Fermi GF100 Graphics Processing Unit (GPU)

Total Page:16

File Type:pdf, Size:1020Kb

Fermi GF100 Graphics Processing Unit (GPU) Fermi GF100 Graphics Processing Unit (GPU) Craig M. Wittenbrink, Emmett Kilgariff, Arjun Prabhu Acknowledgements We would like to thank Jonah Alben, John Robinson, Mark Daly, Henry Moreton, and the entire Fermi GF100 Development team for success of this project Outline GF100 Specifications GF100 Architecture Tessellation Physics Computational Graphics Demos GF100 Specifications 3 Billion Transistors in 40 nm process (TSMC) Up to 512 CUDA / unified shader cores 384-bit GDDR5 memory interface, 6GB capacity GeForce GTX480: Graphics Enthusiast Full Microsoft DirectX 11 Shader Model 5.0 32x coverage sample antialiasing (AA) 128-bit floating point high dynamic-range lighting with AA Tesla C2070: Compute HPC CUDA programming: C, C++, OpenCL, DirectCompute, or Fortran 515 GigaFLOPS double-precision peak floating point ECC Register Files, L1/L2 caches, shared memory and DRAM GF100 Host Interface GigaThread Engine GPC GPC Raster Engine Raster Engine Architecture Memory Controller SM SM SM SM SM SM SM SM 512 CUDA cores Memory Controller 16 PolyMorph Engines Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine 4 raster units Memory Controller 64 texture units L2 Cache Memory Controller 48 ROP units Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine Polymorph Engine 384-bit GDDR5 Memory Controller SM SM SM SM SM SM SM SM Memory Controller Raster Engine Raster Engine GPC GPC GF100 SM Architecture SM – Streaming Multiprocessor 32 CUDA cores: 4x GT200 GT200 – previous high-end GPU by NVIDIA 48 or 16KB of shared memory: 3x GT200 16 or 48KB of L1 cache: No L1 on GT200 ISA improvements 32-bit integer operations FMA (fused multiply add) IEEE-754 2008 4 Texture units 1 PolyMorph Engine Cache Architecture for Graphics Chip Hull, Domain & Vertex Fetch Vertex Shader Rasterizer Pixel Shader ROP Data stays on die Geomtry Shaders L1 & L2 Caches L1 cache Register spilling Stack ops DRAM Global LD/ST L2 Cache Vertex, SM, Texture and ROP Data Cache Architecture Comparison GT200 GF100 Benefit L1 Texture Cache 12 KB 12 KB Fast texture filtering (per SM) Dedicated L1 ✘ 16 or 48 KB Efficient physics and LD/ ST Cache ray tracing Total Shared 16KB 16 or 48 KB More data reuse Memory among threads L2 Cache 256KB 768 KB Greater texture (TEX read only) (all clients coverage, robust read/ write) compute performance DX10 Game Problems, too little detail Geometry throughput is challenge Flat head Silhouette results from coarse triangulation There are other artifacts Image from Far Cry® 2, courtesy of Ubisoft Offline Film Rendering uses Tessellation Tessellation + displacement mapping modelling standard Rich geometric detail More Geometry, Takes More time © Disney Enterprises, Inc. and Jerry Bruckheimer, Inc. All rights reserved. Image courtesy Industrial Light & Magic. Tessellation & Displacement Maps Add Geometric Detail “z” Original Displaced vertices “y” “x” Geometry “v” “u” Tessellated Geometry Final Geometry Displacement Map http://en.wikipedia.org/wiki/File:Displacement.jpg Tessellation in DirectX 11 From input assembly Vertex Hull shader input new patch primitive Patch New pipeline Assembly Outputs tessFactors, modes stages Runs pre-expansion (1 arrow) Hull Control Tessellator Fixed function tessellation stage points input TessFactors and modes Domain Outputs triangles and lines On chip data expansion (3 arrows) Primitive Assembly Geometry Tessellation in DirectX 11, cont From input assembly Vertex Domain shader Input TessFactors, (u,v) domain points Patch Assembly Outputs vertices in (x,y,z,w) Hull “v” “u” Control Tessellator points “z” Domain “z” “x” Primitive Assembly “y” Geometry GF100 Scalable Parallel Implementation Raster Engine HostHost InterfaceInterface GigaThread EngineEngine Edge Setup Rasterizer Z-Cull GPC GPC Raster Engine RasterRaster Engine Engine Memory Controller Memory Controller SM SM SM SM SM SM SM SM PolyMorph Engine Vertex Fetch Tessellator Viewport Transform GPC GPC SM SM SM SM SM SM SM SM Memory Controller Memory Controller Attribute Setup Stream Output Polymorph Engine Polymorph EngineEngine PolymorphPolymorph EngineEngine PolymorphPolymorph EngineEngine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine Memory Controller Memory Controller Distributed, parallel geometry 4 Raster Engines L2L2 CacheCache Memory Controller 16 PolyMorph Engines Memory Controller Polymorph Engine Polymorph EngineEngine PolymorphPolymorph EngineEngine PolymorphPolymorph EngineEngine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine PolymorphPolymorph Engine Engine 8x geometry performance Memory Controller Memory Controller SM SM SM SM SM SM SM SM vs. GT200 GPC GPC SM SM SM SM SM SM SM SM Memory Controller Memory Controller Enables > 2.5 triangles/clock Raster Engine RasterRaster Engine Engine GPC GPC GF100 Enables Geometric Realism Tessellation provides interactive smooth silhouette Film like capabilities in games More geometry detail—3D Stereo viewing benefits Dynamic shadows 3D Vision™ Memory footprint & BW savings Store coarse geometry, expand on-demand Enables more complex animations Scalability Dynamic LOD allows for performance/quality tradeoffs Scale into the future – resolution, compute power © Kenneth Scott, id Software 2008 Dramatic Increase in Geometry Capability 2 Shader TeraFLOP/s triangle/s GeForce GTX 480 1.8 1.6 Geometry 1.4 1.2 1 0.8 0.6 Shader 0.4 0.2 0 Ti4600 6800 Ultra 8800GTX GTX285 GTX480 20022004 2006 2008 2010 Advance in Geometric Complexity 20 18 16 14 12 10 8 6 4 2 Millions of polygons per frame per polygons of Millions - 2003 2010 Physics: PhysX® Example: Fluid Simulation PhysX® is NVIDIA physics engine Uses CUDA SW stack Used in over 150 computer games Particle-based fluid simulation (SPH) 128,000 particles Includes surface tension 3x speed up going from GT200 to GF100 67 to 200 fps Games fluid for water splashes, mud, blood… Computational Graphics: Image Processing – Motion Blur Object Color Buffer Previous Shading Model Position Back Buffer Vertex Unblurred Shader Image Current Motion Blur Model Shader Final Blurred Position Image Pixel Velocity Buffer Previous Shader Screen Space Camera Multiple Position velocity of render every pixel target Current (MRT) Camera Position Detected Zones Computational Graphics: Ray Tracing Combine Rasterization with Raytracing Rasterize primary rays Raytrace shadows and reflections 4x faster than GT200 Efficient use of cache architecture GF100 GPU Compute Performance 4.0 3.5 3.0 2.5 2.0 GF100 GT200GTX285 1.5 1.0 0.5 0.0 © Capcom, Inc. PhysX Fluid Dark Void Ray Tracing AI Demo: SuperSonic Sled--tessellation, physics, and computational graphics SuperSonic Sled: Tessellation - Terrain Terrain Displacement Map SuperSonic Sled: Physics – Smoke, Bridge, Fireballs f (p,v Large ,m) Pieces 128 CPU Objects 128 GPU Dust Small Chunks (1500 Particles Objects) GPU Debris SuperSonic Sled: Computational Graphics- Motion Blur Demo Conclusions GF100 Improves Geometry processing up to 8X over GT200 New Architecture and New family of chips GF100 Architecture breaks 1 triangle per clock barrier More than 2.5 Triangles/clock High throughput tessellation has benefits for interactive gaming Demonstration of Tessellation Physics Computational Graphics.
Recommended publications
  • An Algorithm for the Construction of Intrinsic Delaunay Triangulations with Applications to Digital Geometry Processing
    An Algorithm for the Construction of Intrinsic Delaunay Triangulations with Applications to Digital Geometry Processing Matthew Fisher Boris Springborn Peter Schroder¨ Alexander I. Bobenko Caltech TU Berlin Caltech TU Berlin Abstract The discrete Laplace-Beltrami operator plays a prominent role in many Digital Geometry Processing applications ranging from de- noising to parameterization, editing, and physical simulation. The standard discretization uses the cotangents of the angles in the im- mersed mesh which leads to a variety of numerical problems. We advocate the use of the intrinsic Laplace-Beltrami operator. It sat- isfies a local maximum principle, guaranteeing, e.g., that no flipped triangles can occur in parameterizations. It also leads to better con- ditioned linear systems. The intrinsic Laplace-Beltrami operator is based on an intrinsic Delaunay triangulation of the surface. We detail an incremental algorithm to construct such triangulations to- gether with an overlay structure which captures the relationship be- tween the extrinsic and intrinsic triangulations. Using a variety of example meshes we demonstrate the numerical benefits of the in- trinsic Laplace-Beltrami operator. Figure 1: Left: carrier of the (cat head) surface as defined by the original embedded mesh. Right: the intrinsic Delaunay triangu- 1 Introduction lation of the same carrier. Delaunay edges which appear in the original triangulation are shown in white. Some of the original 2 Delaunay triangulations of domains in R play an important role edges are not present anymore (these are shown in black). In their in many numerical computing applications because of the quality stead we see red edges which appear as the result of intrinsic flip- guarantees they make, such as: among all triangulations of the con- ping.
    [Show full text]
  • Geometry-Aware Topological Decompositions of Meshes
    UC Irvine UC Irvine Electronic Theses and Dissertations Title Geometry-aware topological decompositions of meshes Permalink https://escholarship.org/uc/item/3vh0q7wn Author Chen, Jia Publication Date 2019 Peer reviewed|Thesis/dissertation eScholarship.org Powered by the California Digital Library University of California UNIVERSITY OF CALIFORNIA, IRVINE Geometry-aware topological decompositions of meshes DISSERTATION submitted in partial satisfaction of the requirements for the degree of DOCTOR OF PHILOSOPHY in Computer Science by Jia Chen Dissertation Committee: Professor Gopi Meenakshisundaram, Chair Professor Aditi Majumder Professor Shuang Zhao 2019 Chapter3 c 2016 ACM New York, NY, USA Chapter3 c 2018 ACM New York, NY, USA All other materials c 2019 Jia Chen DEDICATION To my family \. a man who keeps company with glaciers comes to feel tolerably insignificant by and by." { Mark Twain, A Tramp Abroad ii TABLE OF CONTENTS Page LIST OF FIGURESv LIST OF TABLESx LIST OF ALGORITHMS xi ACKNOWLEDGMENTS xii CURRICULUM VITAE xiii ABSTRACT OF THE DISSERTATION xiv 1 Introduction1 1.1 Cycles of topological properties.........................2 1.2 Topological decompositions of meshes......................4 2 Definitions and Background7 2.1 Surfaces and their topological classification...................7 2.2 Primal graph and dual graph embedded on a surface.............8 2.3 Paths and cycles.................................9 2.4 Tunnel and handle cycles............................. 11 3 Iterative localization of handle and tunnel cycles 13 3.1 Related work................................... 14 3.2 Problem formulation............................... 15 3.3 Localizing handle and tunnel cycles....................... 16 3.3.1 Iterative tree-cotree algorithm...................... 16 3.3.2 Cycle tightening.............................. 20 3.3.3 Decoupling composite fundamental cycles...............
    [Show full text]
  • Digital Geometry Processing Mesh Basics
    Digital Geometry Processing Basics Mesh Basics: Definitions, Topology & Data Structures 1 © Alla Sheffer Standard Graph Definitions G = <V,E> V = vertices = {A,B,C,D,E,F,G,H,I,J,K,L} E = edges = {(A,B),(B,C),(C,D),(D,E),(E,F),(F,G), (G,H),(H,A),(A,J),(A,G),(B,J),(K,F), (C,L),(C,I),(D,I),(D,F),(F,I),(G,K), (J,L),(J,K),(K,L),(L,I)} Vertex degree (valence) = number of edges incident on vertex deg(J) = 4, deg(H) = 2 k-regular graph = graph whose vertices all have degree k Face: cycle of vertices/edges which cannot be shortened F = faces = {(A,H,G),(A,J,K,G),(B,A,J),(B,C,L,J),(C,I,L),(C,D,I), (D,E,F),(D,I,F),(L,I,F,K),(L,J,K),(K,F,G)} © Alla Sheffer Page 1 Digital Geometry Processing Basics Connectivity Graph is connected if there is a path of edges connecting every two vertices Graph is k-connected if between every two vertices there are k edge-disjoint paths Graph G’=<V’,E’> is a subgraph of graph G=<V,E> if V’ is a subset of V and E’ is the subset of E incident on V’ Connected component of a graph: maximal connected subgraph Subset V’ of V is an independent set in G if the subgraph it induces does not contain any edges of E © Alla Sheffer Graph Embedding Graph is embedded in Rd if each vertex is assigned a position in Rd Embedding in R2 Embedding in R3 © Alla Sheffer Page 2 Digital Geometry Processing Basics Planar Graphs Planar Graph Plane Graph Planar graph: graph whose vertices and edges can Straight Line Plane Graph be embedded in R2 such that its edges do not intersect Every planar graph can be drawn as a straight-line plane graph ©
    [Show full text]
  • Evolution of the Graphical Processing Unit
    University of Nevada Reno Evolution of the Graphical Processing Unit A professional paper submitted in partial fulfillment of the requirements for the degree of Master of Science with a major in Computer Science by Thomas Scott Crow Dr. Frederick C. Harris, Jr., Advisor December 2004 Dedication To my wife Windee, thank you for all of your patience, intelligence and love. i Acknowledgements I would like to thank my advisor Dr. Harris for his patience and the help he has provided me. The field of Computer Science needs more individuals like Dr. Harris. I would like to thank Dr. Mensing for unknowingly giving me an excellent model of what a Man can be and for his confidence in my work. I am very grateful to Dr. Egbert and Dr. Mensing for agreeing to be committee members and for their valuable time. Thank you jeffs. ii Abstract In this paper we discuss some major contributions to the field of computer graphics that have led to the implementation of the modern graphical processing unit. We also compare the performance of matrix‐matrix multiplication on the GPU to the same computation on the CPU. Although the CPU performs better in this comparison, modern GPUs have a lot of potential since their rate of growth far exceeds that of the CPU. The history of the rate of growth of the GPU shows that the transistor count doubles every 6 months where that of the CPU is only every 18 months. There is currently much research going on regarding general purpose computing on GPUs and although there has been moderate success, there are several issues that keep the commodity GPU from expanding out from pure graphics computing with limited cache bandwidth being one.
    [Show full text]
  • Computer Graphics CMU 15-462/15-662 Scotty 3D Setup Recitation Today! Hunt Library Computer Lab 3:30-5Pm
    Digital Geometry Processing Computer Graphics CMU 15-462/15-662 Scotty 3D setup recitation Today! Hunt Library Computer Lab 3:30-5pm CMU 15-462/662 Last time part 1: overview of geometry Many types of geometry in nature Geometry Demand sophisticated representations Two major categories: - IMPLICIT - “tests” if a point is in shape - EXPLICIT - directly “lists” points Lots of representations for both CMU 15-462/662 Last time part 2: Meshes & Manifolds Mathematical description of geometry - simplifying assumption: manifold - for polygon meshes: “fans, not fins” Data structures for surfaces - polygon soup - halfedge mesh - storage cost vs. access time, etc. Today: next how do we manipulate geometry? twin - face edge - geometry processing / resampling Halfedge vertex CMU 15-462/662 Today: Geometry Processing & Queries Extend traditional digital signal processing (audio, video, etc.) to deal with geometric signals: - upsampling / downsampling / resampling / filtering ... - aliasing (reconstructed surface gives “false impression”) Also ask some basic questions about geometry: - What’s the closest point? Do two triangles intersect? Etc. Beyond pure geometry, these are basic building blocks for many algorithms in graphics (rendering, animation...) CMU 15-462/662 Digital Geometry Processing: Motivation 3D Scanning 3D Printing CMU 15-462/662 Geometry Processing Pipeline scan print process CMU 15-462/662 Geometry Processing Tasks reconstruction filtering remeshing parameterization compression shape analysis CMU 15-462/662 Geometry Processing: Reconstruction
    [Show full text]
  • Creating and Processing 3D Geometry
    Creating and processing 3D geometry Marie-Paule Cani [email protected] Cédric Gérot [email protected] Franck Hétroy [email protected] http://evasion.imag.fr/Membres/Franck.Hetroy/Teaching/Geo3D/ Context: computer graphics ● We want to represent objects – Real objects – Virtual/created objects ● Several ways for virtual object creation – Interactive by graphists – Automatic from real data ● 3D scanner, medical angiography, ... – Procedural (on the fly) ● Complex scenes, terrain, ... ● Different uses – Display, animation, ph ysical simulation, ... Course overview 1.Objects representations – Volume/surface, implicit/explicit, ... Real-time triangulation of implicit surfaces Course overview 1.Objects representations – Volume/surface, implicit/explicit, ... 2.Geometry processing – Simplify, smooth, ... Interactive multiresolution surface exploration Course overview 1.Objects representations – Volume/surface, implicit/explicit, ... 2.Geometry processing – Simplify, smooth, ... 3.Virtual object creation – Surface reconstruction, interactive modeling Shape modeling by sketching Planning (provisional) Part I – Geometry representations ● Lecture 1 – Oct 9th – FH – Introduction to the lectures; point sets, meshes, discrete geometry. ● Lecture 2 – Oct 16th – MPC – Parametric curves and surfaces; subdivision surfaces. ● Lecture 3 – Oct 23rd - MPC – Implicit surfaces. Planning (provisional) Part II – Geometry processing ● Lecture 4 – Nov 6th – FH – Discrete differential geometry; mesh smoothing and simplification (paper presentations).
    [Show full text]
  • Research on Shape Mapping of 3D Mesh Models Based on Hidden
    Research on Shape Mapping of 3D Mesh Models based on Hidden Markov Random Field and EM Algorithm WANG Yong1 WU Huai-yu2 1 (University of Chinese Academy of Sciences, Beijing 100049, China) 2 (Institute of Automation, Chinese Academy of Sciences, Beijing, 100080, China) Abstract How to establish the matching (or corresponding) between two different 3D shapes is a classical problem. This paper focused on the research on shape mapping of 3D mesh models, and proposed a shape mapping algorithm based on Hidden Markov Random Field and EM algorithm, as introducing a hidden state random variable associated with the adjacent blocks of shape matching when establishing HMRF. This algorithm provides a new theory and method to ensure the consistency of the edge data of adjacent blocks, and the experimental results show that the algorithm in this paper has a great improvement on the shape mapping of 3D mesh models. Keywords Shape Mapping, 3D Mesh Model, Hidden Markov Random Field, EM Algorithm 1 Introduction bending deformation, different sampling rate, and Digital geometry processing of 3D mesh models different parameterization methods. (a1'/a2', b1'/b2') has broad application prospects in the fields of are the transformed models of (a1/a2, b1/b2) by the industrial design, virtual reality, game entertainment, geometry processing framework of global Internet, digital museum, urban planning and so on[1]. perspective. It can be seen that the transformed However the surface of 3D mesh models is usually models are much similar in their poses and shapes. bent arbitrarily, lack of continuous parameters, and So the difficulty of establishing the automatic has complex characterized details, as is quite different matching between two different 3D shapes has been from the regular 2D image data with the uniform greatly reduced.
    [Show full text]
  • Computer Graphics CMU 15-462/15-662
    Digital Geometry Processing Computer Graphics CMU 15-462/15-662 Last time: Meshes & Manifolds Mathematical description of geometry - simplifying assumption: manifold - for polygon meshes: “fans, not fns” Data structures for surfaces - polygon soup - halfedge mesh - storage cost vs. access time, etc. Today: next how do we manipulate geometry? twin - face edge - geometry processing / resampling Halfedge vertex CMU 15-462/662 Today: Geometry Processing Extend traditional digital signal processing (audio, video, etc.) to deal with geometric signals: - upsampling / downsampling / resampling / fltering ... - aliasing (reconstructed surface gives “false impression”) Beyond pure geometry, these are basic building blocks for many areas/algorithms in graphics (rendering, animation...) CMU 15-462/662 Digital Geometry Processing: Motivation 3D Scanning 3D Printing CMU 15-462/662 Geometry Processing Pipeline scan print process CMU 15-462/662 Geometry Processing Tasks reconstruction filtering remeshing parameterization compression shape analysis CMU 15-462/662 Geometry Processing: Reconstruction Given samples of geometry, reconstruct surface What are “samples”? Many possibilities: - points, points & normals, ... - image pairs / sets (multi-view stereo) - line density integrals (MRI/CT scans) How do you get a surface? Many techniques: - silhouette-based (visual hull) - Voronoi-based (e.g., power crust) - PDE-based (e.g., Poisson reconstruction) - Radon transform / isosurfacing (marching cubes) CMU 15-462/662 Geometry Processing: Upsampling Increase resolution
    [Show full text]
  • Topology Preserving Isosurface Extraction for Geometry Processing
    Topology Preserving Isosurface Extraction for Geometry Processing Gokul Varadhan1, Shankar Krishnan2, TVN Sriram1, and Dinesh Manocha1 1Department of Computer Science, University of North Carolina, Chapel Hill, U.S.A 2AT&T Labs - Research, Florham Park, New Jersey, U.S.A http://gamma.cs.unc.edu/recons ABSTRACT Marching Cubes (MC) [2] or any of its variants [3–5]. We We address the problem of computing a topology preserving refer to these isosurface extraction algorithms collectively as isosurface from a volumetric grid using Marching Cubes for MC-like algorithms. The output of an MC-like algorithm is geometry processing applications. We present a novel adap- an approximation – usually a polygonal approximation – of tive subdivision algorithm to generate a volumetric grid. the implicit surface. We refer to this step as reconstruction Our algorithm ensures that every grid cell satisfies certain of the implicit surface. sampling criteria. We show that these sampling criteria Compared to other surface representations (e.g. para- are sufficient to ensure that the isosurface extracted from metric surfaces), implicit surface representations are easy to the grid using Marching Cubes is topologically equivalent use to perform geometric operations like union, intersection, to the exact isosurface: both the exact isosurface and the difference, blending, and warping. Specifically, they map extracted isosurface have the same genus and connectivity. Boolean operations into simple minimum/maximum opera- We use our algorithm for accurate boundary evaluation of tions on the scalar fields of the primitives. Suppose we have Boolean combinations of polyhedra and low degree algebraic two primitives P1 and P2 with scalar fields f1 and f2.
    [Show full text]
  • Geometry Processing Algorithms
    Geometry Processing Algorithms CSCS468468 http: //cs468.stfdd/tanford.edu/ Mirela Ben Chen Objective • Theory and algorithms for efficient analysis and manipulation of complex 3D models • Hands-on experience 2 Requirements Prerequisites: - Experience with C++ programming - Background in geometry or computational geometry helpful, but not necessary. Grade (3 units): - Programming exercises - OpenMesh intro (10%) - Surface smoothing (20%) - Simplification (20%) - Parameterization (25%) - Remeshing (25%) Use OpenMesh API Sign up to Piazza! 3 References • Book “Polygon Mesh Processing” by Mario Botsch, Leif Kobbelt, Mark Pauly, Pierre Alliez, Bruno Levy • Eurographics 2008 course notes “Geometric Modeling Based on Polygonal Meshes” by Mario Botsch, Mark Pauly, Leif Kobbelt, Pierre Alliez, Bruno Levy, Stephan Bischoff, Christian Rössl • More links on web site 4 What is Geometry Processing About? • Acquiring • Analyzing • Manipulating 5 Applications Medical E-Commerce Simulation Engineering Culture Games & Movies Architecture Architecture Creating Reverse Engineering 6 A Geometry Processing Pipeline Low Level Algorithms 7 A Geometry Processing Pipeline 8 A Geometry Processing Pipeline High Level Algorithms Deformation and editing Extracting shape structure 9 Acquiring 33DD Geometry RSRange Scanners 10 Acquiring 33DD Geometry RSRange Scanners 11 Acquiring 33DD Geometry Tomography Simplification 20,000 8,000 2,000 Demo Applications Multi-resolution hierarchies for – efficient geometry processing – level-of-detail (()LOD) renderin g 14 Size-Quality
    [Show full text]
  • Challenges & Opportunities for 3D Graphics on the PC
    ChallengesChallenges && OpportunitiesOpportunities forfor 3D3D GraphicsGraphics onon thethe PCPC Neil Trevett, VP Marketing 3Dlabs President, Web3D Consortium www.3dlabs.com © Copyright 3Dlabs, 1999 - Page 1 - PROPRIETARY & CONFIDENTIAL Topics Graphics Challenges on the PC Platform • What is going to be the killer 3D application? - No-one cares about 3D other than workstations applications and gamers - What is going to change that - on the PC and on the Web? • Geometry processing performance - How to push to the next level of performance i.e. >40M polygons/sec - CPUs are not fast enough - we need geometry acceleration ... - … but high-end volumes are too small to warrant specialized chip development • PC system bandwidth - passing data to the graphics engine - Front side bus bandwidth is a fundamental barrier to polygon performance today - What are the possible hardware and software solutions? • Graphics memory architecture - On-board texture management is an unbounded problem and a difficult software problem - UMA memory is cheap - but lacks high performance - Has the time come for memory management in graphics chips? © Copyright 3Dlabs, 1999 - Page 2 3Dlabs Industrial-strength boards for design professionals • The pioneer in bringing professional-class 3D to the PC - The first 3D chip on the PC: the GLINT 300SX in 1994 - First integrated 3D setup chip: the GLINT Delta in 1996 - First integrated geometry and lighting chip: the GLINT Gamma in 1997 • Have been shipping professional 3D for over 15 years - Licensed IRIS GL from SGI before
    [Show full text]
  • Spectral Geometry Processing with Manifold Harmonics
    Author manuscript, published in "Computer Graphics Forum 27, 2 (2008) 251-260" DOI : 10.1111/j.1467-8659.2008.01122.x EUROGRAPHICS 2008 / G. Drettakis and R. Scopigno Volume 27 (2008), Number 2 (Guest Editors) Spectral Geometry Processing with Manifold Harmonics B. Vallet1 and B. Lévy1 1ALICE team, INRIA Abstract We present an explicit method to compute a generalization of the Fourier Transform on a mesh. It is well known that the eigenfunctions of the Laplace Beltrami operator (Manifold Harmonics) define a function basis allowing for such a transform. However, computing even just a few eigenvectors is out of reach for meshes with more than a few thousand vertices, and storing these eigenvectors is prohibitive for large meshes. To overcome these limitations, we propose a band-by-band spectrum computation algorithm and an out-of-core implementation that can compute thousands of eigenvectors for meshes with up to a million vertices. We also propose a limited-memory filtering algorithm, that does not need to store the eigenvectors. Using this latter algorithm, specific frequency bands can be filtered, without needing to compute the entire spectrum. Finally, we demonstrate some applications of our method to interactive convolution geometry filtering. These technical achievements are supported by a solid yet simple theoretic framework based on Discrete Exterior Calculus (DEC). In particular, the issues of symmetry and discretization of the operator are considered with great care. Categories and Subject Descriptors (according to ACM CCS): I.3.5 [Computer Graphics]: Computational Geometry and Object Modeling, Hierarchy and geometric transformations 1. Introduction the one described in [PG01] for point sets.
    [Show full text]