Cuda-Gdb Cuda Debugger

Total Page:16

File Type:pdf, Size:1020Kb

Cuda-Gdb Cuda Debugger CUDA-GDB CUDA DEBUGGER DU-05227-042 _v5.0 | October 2012 User Manual TABLE OF CONTENTS Chapter 1. Introduction.........................................................................................1 1.1 What is CUDA-GDB?...................................................................................... 1 1.2 Supported Features...................................................................................... 1 1.3 About This Document.................................................................................... 2 Chapter 2. Release Notes...................................................................................... 3 Chapter 3. Getting Started.....................................................................................6 3.1 Installation Instructions..................................................................................6 3.2 Setting Up the Debugger Environment................................................................6 3.2.1 Linux...................................................................................................6 3.2.2 Mac OS X............................................................................................. 6 3.2.3 Temporary Directory................................................................................ 7 3.3 Compiling the Application...............................................................................8 3.3.1 Debug Compilation.................................................................................. 8 3.3.2 Compiling for Fermi GPUs......................................................................... 8 3.3.3 Compiling for Fermi and Tesla GPUs.............................................................8 3.4 Using the Debugger...................................................................................... 8 3.4.1 Single GPU Debugging.............................................................................. 9 3.4.2 Multi-GPU Debugging............................................................................... 9 3.4.3 Multi-GPU Debugging in Console Mode.......................................................... 9 3.4.4 Multi-GPU Debugging with the Desktop Manager Running.................................... 9 3.4.5 Remote Debugging................................................................................. 11 3.4.6 Multiple Debuggers................................................................................ 11 3.4.7 Attaching/Detaching...............................................................................12 3.4.8 CUDA/OpenGL Interop Applications on Linux................................................. 12 Chapter 4. CUDA-GDB Extensions........................................................................... 13 4.1 Command Naming Convention........................................................................ 13 4.2 Getting Help............................................................................................. 13 4.3 Initialization File........................................................................................ 13 4.4 GUI Integration.......................................................................................... 14 Chapter 5. Kernel Focus...................................................................................... 15 5.1 Software Coordinates vs. Hardware Coordinates.................................................. 15 5.2 Current Focus............................................................................................ 15 5.3 Switching Focus..........................................................................................16 Chapter 6. Program Execution...............................................................................17 6.1 Interrupting the Application...........................................................................17 6.2 Single Stepping.......................................................................................... 17 Chapter 7. Breakpoints........................................................................................ 19 7.1 Symbolic Breakpoints................................................................................... 19 7.2 Line Breakpoints.........................................................................................20 7.3 Address Breakpoints.................................................................................... 20 www.nvidia.com CUDA Debugger DU-05227-042 _v5.0 | ii 7.4 Kernel Entry Breakpoints.............................................................................. 20 7.5 Conditional Breakpoints................................................................................ 20 Chapter 8. Inspecting Program State....................................................................... 22 8.1 Memory and Variables.................................................................................. 22 8.2 Variable Storage and Accessibility....................................................................22 8.3 Inspecting Textures..................................................................................... 23 8.4 Info CUDA Commands.................................................................................. 23 8.4.1 info cuda devices.................................................................................. 24 8.4.2 info cuda sms.......................................................................................24 8.4.3 info cuda warps.................................................................................... 25 8.4.4 info cuda lanes.....................................................................................25 8.4.5 info cuda kernels.................................................................................. 26 8.4.6 info cuda blocks................................................................................... 26 8.4.7 info cuda threads.................................................................................. 27 Chapter 9. Context and Kernel Events.................................................................... 29 9.1 Display CUDA context events......................................................................... 29 9.2 Display CUDA kernel events........................................................................... 29 9.3 Examples of displayed events.........................................................................29 Chapter 10. Checking Memory Errors...................................................................... 31 10.1 Increasing the Precision of Memory Errors With Autostep.......................................31 10.1.1 Usage............................................................................................... 32 10.1.2 Related Commands............................................................................... 33 10.1.2.1 info autosteps............................................................................... 33 10.1.2.2 disable autosteps n......................................................................... 33 10.1.2.3 delete autosteps n..........................................................................33 10.1.2.4 ignore n i.....................................................................................33 10.2 GPU Error Reporting...................................................................................33 Chapter 11. Walk-Through Examples.......................................................................36 11.1 Example 1: bitreverse................................................................................ 36 11.1.1 Walking through the Code...................................................................... 37 11.2 Example 2: autostep.................................................................................. 40 11.2.1 Debugging with Autosteps.......................................................................40 11.3 Example 3: Debugging an MPI CUDA Application................................................. 41 Appendix A. Supported Platforms...........................................................................44 A.1 Host Platform Requirements.......................................................................... 44 Appendix B. Known Issues.................................................................................... 46 www.nvidia.com CUDA Debugger DU-05227-042 _v5.0 | iii LIST OF FIGURES Figure 1 deviceQuery Output................................................................................. 11 www.nvidia.com CUDA Debugger DU-05227-042 _v5.0 | iv LIST OF TABLES Table 1 CUDA Exception Codes............................................................................... 34 www.nvidia.com CUDA Debugger DU-05227-042 _v5.0 | v www.nvidia.com CUDA Debugger DU-05227-042 _v5.0 | vi Chapter 1. INTRODUCTION This document introduces CUDA-GDB, the NVIDIA® CUDA™ debugger for Linux and Mac OS. 1.1 What is CUDA-GDB? CUDA-GDB is the NVIDIA tool for debugging CUDA applications running on Linux and Mac. CUDA-GDB is an extension to the x86-64 port of GDB, the GNU Project debugger. The tool provides developers with a mechanism for debugging CUDA applications running on actual hardware. This enables developers to debug applications without the potential variations introduced by simulation and emulation environments. CUDA-GDB runs on Linux and Mac OS X, 32-bit and 64-bit. CUDA-GDB is based on GDB 7.2 on both Linux and Mac OS X. 1.2 Supported Features CUDA-GDB is designed to present the user with a seamless debugging
Recommended publications
  • Energy-Efficient VLSI Architectures for Next
    UNIVERSITY OF CALIFORNIA Los Angeles Energy-Efficient VLSI Architectures for Next- Generation Software-Defined and Cognitive Radios A dissertation submitted in partial satisfaction of the requirements for the degree Doctor of Philosophy in Electrical Engineering by Fang-Li Yuan 2014 c Copyright by Fang-Li Yuan 2014 ABSTRACT OF THE DISSERTATION Energy-Efficient VLSI Architectures for Next- Generation Software-Defined and Cognitive Radios by Fang-Li Yuan Doctor of Philosophy in Electrical Engineering University of California, Los Angeles, 2014 Professor Dejan Markovic,´ Chair Dedicated radio hardware is no longer promising as it was in the past. Today, the support of diverse standards dictates more flexible solutions. Software-defined radio (SDR) provides the flexibility by replacing dedicated blocks (i.e. ASICs) with more general processors to adapt to various functions, standards and even allow mutable de- sign changes. However, such replacement generally incurs significant efficiency loss in circuits, hindering its feasibility for energy-constrained devices. The capability of dy- namic and blind spectrum analysis, as featured in the cognitive radio (CR) technology, makes chip implementation even more challenging. This work discusses several design techniques to achieve near-ASIC energy effi- ciency while providing the flexibility required by software-defined and cognitive radios. The algorithm-architecture co-design is used to determine domain-specific dataflow ii structures to achieve the right balance between energy efficiency and flexibility. The flexible instruction-set-architecture (ISA), the multi-scale interconnects, and the multi- core dynamic scheduling are also proposed to reduce the energy overhead. We demon- strate these concepts on two real-time blind classification chips for CR spectrum anal- ysis, as well as a 16-core processor for baseband SDR signal processing.
    [Show full text]
  • Shippensburg University Investment Management Program
    Shippensburg University Investment Management Program Hold NVIDIA Corp. (NASDAQ: NVDA) 11.03.2020 Current Price Fair Value 52 Week Range $501.36 $300 $180.68 - 589.07 Analyst: Valentina Alonso Key Stock Statistics Email: [email protected] Sector: Information Technology Revenue (TTM) $13.06B Stock Type: Large Growth Operating Margin (TTM) 28.56% Industry: Semiconductors and Semiconductors Equipment Market Cap: $309.697B Net Income (TTM) $3.39B EPS (TTM) $5.44 Operating Cash Flow (TTM) $5.58B Free Cash Flow (TTM) $3.67B Return on Assets (TTM) 11.67% Return on Equity (TTM) 27.94% P/E $92.59 Company overview P/B $22.32 Nvidia is the leading designer of graphics processing units that P/S $23.29 enhance the experience on computing platforms. The firm's chips are used in a variety of end markets, including high-end PCs for gaming, P/FCF 44.22 data centers, and automotive infotainment systems. In recent years, the firm has broadened its focus from traditional PC graphics Beta (5-Year) 1.54 applications such as gaming to more complex and favorable Dividend Yield 0.13% opportunities, including artificial intelligence and autonomous driving, which leverage the high-performance capabilities of the Projected 5 Year Growth 17.44% firm's graphics processing units. (per annum) Contents Executive Summary ....................................................................................................................................................3 Company Overview ....................................................................................................................................................4
    [Show full text]
  • 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
    [Show full text]
  • 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.
    [Show full text]
  • GPU Architecture:Architecture: Implicationsimplications && Trendstrends
    GPUGPU Architecture:Architecture: ImplicationsImplications && TrendsTrends DavidDavid Luebke,Luebke, NVIDIANVIDIA ResearchResearch Beyond Programmable Shading: In Action GraphicsGraphics inin aa NutshellNutshell • Make great images – intricate shapes – complex optical effects – seamless motion • Make them fast – invent clever techniques – use every trick imaginable – build monster hardware Eugene d’Eon, David Luebke, Eric Enderton In Proc. EGSR 2007 and GPU Gems 3 …… oror wewe couldcould justjust dodo itit byby handhand Perspective study of a chalice Paolo Uccello, circa 1450 Beyond Programmable Shading: In Action GPU Evolution - Hardware 1995 1999 2002 2003 2004 2005 2006-2007 NV1 GeForce 256 GeForce4 GeForce FX GeForce 6 GeForce 7 GeForce 8 1 Million 22 Million 63 Million 130 Million 222 Million 302 Million 754 Million Transistors Transistors Transistors Transistors Transistors Transistors Transistors 20082008 GeForceGeForce GTX GTX 200200 1.41.4 BillionBillion TransistorsTransistors Beyond Programmable Shading: In Action GPUGPU EvolutionEvolution -- ProgrammabilityProgrammability ? Future: CUDA, DX11 Compute, OpenCL CUDA (PhysX, RT, AFSM...) 2008 - Backbreaker DX10 Geo Shaders 2007 - Crysis DX9 Prog Shaders 2004 – Far Cry DX7 HW T&L DX8 Pixel Shaders 1999 – Test Drive 6 2001 – Ballistics TheThe GraphicsGraphics PipelinePipeline Vertex Transform & Lighting Triangle Setup & Rasterization Texturing & Pixel Shading Depth Test & Blending Framebuffer Beyond Programmable Shading: In Action TheThe GraphicsGraphics PipelinePipeline Vertex Transform
    [Show full text]
  • Cinefx Architecture Siggraph 2002 NV1 SEGA Virtua Fighter 50K Polygons/Sec 1M Pixel Ops/Sec Circa 1995
    CineFX Architecture Siggraph 2002 NV1 SEGA Virtua Fighter 50K polygons/sec 1M pixel ops/sec Circa 1995 NVIDIA CONFIDENTIAL XBOX 100MPolys/sec 1G pixel ops/sec Circa 2001 NVIDIA CONFIDENTIAL Convergence of Film and Real-time Rendering NVIDIA CONFIDENTIAL CinematicCinematic ShadingShading Final Fantasy NVIDIA CONFIDENTIAL The Spirits Within Square Real-time Cinematic Shading requires new levels of features and performance • Advanced Programmability • High-precision color • High-level Shading Language • Highly efficient architecture • High bandwidth to system memory and CPU NVIDIA CONFIDENTIAL Artist: Count Love Introducing the CineFX Architecture Generalized Vertex Processing Generalized Pixel Processing 128-bit Floating Point Precision Highly advanced rendering architecture Dramatically improved performance NVIDIA CONFIDENTIAL CineFX Generalized Vertex Processing DX8.0 R300 CineFX Up to 65536 vertex Vertex Shaders 1.1 2.0 2.0+ Max Instructions 128 1024 65536 instructions Max Static Instructions 128 256 256 Max. Constants 96 256 256 256 constants Temporary Registers 12 12 16 Loops & Branching Max Loops 0 4 256 Conditional Write Masks - - ü Forward & backwards Call & Return - - ü Static Flow Control - ü ü Data Dependent Dynamic Flow Control - - ü Call & Return - Subroutines Per component condition codes & write masks Faster than branching for short basic blocks NVIDIA CONFIDENTIAL CineFX Vertex Processing Instruction Set Add & multiply instructions ADD, DP3, DP4, DPH, MAD, MOV, SUB Math functions ABS, COS, EX2, EXP, FLR, FRC, LG2, LOG, RCP,
    [Show full text]
  • Your Graphic Designer
    Monday-Friday 9am-6pm Holiday/Weekend Closed (619) 425-5592 We Custom Built Computers PC . Upgrade . Fix Probems. Remove Virus Free No Obligation Computer Diagnostic | Free Upgrade Consultation ON-SALE : $995.00 Asus ExpertBook P2451FA-XS74 (14 inch) Intel Core i7-10510U 1.8GHz up to 4.9 GHz Memory 16GB DDR4 (On-Board) SSD 512GB PCIE SSD + TPM Operating System Windows 10 Professional Webcam HD IR Camera Illuminated Chiclet Keyboard with Track Point Add $149 : Microso� Office Home/Student Classic 2019 versions of Word, Excel and PowerPoint. Plus OneNote Add $239 : Microso� Office Home/Business Classic 2019 versions of Word, Excel, PowerPoint, and Outlook Add $115 : Microso� Windows 10 Home Add $139 : Microso� Windows 10 Pro In-Win Case Add $26.00 Custom Ul�mate Performance PC Custom AMD The Elite Gaming PC SSD 1TB NVMe M.2 Solid State Price $1159.00: ON SALE! Custom Pro PC : Home/Business Memory Kingston 16GB DDR4-3600 DEEPCOOL Mid Tower 3-Fans RGB $1059.00 SSD 500GB NVMe M.2 Solid State EVGA Super NOVA GOLD 650W SPU Processor Ryzen 5 3600 6-Core 3.6GHz SSD 500GB NVMe M.2 Solid State Memory Kingston 16GB DDR4-2666 Gigabyte Intel Z590 Motherboard SuperCase w/ 300W SPU Integrated Graphics, Ethernet, audio Memory Kingston 16GB DDR4-3200 Antec NX400 NX Gaming Case Asus Intel B460M Motherboard Intel Core i5-11600K 3.9GHz : $1090.00 EVGA Super NOVA GOLD 650W SPU Integrated Graphics, Ethernet, audio Asus B550M-A WiFi Motherboard Intel i5 2.9GHz Six-Core : $645.00 Intel Core i7-11700K 3.6GHz : $1223.00 Graphic nVidia GTX 1050 Ti OC 4GB Intel Core i9-11900K 3.5GHz : $1440.00 OS: Windows 10 Home 64bit Intel i7 2.5GHz Eight-Core : $845.00 Best Deal Computer Services, Inc.
    [Show full text]
  • Linux Hardware Compatibility HOWTO
    Linux Hardware Compatibility HOWTO Steven Pritchard Southern Illinois Linux Users Group / K&S Pritchard Enterprises, Inc. <[email protected]> 3.2.4 Copyright © 2001−2007 Steven Pritchard Copyright © 1997−1999 Patrick Reijnen 2007−05−22 This document attempts to list most of the hardware known to be either supported or unsupported under Linux. Copyright This HOWTO is free documentation; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free software Foundation; either version 2 of the license, or (at your option) any later version. Linux Hardware Compatibility HOWTO Table of Contents 1. Introduction.....................................................................................................................................................1 1.1. Notes on binary−only drivers...........................................................................................................1 1.2. Notes on proprietary drivers.............................................................................................................1 1.3. System architectures.........................................................................................................................1 1.4. Related sources of information.........................................................................................................2 1.5. Known problems with this document...............................................................................................2 1.6. New versions of this document.........................................................................................................2
    [Show full text]
  • NVIDIA Opengl Extension Specifications
    NVIDIA OpenGL Extension Specifications NVIDIA OpenGL Extension Specifications NVIDIA Corporation Mark J. Kilgard, editor [email protected] May 21, 2001 1 NVIDIA OpenGL Extension Specifications Copyright NVIDIA Corporation, 1999, 2000, 2001. This document is protected by copyright and contains information proprietary to NVIDIA Corporation as designated in the document. Other OpenGL extension specifications can be found at: http://oss.sgi.com/projects/ogl-sample/registry/ 2 NVIDIA OpenGL Extension Specifications Table of Contents Table of NVIDIA OpenGL Extension Support.............................. 4 ARB_imaging........................................................... 6 ARB_multisample....................................................... 7 ARB_multitexture..................................................... 18 ARB_texture_border_clamp............................................. 19 ARB_texture_compression.............................................. 25 ARB_texture_cube_map................................................. 48 ARB_texture_env_add.................................................. 62 ARB_texture_env_combine.............................................. 65 ARB_texture_env_dot3................................................. 73 ARB_transpose_matrix................................................. 76 EXT_abgr............................................................. 81 EXT_bgra............................................................. 84 EXT_blend_color...................................................... 86 EXT_blend_minmax....................................................
    [Show full text]
  • NVIDIA Topology-Aware GPU Selection 0.1.0 (Early Access)
    NVIDIA Topology-Aware GPU Selection 0.1.0 (Early Access) User Guide DU-09998-001_v0.1.0 (Early Access) | July 2020 Table of Contents Chapter 1. Introduction........................................................................................................ 1 Chapter 2. Getting Started................................................................................................... 4 2.1. Prerequisites............................................................................................................................. 4 2.2. Installing NVTAGS..................................................................................................................... 4 Chapter 3. Using NVTAGS.................................................................................................... 6 3.1. NVTAGS Tune Mode..................................................................................................................6 3.1.1. Tune with profiling............................................................................................................. 6 3.1.2. Run NVTAGS in Tune with Profiling Mode........................................................................7 3.2. Tune NVTAGS without Profiling Mode..................................................................................... 7 3.2.1. Run NVTAGS in Tune without Profiling Mode...................................................................7 3.3. NVTAGS Run Mode..................................................................................................................
    [Show full text]
  • John Carmack Archive - Interviews
    John Carmack Archive - Interviews http://www.team5150.com/~andrew/carmack August 2, 2008 Contents 1 John Carmack Interview5 2 John Carmack - The Boot Interview 12 2.1 Page 1............................... 13 2.2 Page 2............................... 14 2.3 Page 3............................... 16 2.4 Page 4............................... 18 2.5 Page 5............................... 21 2.6 Page 6............................... 22 2.7 Page 7............................... 24 2.8 Page 8............................... 25 3 John Carmack - The Boot Interview (Outtakes) 28 4 John Carmack (of id Software) interview 48 5 Interview with John Carmack 59 6 Carmack Q&A on Q3A changes 67 1 John Carmack Archive 2 Interviews 7 Carmack responds to FS Suggestions 70 8 Slashdot asks, John Carmack Answers 74 9 John Carmack Interview 86 9.1 The Man Behind the Phenomenon.............. 87 9.2 Carmack on Money....................... 89 9.3 Focus and Inspiration...................... 90 9.4 Epiphanies............................ 92 9.5 On Open Source......................... 94 9.6 More on Linux.......................... 95 9.7 Carmack the Student...................... 97 9.8 Quake and Simplicity...................... 98 9.9 The Next id Game........................ 100 9.10 On the Gaming Industry.................... 101 9.11 id is not a publisher....................... 103 9.12 The Trinity Thing........................ 105 9.13 Voxels and Curves........................ 106 9.14 Looking at the Competition.................. 108 9.15 Carmack’s Research......................
    [Show full text]
  • Evolution of Gpus.Pdf
    Evolution of GPUs Chris Seitz PDF created with pdfFactory Pro trial version www.pdffactory.com Overview Concepts: Real-time rendering Hardware graphics pipeline Evolution of the PC hardware graphics pipeline: 1995-1998: Texture mapping and z-buffer 1998: Multitexturing 1999-2000: Transform and lighting 2001: Programmable vertex shader 2002-2003: Programmable pixel shader 2004: Shader model 3.0 and 64-bit color support ©2004 NVIDIA Corporation. All rights reserved. PDF created with pdfFactory Pro trial version www.pdffactory.com Real-Time Rendering Graphics hardware enables real-time rendering Real-time means display rate at more than 10 images per second 3D Scene = Image = Collection of Array of pixels 3D primitives (triangles, lines, points) ©2004 NVIDIA Corporation. All rights reserved. PDF created with pdfFactory Pro trial version www.pdffactory.com PC Architecture Motherboard CPU System Memory Bus Port (PCI, AGP, PCIe) Graphics Board Video Memory GPU ©2004 NVIDIA Corporation. All rights reserved. PDF created with pdfFactory Pro trial version www.pdffactory.com Hardware Graphics Pipeline Application 3D Geometry 2D Rasterization Pixels Stage Triangles Stage Triangles Stage For each For each triangle vertex: triangle: Transform Rasterize position: triangle 3D -> screen Interpolate Compute vertex attributes attributes Shade pixels Resolve visibility ©2004 NVIDIA Corporation. All rights reserved. PDF created with pdfFactory Pro trial version www.pdffactory.com Real-time Graphics 1997: RIVA 128 –3M Transistors ©2004 NVIDIA Corporation. All rights reserved. PDF created with pdfFactory Pro trial version www.pdffactory.com Real-time Graphics 2004: ©2004 NVIDIA Corporation. All rights reserved. UnRealEngine 3.0 Images Courtesy of Epic Games PDF created with pdfFactory Pro trial version www.pdffactory.com ‘95-‘98: Texture Mapping & Z-Buffer CPU GPU Application / Rasterization Stage Geometry Stage Raster Texture Rasterizer Operations Unit Unit 2D Triangles Bus (PCI) Frame 2D Triangles Textures Buffer Textures System Memory Video Memory ©2004 NVIDIA Corporation.
    [Show full text]