NVIDIA workstation 3D graphics card upgrade options deliver productivity improvements and superior image quality
Hardware Announcement ZG09-0170, dated March 31, 2009 NVIDIA workstation 3D graphics card upgrade options deliver productivity improvements and superior image quality Table of contents 1 At a glance 3
CUDA. Multicore machines
CUDA GPU vs Multicore computers Multicore machines Emphasize multiple full-blown processor cores, implementing the complete instruction set of the CPU The cores are out-of-order implying that they could
In the early 1990s, ubiquitous
How GPUs Work David Luebke, NVIDIA Research Greg Humphreys, University of Virginia In the early 1990s, ubiquitous interactive 3D graphics was still the stuff of science fiction. By the end of the decade,
Parallel Programming Survey
Christian Terboven 02.09.2014 / Aachen, Germany Stand: 26.08.2014 Version 2.3 IT Center der RWTH Aachen University Agenda Overview: Processor Microarchitecture Shared-Memory
Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA
Accelerating Intensity Layer Based Pencil Filter Algorithm using CUDA Dissertation submitted in partial fulfillment of the requirements for the degree of Master of Technology, Computer Engineering by Amol
ME964 High Performance Computing for Engineering Applications
ME964 High Performance Computing for Engineering Applications Intro, GPU Computing February 9, 2012 Dan Negrut, 2012 ME964 UW-Madison "The Internet is a great way to get on the net. US Senator Bob Dole
Fast Implementations of AES on Various Platforms
Fast Implementations of AES on Various Platforms Joppe W. Bos 1 Dag Arne Osvik 1 Deian Stefan 2 1 EPFL IC IIF LACAL, Station 14, CH-1015 Lausanne, Switzerland {joppe.bos, dagarne.osvik}@epfl.ch 2 Dept.
GPU Computing - CUDA
GPU Computing - CUDA A short overview of hardware and programing model Pierre Kestener 1 1 CEA Saclay, DSM, Maison de la Simulation Saclay, June 12, 2012 Atelier AO and GPU 1 / 37 Content Historical perspective
GPU Parallel Computing Architecture and CUDA Programming Model
GPU Parallel Computing Architecture and CUDA Programming Model John Nickolls Outline Why GPU Computing? GPU Computing Architecture Multithreading and Arrays Data Parallel Problem Decomposition Parallel
Developer Tools. Tim Purcell NVIDIA
Developer Tools Tim Purcell NVIDIA Programming Soap Box Successful programming systems require at least three tools High level language compiler Cg, HLSL, GLSL, RTSL, Brook Debugger Profiler Debugging
NVIDIA Tools For Profiling And Monitoring. David Goodwin
NVIDIA Tools For Profiling And Monitoring David Goodwin Outline CUDA Profiling and Monitoring Libraries Tools Technologies Directions CScADS Summer 2012 Workshop on Performance Tools for Extreme Scale
GPU architecture II: Scheduling the graphics pipeline
GPU architecture II: Scheduling the graphics pipeline Mike Houston, AMD / Stanford Aaron Lefohn, Intel / University of Washington 1 Notes The front half of this talk is almost verbatim from: Keeping Many
ST810 Advanced Computing
ST810 Advanced Computing Lecture 17: Parallel computing part I Eric B. Laber Hua Zhou Department of Statistics North Carolina State University Mar 13, 2013 Outline computing Hardware computing overview
Stream Processing on GPUs Using Distributed Multimedia Middleware
Stream Processing on GPUs Using Distributed Multimedia Middleware Michael Repplinger 1,2, and Philipp Slusallek 1,2 1 Computer Graphics Lab, Saarland University, Saarbrücken, Germany 2 German Research
Graphics and Computing GPUs
C A P P E N D I X Imagination is more important than knowledge. Albert Einstein On Science, 1930s Graphics and Computing GPUs John Nickolls Director of Architecture NVIDIA David Kirk Chief Scientist NVIDIA
Optimization for DirectX9 Graphics. Ashu Rege
Optimization for DirectX9 Graphics Ashu Rege Last Year: Batch, Batch, Batch Moral of the story: Small batches BAD What is a batch Every DrawIndexedPrimitive call is a batch All render, texture, shader,...
Benchmark Hadoop and Mars: MapReduce on cluster versus on GPU
Benchmark Hadoop and Mars: MapReduce on cluster versus on GPU Heshan Li, Shaopeng Wang The Johns Hopkins University 3400 N. Charles Street Baltimore, Maryland 21218 {heshanli, shaopeng}@cs.jhu.edu 1 Overview
ultra fast SOM using CUDA
ultra fast SOM using CUDA SOM (Self-Organizing Map) is one of the most popular artificial neural network algorithms in the unsupervised learning category. Sijo Mathew Preetha Joy Sibi Rajendra Manoj A
Writing Applications for the GPU Using the RapidMind Development Platform
Writing Applications for the GPU Using the RapidMind Development Platform Contents Introduction... 1 Graphics Processing Units... 1 RapidMind Development Platform... 2 Writing RapidMind Enabled Applications...
Image Processing and Computer Graphics. Rendering Pipeline. Matthias Teschner. Computer Science Department University of Freiburg
Image Processing and Computer Graphics Rendering Pipeline Matthias Teschner Computer Science Department University of Freiburg Outline introduction rendering pipeline vertex processing primitive processing
Accelerating Wavelet-Based Video Coding on Graphics Hardware
Wladimir J. van der Laan, Andrei C. Jalba, and Jos B.T.M. Roerdink. Accelerating Wavelet-Based Video Coding on Graphics Hardware using CUDA. In Proc. 6th International Symposium on Image and Signal Processing
Real-Time Graphics Architecture
Real-Time Graphics Architecture Kurt Akeley Pat Hanrahan http://www.graphics.stanford.edu/courses/cs448a-01-fall Display and Framebuffer Displays Key properties Bandwidth Framebuffers Definitions and key
Videocard Benchmarks Over 600,000 Video Cards Benchmarked
1 z 5 2013-07-16 10:45 Shopping cart Search Home Software Hardware Benchmarks Services Store Support Forums About Us Home» Video Card Benchmarks» Mid to High Range Video Cards CPU Benchmarks Video Card
Ray Tracing on Graphics Hardware
Ray Tracing on Graphics Hardware Toshiya Hachisuka University of California, San Diego Abstract Ray tracing is one of the important elements in photo-realistic image synthesis. Since ray tracing is computationally
Guided Performance Analysis with the NVIDIA Visual Profiler
Guided Performance Analysis with the NVIDIA Visual Profiler Identifying Performance Opportunities NVIDIA Nsight Eclipse Edition (nsight) NVIDIA Visual Profiler (nvvp) nvprof command-line profiler Guided
PARALLEL DISCRETE EVENT SIMULATION OF QUEUING NETWORKS USING GPU-BASED HARDWARE ACCELERATION
PARALLEL DISCRETE EVENT SIMULATION OF QUEUING NETWORKS USING GPU-BASED HARDWARE ACCELERATION By HYUNGWOOK PARK A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
Overview Motivation and applications Challenges. Dynamic Volume Computation and Visualization on the GPU. GPU feature requests Conclusions
Module 4: Beyond Static Scalar Fields Dynamic Volume Computation and Visualization on the GPU Visualization and Computer Graphics Group University of California, Davis Overview Motivation and applications
Brook for GPUs: Stream Computing on Graphics Hardware
Brook for GPUs: Stream Computing on Graphics Hardware Ian Buck, Tim Foley, Daniel Horn, Jeremy Sugerman, Kayvon Fatahalian, Mike Houston, and Pat Hanrahan Computer Science Department Stanford University
Fast Software AES Encryption
Calhoun: The NPS Institutional Archive Faculty and Researcher Publications Faculty and Researcher Publications 2010 Fast Software AES Encryption Osvik, Dag Arne Proceedings FSE'10 Proceedings of the 17th
OpenCL Optimization. San Jose 10/2/2009 Peng Wang, NVIDIA
OpenCL Optimization San Jose 10/2/2009 Peng Wang, NVIDIA Outline Overview The CUDA architecture Memory optimization Execution configuration optimization Instruction optimization Summary Overall Optimization
Interactive Rendering In The Post-GPU Era. Matt Pharr Graphics Hardware 2006
Interactive Rendering In The Post-GPU Era Matt Pharr Graphics Hardware 2006 Last Five Years Rise of programmable GPUs 100s/GFLOPS compute 10s/GB/s bandwidth Architecture characteristics have deeply influenced
Evaluation of CUDA Fortran for the CFD code Strukti
Evaluation of CUDA Fortran for the CFD code Strukti Practical term report from Stephan Soller High performance computing center Stuttgart 1 Stuttgart Media University 2 High performance computing center
Real-time Visual Tracker by Stream Processing
Real-time Visual Tracker by Stream Processing Simultaneous and Fast 3D Tracking of Multiple Faces in Video Sequences by Using a Particle Filter Oscar Mateo Lozano & Kuzahiro Otsuka presented by Piotr Rudol
Videocard Benchmarks Over 600,000 Video Cards Benchmarked
Strona 1 z 8 Shopping cart Search Home Software Hardware Benchmarks Services Store Support Forums About Us Home» Video Card Benchmarks» High End Video Cards CPU Benchmarks Video Card Benchmarks Hard Drive
Go Faster - Preprocessing Using FPGA, CPU, GPU. Dipl.-Ing. (FH) Bjoern Rudde Image Acquisition Development STEMMER IMAGING
Go Faster - Preprocessing Using FPGA, CPU, GPU Dipl.-Ing. (FH) Bjoern Rudde Image Acquisition Development STEMMER IMAGING WHO ARE STEMMER IMAGING? STEMMER IMAGING is: Europe's leading independent provider
GRAPHICS CARDS IN RADIO RECONNAISSANCE: THE GPGPU TECHNOLOGY
IV. Évfolyam 4. szám - 2009. december Fürjes János furjes.janos@chello.hu GRAPHICS CARDS IN RADIO RECONNAISSANCE: THE GPGPU TECHNOLOGY Absztrakt/Abstract Jelen írás egy modern technológiát elemez, amely
A Survey of General-Purpose Computation on Graphics Hardware
EUROGRAPHICS 2005 STAR State of The Art Report A Survey of General-Purpose Computation on Graphics Hardware Lefohn, and Timothy J. Purcell Abstract The rapid increase in the performance of graphics hardware,
QuickSpecs. NVIDIA Quadro M6000 12GB Graphics INTRODUCTION. NVIDIA Quadro M6000 12GB Graphics. Overview
Overview L2K02AA INTRODUCTION Push the frontier of graphics processing with the new NVIDIA Quadro M6000 12GB graphics card. The Quadro M6000 features the top of the line member of the latest NVIDIA Maxwell-based
MIDeA: A Multi-Parallel Intrusion Detection Architecture
MIDeA: A Multi-Parallel Intrusion Detection Architecture Giorgos Vasiliadis, FORTH-ICS, Greece Michalis Polychronakis, Columbia U., USA Sotiris Ioannidis, FORTH-ICS, Greece CCS 2011, 19 October 2011 Network
How To Test Your Code On A Cuda Gdb (Gdb) On A Linux Computer With A Gbd (Gbd) And Gbbd Gbdu (Gdb) (Gdu) (Cuda
Mitglied der Helmholtz-Gemeinschaft Hands On CUDA Tools and Performance-Optimization JSC GPU Programming Course 26. März 2011 Dominic Eschweiler Outline of This Talk Introduction Setup CUDA-GDB Profiling
GPU Shading and Rendering: Introduction & Graphics Hardware
GPU Shading and Rendering: Introduction & Graphics Hardware Marc Olano Computer Science and Electrical Engineering University of Maryland, Baltimore County SIGGRAPH 2005 Schedule Shading Technolgy 8:30
Clustering Billions of Data Points Using GPUs
Clustering Billions of Data Points Using GPUs Ren Wu ren.wu@hp.com Bin Zhang bin.zhang2@hp.com Meichun Hsu meichun.hsu@hp.com ABSTRACT In this paper, we report our research on using GPUs to accelerate
PassMark - G3D Mark High End Videocards - Updated 12th of November 2015
1 z 8 2015-11-13 00:32 Shopping cart Home Software Hardware Benchmarks Services Store Support Forums Abou Home» Video Card Benchmarks» High End Video Cards ----Select A Page ---- High End Video Card Chart
String Matching on a multicore GPU using CUDA
String Matching on a multicore GPU using CUDA Charalampos S. Kouzinopoulos and Konstantinos G. Margaritis Parallel and Distributed Processing Laboratory Department of Applied Informatics, University of
CUDA Basics. Murphy Stein New York University
CUDA Basics Murphy Stein New York University Overview Device Architecture CUDA Programming Model Matrix Transpose in CUDA Further Reading What is CUDA? CUDA stands for: Compute Unified Device Architecture
IP Video Rendering Basics
CohuHD offers a broad line of High Definition network based cameras, positioning systems and VMS solutions designed for the performance requirements associated with critical infrastructure applications.
Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries
Performance Evaluations of Graph Database using CUDA and OpenMP Compatible Libraries Shin Morishima 1 and Hiroki Matsutani 1,2,3 1Keio University, 3 14 1 Hiyoshi, Kohoku ku, Yokohama, Japan 2National Institute