SCIENTIFIC VISUALIZATION IN HPC
|
|
- Loreen Sullivan
- 6 years ago
- Views:
Transcription
1 April 4-7, 2016 Silicon Valley SCIENTIFIC VISUALIZATION IN HPC Peter Messmer, 4/4/2016
2 HIGH PERFORMANCE COMPUTING TODAY* "Yes," said Deep Thought, "I can do it." [Seven and a half million years later... ] The Answer to the Great Question... Of Life, the Universe and Everything... Is... Forty-two,' said Deep Thought, with infinite majesty and calm. Douglas Adams, Hitchhiker s Guide to the Galaxy *mostly 2
3 Latency Month Week HPC application Day Hour 5 min Accuracy Moves Flies 100 ms Has Engine Sit in it 30 ms 10 ms 3
4 Latency Month Week HPC application Day Hour 5 min Accuracy Moves Flies 100 ms Has Engine Sit in it Action Game 30 ms 10 ms 4
5 Latency Month Week HPC application Day CG Movie Hour 5 min Accuracy Moves Flies 100 ms Has Engine Sit in it Action Game 30 ms 10 ms Flight Simulator 5
6 Latency CG Movie Month Week Day Hour Parameter Space Exploration, Approximate models HPC application 5 min Accuracy Moves Flies 100 ms Has Engine Sit in it Action Game 30 ms 10 ms Flight Simulator 6
7 Latency CG Movie Month Week Day Hour Parameter Space Exploration, Approximate models HPC application 5 min Explorative Science, Real-time systems Accuracy Moves Flies 100 ms Has Engine Sit in it Action Game 30 ms 10 ms Opportunity! Flight Simulator 7
8 BONSAI WITH IN-SITU VIZ ON PIZ DAINT J. Bedorf, E. Gaburov, P.Messmer, S. Portegies Zwart Compute & Vis on 1024 GPU nodes Live Streaming Presented at at SC14, streaming from CSCS/Switzerland to to New Orleans 8
9 BONSAI WITH IN-SITU VIZ ON PIZ DAINT Compute & Vis on 1024 GPU nodes Live Streaming Presented at at SC14, streaming from CSCS/Switzerland to to New Orleans 9
10 10
11 VISUALIZATION RENDERING * * but it s a part of it Isosurfaces, Isovolumes Field Operators (Gradient, Curl,.. ) Streamlines Coordinate transformations Feature extraction Clip, Slice Compositing Surface Rendering Thresholding Binning, Resample Line Rendering Volume Rendering 11
12 VISUALIZATION PIPELINE Your typical scientific visualization system Simulation Analysis& Filtering Rendering Compositing Delivery Visualization - Analysis: Data processing to extract meaningful quantities of interest - Filtering: Conversion of simulation data into data ready for rendering - Rendering: Conversion of shapes to pixels (Fragment processing) - Compositing: Combination of independently generated pixels into final frame 12
13 VISUALIZATION TOOLKIT (VTK) Venerable backbone of scientific visualization Focus on visualization, not (only) rendering Provides more complex operations on data ( filtering ) Visualization pipeline At the core of many high-level viz tools Paraview, Visit,.. Developed by Kitware, open source Tue - 15:00 : S Visualization Toolkit: Improving Rendering and Compute on GPU's 13
14 VTK-M Visualization algorithms on modern architectures Ongoing development (Sandia, Kitware, ORNL,..) VTK type filters and more fine-grained worklets Platform portable (GPU, multicore CPU) Thrust, TBB backend Wed - 15:00 : S Adapting the Visualization Toolkit for Many-Core Processors with the VTK-m Library 14
15 TYPICAL HPC ENVIRONMENT Workstation on scientist s desk Remote HPC center Compute nodes not directly accessible Output from compute nodes: Workstation GPU Login Node - File transfer - X forwarding - Remote rendering Compute Node Compute Node GPU GPU 15
16 X-FORWARDING Be prepared for latencies ssh Y loginnode.edu ssh Y computenode No extra process on compute node Workstation GPU Login Node Rendering by workstation GPU X server on workstation needed Often prohibitively slow Compute Node Compute Node GPU GPU 16
17 REMOTE RENDERING Know your latencies Use compute node s GPU for rendering Capture renderings and ship pixel data to user Workstation GPU Login Node Compression is key Requires running X server on compute node* * Requirements will change with EGL Compute Node Compute Node GPU GPU 17
18 Remote visualization on Blue Waters Improvement in time to solution Limited resources in the local viz cluster Long data transfer times Stellar combustion visualized on Blue Waters (26 TB dataset) 6 GPUs in local viz cluster Data transfer Rendering 48 days 128 GPUs in HPC center 1 day 48x speed ups by using the Tesla GPUs in the HPC center Paul Woodward, U. Minnesota: HVR w/ OpenGL on Blue Waters Data courtesy of John Stone, UIUC 18 18
19 REMOTE RENDERING: VIDEO ENCODING Interactivity over large distances Open source approaches: TurboVNC + VirtualGL Currently not leveraging HW H264 encoder Commercial tools (e.g. Nice DCV) Leverages HW encoder No application modification needed NvENC library to access HW H264 encoder (lossless on Maxwell) Tue - 13:00: S VMD: Petascale Molecular Visualization and Analysis with Remote Video Streaming 19
20 OPENGL: GPU ACCELERATED RENDERING Primitives: points, lines, polygons Properties: colors, lighting, textures,.. View: camera position and perspective Shaders: Rendering to screen/framebuffer C-style functions, enums See e.g. What Every CUDA Programmer Should Know About OpenGL ( Mon - 10:00 : S High-Performance, Low-Overhead Rendering with OpenGL and Vulkan Mon - 14:00 : H Hangout: Maximizing Performance of CUDA and OpenGL Applications 20
21 VIS TOOLS EMBRACE OPENGL ON EGL Streamlined GPU accelerated off-screen rendering Prior to EGL: X server required for GPU accelerated OpenGL Full OpenGL on EGL announced at SC16 With EGL: OpenGL without X Major enabler for GPU rendering in HPC, incl. Cray systems* Quick adoption by vis tool developers * Driver version or newer required 21 4/20/2016
22 USE CASE: CSI ENSIGHT D EGL for batch renderer Customers post-process large collections of simulations offline Even though rendering takes place offscreen, EnSight requires that an X server is running, and is open enough to allow users to access it. At some sites, it is unacceptable to configure an X server to have wide open access. By using EGL to make our GL context and pbuffer, we can remove the need to start an Xserver, and solve all of these system management problems. Dave Bremer, CEI Image by Astec Inc 22 4/20/2016
23 PARAVIEW: DESIGNED FOR HPC Satisfying scientific computing needs Supports all major scientific data formats Extensive collection of operators : isosurface, streamlines, volume renderer, reductions, custom operators,.. Approachable user interface Supports remote, distributed memory vis Open source, free Extensible via plugins 23 4/20/2016
24 MODERN OPENGL FOR HPC VIZ Mandatory to access advanced rendering features VTK supports now OpenGL 3.2 Enables advanced shaders (AO, VXGI,..) Some algorithms well suited for distributed memory rendering GPU hardware support Data courtesy Florida Intl University & TACC 24
25 Bigger is better OPENGL RENDERING POWERHOUSE OpenGL vs OpenSWR 25
26 OPENGL NOT LIMITED TO RENDERING TASKS Interop goes both ways, esp with EGL CUDA->OpenGL typically one-way only EGL enables lighter weight access to OpenGL No X server needed Potential use of OpenGL for rasterization-like problems? Determine covered pixels 3D ordering/occlusion via Z-buffer 26
27 SCALABLE RENDERING AND COMPOSITING NVIDIA INDEX Large-scale (volume) data visualization Interactive visualization of TB of data Stand-alone or coupling into simulation HW Accelerated remote rendering Plugin for ParaView Mon - 10:00: S HPC Visualization Using NVIDIA IndeX 27
28 SCALABLE VOLUME RENDERING IN PARAVIEW Plugin enables GPU accelerated volume rendering Index plugin addresses shortcomings in ParaView built-in volume renderer Beta version supports - 3D structured, scalar grids - 32bit float, 16 bit/8bit uint - Overlay of opaque ParaView geometries (e.g. streamlines) Free plugin, requires commercial IndeX license RC coming out soon.. for enquiries Tue - 16:00 : S Toward Bridging the Gap Between High Quality and High Performance for HPC Visualization 28 4/20/2016
29 Advanced Rendering in scientific visualization Better insight with visual cues Two lights, no shadows Two lights, hard shadows, 1 shadow ray per light Ambient occlusion + two lights, 144 AO rays/hit Ray tracing offers ambient occlusion lighting, shadows, high quality transparent surfaces Courtesy of John Stone, UIUC 29
30 OPTIX RAY TRACING FRAMEWORK GPU accelerated ray-tracing framework Build your own RT application Generic Ray-Geometry interaction Rays with arbitrary payloads Multi-GPU-support Tue - 14:00 : H Hangout: CUDA for HPC Simulation and Visualization Tue - 14:00 : H Hangout: OptiX Ray Tracing Library: Best practices and Use-Case Consultation Wed -10:30 S: S Opticks: Optical Photon Simulation for High Energy Physics with NVIDIA OptiX 30
31 TELLING A BETTER STORY, VISUALLY Advanced rendering improves messaging Advanced rendering can help visual message, e.g. guiding the eye via depth of field Particularly useful for complex visualizations Interactive ray-tracing via NVIDIA Iray Post-processing of ParaView files Mon - 15:00 : H6142A - Hangout: Iray Rendering for Developers 31
32 PARALLEL COMPOSITING WITH ICE-T Modern networks remove compositing bottleneck Each node renders fraction of image Sort last compositing Widely used (Paraview, VisIt.. ) Critical element for real-time viz Up to 30 fps for 4k frames on 1024 nodes Cray XC30, Piz CSCS Tue- 14:30 : S Image Compositing on GPU-Accelerated Supercomputers 32
33 HIGH FRAMERATE = MINIMAL IMPACT ON SIMULATION FPS matter, even in HPC Real-time visualization only one use case Batch processing will not go away Acceptable time budget for visualization/analysis Up to the I/O time, ~ 2 % More diagnostics in the same time E.g. ParaView Cinema 33
34 VISUALIZATION-ENABLED SUPERCOMPUTERS CSCS Piz Daint NCSA Blue Waters ORNL Titan Galaxy formation Molecular dynamics -visualization-nvidia-tesla-gpus/ Cosmology highlights/visualization/66-accelerated-cosmologydata-anal.html 34
35 COMPUTE+VIS SUPPORTS MULTIPLE WORKFLOWS LEGACY WORKFLOW PARTITIONED SYSTEM CO-PROCESSING Separate compute & vis system Communication via file system Different nodes for different roles Communication via highspeed network Compute and visualization on same GPU Communication via hostdevice transfers or memcpy 35
36 - Minimize IO traffic IN-SITU VIS: ADVANTAGES AND OPPORTUNITIES - Exploit data locality - Less pressure on file system Workstation - Less time wasted in I/O - Reduce latency to first result - Monitoring, early termination GPU-accelerated Supercomputer - Enable real-time/interactive visualization - Novel workflows, new applications Tue 9:00: S Navigating the In-Situ Visualization Landscape File System 36
37 COSMO WITH IN-SITU VISUALIZATION Live, Interactive Weather Simulation COSMO-1 model, operational weather model in Switzerland 6 nodes Cray CS-Storm system 8 K80 GPUs/node 96 GPU sockets total ~ 20s per 0.7s NVIDIA IndeX for visualizaiton Tue - 13:30: S Co-Designing GPU-Based Systems and Tools for Numerical Weather Predictions 37
38 IN-SITU VISUALIZATION ON TITAN First prototype of ParaView in-situ visualization capabilities in pyfr (CFD) simulations, predicting jet engine acoustics Both compute and visualization running on Titan GPUs and streaming to a remote location When running PyFR at scale, it generates very large data sets that need analyzing for acoustics. The traditional post hoc method is simply not fit for purpose in situ visualization and processing are critical. We see a potential for 50x speed ups with in situ, which significantly accelerates our scientific discovery - Dr. Peter Vincent Imperial College Thu-10:30 : S Petascale Computational Fluid Dynamics with Python on GPUs Tue-15:00 : S Visualization Toolkit: Improving Rendering and Compute on GPU's 38
39 VISUALIZATION IN HPC Leverage graphics capabilities on heterogeneous nodes GPUs offer features for visualization, rendering, remote viz Modern networks enable parallel compositing at massive scale Graphics capabilities may help for graphics-like algorithms Supported by popular visualization tools Fast rendering relevant even for batch processing In-situ visualization for monitoring, steering, and other novel workflows 39
40 April 4-7, 2016 Silicon Valley THANK YOU JOIN THE NVIDIA DEVELOPER PROGRAM AT developer.nvidia.com/join
SCIENTIFIC VISUALIZATION ON GPU CLUSTERS PETER MESSMER, NVIDIA
SCIENTIFIC VISUALIZATION ON GPU CLUSTERS PETER MESSMER, NVIDIA Visualization Rendering Visualization Isosurfaces, Isovolumes Field Operators (Gradient, Curl,.. ) Coordinate transformations Feature extraction
More informationJohannes Günther, Senior Graphics Software Engineer. Intel Data Center Group, HPC Visualization
Johannes Günther, Senior Graphics Software Engineer Intel Data Center Group, HPC Visualization Data set provided by Florida International University: Simulated fluid flow through a porous medium Large
More informationGPU Ray Tracing at the Desktop and in the Cloud. Phillip Miller, NVIDIA Ludwig von Reiche, mental images
GPU Ray Tracing at the Desktop and in the Cloud Phillip Miller, NVIDIA Ludwig von Reiche, mental images Ray Tracing has always had an appeal Ray Tracing Prediction The future of interactive graphics is
More informationVMD: Immersive Molecular Visualization and Interactive Ray Tracing for Domes, Panoramic Theaters, and Head Mounted Displays
VMD: Immersive Molecular Visualization and Interactive Ray Tracing for Domes, Panoramic Theaters, and Head Mounted Displays John E. Stone Theoretical and Computational Biophysics Group Beckman Institute
More informationResponsive Large Data Analysis and Visualization with the ParaView Ecosystem. Patrick O Leary, Kitware Inc
Responsive Large Data Analysis and Visualization with the ParaView Ecosystem Patrick O Leary, Kitware Inc Hybrid Computing Attribute Titan Summit - 2018 Compute Nodes 18,688 ~3,400 Processor (1) 16-core
More informationInteractive Supercomputing for State-of-the-art Biomolecular Simulation
Interactive Supercomputing for State-of-the-art Biomolecular Simulation John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology University of
More informationBridging the Gap Between High Quality and High Performance for HPC Visualization
Bridging the Gap Between High Quality and High Performance for HPC Visualization Rob Sisneros National Center for Supercomputing Applications University of Illinois at Urbana Champaign Outline Why am I
More informationPipeline Operations. CS 4620 Lecture Steve Marschner. Cornell CS4620 Spring 2018 Lecture 11
Pipeline Operations CS 4620 Lecture 11 1 Pipeline you are here APPLICATION COMMAND STREAM 3D transformations; shading VERTEX PROCESSING TRANSFORMED GEOMETRY conversion of primitives to pixels RASTERIZATION
More informationPERFORMANCE OPTIMIZATIONS FOR AUTOMOTIVE SOFTWARE
April 4-7, 2016 Silicon Valley PERFORMANCE OPTIMIZATIONS FOR AUTOMOTIVE SOFTWARE Pradeep Chandrahasshenoy, Automotive Solutions Architect, NVIDIA Stefan Schoenefeld, ProViz DevTech, NVIDIA 4 th April 2016
More informationIntroduction to Visualization on Stampede
Introduction to Visualization on Stampede Aaron Birkland Cornell CAC With contributions from TACC visualization training materials Parallel Computing on Stampede June 11, 2013 From data to Insight Data
More informationAccelerating Realism with the (NVIDIA Scene Graph)
Accelerating Realism with the (NVIDIA Scene Graph) Holger Kunz Manager, Workstation Middleware Development Phillip Miller Director, Workstation Middleware Product Management NVIDIA application acceleration
More informationNVIDIA Update and Directions on GPU Acceleration for Earth System Models
NVIDIA Update and Directions on GPU Acceleration for Earth System Models Stan Posey, HPC Program Manager, ESM and CFD, NVIDIA, Santa Clara, CA, USA Carl Ponder, PhD, Applications Software Engineer, NVIDIA,
More informationVisualizing Biomolecular Complexes on x86 and KNL Platforms: Integrating VMD and OSPRay
Visualizing Biomolecular Complexes on x86 and KNL Platforms: Integrating VMD and OSPRay John E. Stone Theoretical and Computational Biophysics Group Beckman Institute for Advanced Science and Technology
More informationLecture Topic Projects
Lecture Topic Projects 1 Intro, schedule, and logistics 2 Applications of visual analytics, data types 3 Data sources and preparation Project 1 out 4 Data reduction, similarity & distance, data augmentation
More informationHardware Accelerated Volume Visualization. Leonid I. Dimitrov & Milos Sramek GMI Austrian Academy of Sciences
Hardware Accelerated Volume Visualization Leonid I. Dimitrov & Milos Sramek GMI Austrian Academy of Sciences A Real-Time VR System Real-Time: 25-30 frames per second 4D visualization: real time input of
More informationParallelizing Graphics Pipeline Execution (+ Basics of Characterizing a Rendering Workload)
Lecture 2: Parallelizing Graphics Pipeline Execution (+ Basics of Characterizing a Rendering Workload) Visual Computing Systems Analyzing a 3D Graphics Workload Where is most of the work done? Memory Vertex
More informationCS427 Multicore Architecture and Parallel Computing
CS427 Multicore Architecture and Parallel Computing Lecture 6 GPU Architecture Li Jiang 2014/10/9 1 GPU Scaling A quiet revolution and potential build-up Calculation: 936 GFLOPS vs. 102 GFLOPS Memory Bandwidth:
More informationPipeline Operations. CS 4620 Lecture 14
Pipeline Operations CS 4620 Lecture 14 2014 Steve Marschner 1 Pipeline you are here APPLICATION COMMAND STREAM 3D transformations; shading VERTEX PROCESSING TRANSFORMED GEOMETRY conversion of primitives
More informationJim Jeffers Principal Engineer and Manager, HPC Visualization Intel Corporation
Jim Jeffers Principal Engineer and Manager, HPC Visualization 2016 Intel Corporation Software Defined Visualization Delivers Higher Visual Fidelity Of Larger DataSETS On Existing HPC Infrastructure Through
More informationCS450/550. Pipeline Architecture. Adapted From: Angel and Shreiner: Interactive Computer Graphics6E Addison-Wesley 2012
CS450/550 Pipeline Architecture Adapted From: Angel and Shreiner: Interactive Computer Graphics6E Addison-Wesley 2012 0 Objectives Learn the basic components of a graphics system Introduce the OpenGL pipeline
More informationHPC Visualization with EnSight
HPC Visualization with EnSight Beijing 2010.10.27 Aric Meyer Marketing Director, Asia & Pacific CEI Computational Engineering International, Inc. Founded in 1994 out of Cray Research Headquarters in Raleigh,
More informationNVIDIA DESIGNWORKS Ankit Patel - Prerna Dogra -
NVIDIA DESIGNWORKS Ankit Patel - ankitp@nvidia.com Prerna Dogra - pdogra@nvidia.com 1 Autonomous Driving Deep Learning Visual Effects Virtual Desktops Visual Computing is our singular mission Gaming Product
More informationVisualization of Energy Conversion Processes in a Light Harvesting Organelle at Atomic Detail
Visualization of Energy Conversion Processes in a Light Harvesting Organelle at Atomic Detail Theoretical and Computational Biophysics Group Center for the Physics of Living Cells Beckman Institute for
More informationLecture Topic Projects 1 Intro, schedule, and logistics 2 Applications of visual analytics, basic tasks, data types 3 Introduction to D3, basic vis
Lecture Topic Projects 1 Intro, schedule, and logistics 2 Applications of visual analytics, basic tasks, data types 3 Introduction to D3, basic vis techniques for non-spatial data Project #1 out 4 Data
More informationExploratory Visualization of Petascale Particle Data in Nvidia DGX-1
Exploratory Visualization of Petascale Particle Data in Nvidia DGX-1 Benjamin Hernandez, PhD hernandezarb@ornl.gov Advanced Data and Workflows Group Oak Ridge Leadership Computing Facility Oak Ridge National
More information2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or
2007 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries. The
More informationCS8803SC Software and Hardware Cooperative Computing GPGPU. Prof. Hyesoon Kim School of Computer Science Georgia Institute of Technology
CS8803SC Software and Hardware Cooperative Computing GPGPU Prof. Hyesoon Kim School of Computer Science Georgia Institute of Technology Why GPU? A quiet revolution and potential build-up Calculation: 367
More informationParallelizing Graphics Pipeline Execution (+ Basics of Characterizing a Rendering Workload)
Lecture 2: Parallelizing Graphics Pipeline Execution (+ Basics of Characterizing a Rendering Workload) Visual Computing Systems Today Finishing up from last time Brief discussion of graphics workload metrics
More informationCS GPU and GPGPU Programming Lecture 2: Introduction; GPU Architecture 1. Markus Hadwiger, KAUST
CS 380 - GPU and GPGPU Programming Lecture 2: Introduction; GPU Architecture 1 Markus Hadwiger, KAUST Reading Assignment #2 (until Feb. 17) Read (required): GLSL book, chapter 4 (The OpenGL Programmable
More informationWorking with Metal Overview
Graphics and Games #WWDC14 Working with Metal Overview Session 603 Jeremy Sandmel GPU Software 2014 Apple Inc. All rights reserved. Redistribution or public display not permitted without written permission
More informationECP Alpine: Algorithms and Infrastructure for In Situ Visualization and Analysis
ECP Alpine: Algorithms and Infrastructure for In Situ Visualization and Analysis Presented By: Matt Larsen LLNL-PRES-731545 This work was performed under the auspices of the U.S. Department of Energy by
More informationReal-Time Rendering (Echtzeitgraphik) Michael Wimmer
Real-Time Rendering (Echtzeitgraphik) Michael Wimmer wimmer@cg.tuwien.ac.at Walking down the graphics pipeline Application Geometry Rasterizer What for? Understanding the rendering pipeline is the key
More informationVisualization. Christopher Fluke. & David Barnes. Gas and Stars in Galaxies A Multi-Wavelength 3D Perspective. THINGS datacubes courtesy Erwin De Blok
Visualization Christopher Fluke & David Barnes THINGS datacubes courtesy Erwin De Blok Gas and Stars in Galaxies A Multi-Wavelength 3D Perspective Astronomy Datasets Increasingly multi-dimensional (N 3)
More informationGraphics Hardware and Display Devices
Graphics Hardware and Display Devices CSE328 Lectures Graphics/Visualization Hardware Many graphics/visualization algorithms can be implemented efficiently and inexpensively in hardware Facilitates interactive
More informationGPGPU Applications. for Hydrological and Atmospheric Simulations. and Visualizations on the Web. Ibrahim Demir
GPGPU Applications for Hydrological and Atmospheric Simulations and Visualizations on the Web Ibrahim Demir Big Data We are collecting and generating data on a petabyte scale (1Pb = 1,000 Tb = 1M Gb) Data
More informationCSE4030 Introduction to Computer Graphics
CSE4030 Introduction to Computer Graphics Dongguk University Jeong-Mo Hong Timetable 00:00~00:10 Introduction (English) 00:10~00:50 Topic 1 (English) 00:50~00:60 Q&A (English, Korean) 01:00~01:40 Topic
More informationHeadline in Arial Bold 30pt. Visualisation using the Grid Jeff Adie Principal Systems Engineer, SAPK July 2008
Headline in Arial Bold 30pt Visualisation using the Grid Jeff Adie Principal Systems Engineer, SAPK July 2008 Agenda Visualisation Today User Trends Technology Trends Grid Viz Nodes Software Ecosystem
More informationCourse Recap + 3D Graphics on Mobile GPUs
Lecture 18: Course Recap + 3D Graphics on Mobile GPUs Interactive Computer Graphics Q. What is a big concern in mobile computing? A. Power Two reasons to save power Run at higher performance for a fixed
More informationCSE 591/392: GPU Programming. Introduction. Klaus Mueller. Computer Science Department Stony Brook University
CSE 591/392: GPU Programming Introduction Klaus Mueller Computer Science Department Stony Brook University First: A Big Word of Thanks! to the millions of computer game enthusiasts worldwide Who demand
More informationCS4620/5620: Lecture 14 Pipeline
CS4620/5620: Lecture 14 Pipeline 1 Rasterizing triangles Summary 1! evaluation of linear functions on pixel grid 2! functions defined by parameter values at vertices 3! using extra parameters to determine
More informationGraphics Processing Unit Architecture (GPU Arch)
Graphics Processing Unit Architecture (GPU Arch) With a focus on NVIDIA GeForce 6800 GPU 1 What is a GPU From Wikipedia : A specialized processor efficient at manipulating and displaying computer graphics
More informationXSEDE Visualization Use Cases
XSEDE Visualization Use Cases July 24, 2014 Version 1.4 XSEDE Visualization Use Cases Page i Table of Contents A. Document History iii B. Document Scope iv XSEDE Visualization Use Cases Page ii A. Document
More informationCSE 591: GPU Programming. Introduction. Entertainment Graphics: Virtual Realism for the Masses. Computer games need to have: Klaus Mueller
Entertainment Graphics: Virtual Realism for the Masses CSE 591: GPU Programming Introduction Computer games need to have: realistic appearance of characters and objects believable and creative shading,
More information2: Introducing image synthesis. Some orientation how did we get here? Graphics system architecture Overview of OpenGL / GLU / GLUT
COMP27112 Computer Graphics and Image Processing 2: Introducing image synthesis Toby.Howard@manchester.ac.uk 1 Introduction In these notes we ll cover: Some orientation how did we get here? Graphics system
More informationSiggraph Asia December 2011
Siggraph Asia December 2011 Advanced Graphics Always Core to NVIDIA Worldwide Leader in GPU Development & Professional Graphics Advanced Rendering Commitment 2007 Worldwide Leader in GPU Development &
More informationReal-Time Shadows. MIT EECS 6.837, Durand and Cutler
Real-Time Shadows Last Time? The graphics pipeline Clipping & rasterization of polygons Visibility the depth buffer (z-buffer) Schedule Quiz 2: Thursday November 20 th, in class (two weeks from Thursday)
More informationModels and Architectures
Models and Architectures Objectives Learn the basic design of a graphics system Introduce graphics pipeline architecture Examine software components for an interactive graphics system 1 Image Formation
More informationRendering. Converting a 3D scene to a 2D image. Camera. Light. Rendering. View Plane
Rendering Pipeline Rendering Converting a 3D scene to a 2D image Rendering Light Camera 3D Model View Plane Rendering Converting a 3D scene to a 2D image Basic rendering tasks: Modeling: creating the world
More informationSpring 2009 Prof. Hyesoon Kim
Spring 2009 Prof. Hyesoon Kim Application Geometry Rasterizer CPU Each stage cane be also pipelined The slowest of the pipeline stage determines the rendering speed. Frames per second (fps) Executes on
More informationPowerVR Hardware. Architecture Overview for Developers
Public Imagination Technologies PowerVR Hardware Public. This publication contains proprietary information which is subject to change without notice and is supplied 'as is' without warranty of any kind.
More informationRendering Algorithms: Real-time indirect illumination. Spring 2010 Matthias Zwicker
Rendering Algorithms: Real-time indirect illumination Spring 2010 Matthias Zwicker Today Real-time indirect illumination Ray tracing vs. Rasterization Screen space techniques Visibility & shadows Instant
More informationRay Tracing with Multi-Core/Shared Memory Systems. Abe Stephens
Ray Tracing with Multi-Core/Shared Memory Systems Abe Stephens Real-time Interactive Massive Model Visualization Tutorial EuroGraphics 2006. Vienna Austria. Monday September 4, 2006 http://www.sci.utah.edu/~abe/massive06/
More informationPerformance OpenGL Programming (for whatever reason)
Performance OpenGL Programming (for whatever reason) Mike Bailey Oregon State University Performance Bottlenecks In general there are four places a graphics system can become bottlenecked: 1. The computer
More informationModern Processor Architectures. L25: Modern Compiler Design
Modern Processor Architectures L25: Modern Compiler Design The 1960s - 1970s Instructions took multiple cycles Only one instruction in flight at once Optimisation meant minimising the number of instructions
More informationEnhancing Traditional Rasterization Graphics with Ray Tracing. October 2015
Enhancing Traditional Rasterization Graphics with Ray Tracing October 2015 James Rumble Developer Technology Engineer, PowerVR Graphics Overview Ray Tracing Fundamentals PowerVR Ray Tracing Pipeline Using
More information! Readings! ! Room-level, on-chip! vs.!
1! 2! Suggested Readings!! Readings!! H&P: Chapter 7 especially 7.1-7.8!! (Over next 2 weeks)!! Introduction to Parallel Computing!! https://computing.llnl.gov/tutorials/parallel_comp/!! POSIX Threads
More informationCUDA Conference. Walter Mundt-Blum March 6th, 2008
CUDA Conference Walter Mundt-Blum March 6th, 2008 NVIDIA s Businesses Multiple Growth Engines GPU Graphics Processing Units MCP Media and Communications Processors PESG Professional Embedded & Solutions
More informationSpring 2011 Prof. Hyesoon Kim
Spring 2011 Prof. Hyesoon Kim Application Geometry Rasterizer CPU Each stage cane be also pipelined The slowest of the pipeline stage determines the rendering speed. Frames per second (fps) Executes on
More informationGraphics Hardware. Instructor Stephen J. Guy
Instructor Stephen J. Guy Overview What is a GPU Evolution of GPU GPU Design Modern Features Programmability! Programming Examples Overview What is a GPU Evolution of GPU GPU Design Modern Features Programmability!
More informationA Broad Overview of Scientific Visualization with a Focus on Geophysical Turbulence Simulation Data (SciVis
A Broad Overview of Scientific Visualization with a Focus on Geophysical Turbulence Simulation Data (SciVis 101 for Turbulence Researchers) John Clyne clyne@ucar.edu Examples: Medicine Examples: Biology
More informationCurrent Trends in Computer Graphics Hardware
Current Trends in Computer Graphics Hardware Dirk Reiners University of Louisiana Lafayette, LA Quick Introduction Assistant Professor in Computer Science at University of Louisiana, Lafayette (since 2006)
More informationInteractively Visualizing Science at Scale
Interactively Visualizing Science at Scale Kelly Gaither Director of Visualization/Senior Research Scientist Texas Advanced Computing Center November 13, 2012 Issues and Concerns Maximizing Scientific
More informationDirect Rendering of Trimmed NURBS Surfaces
Direct Rendering of Trimmed NURBS Surfaces Hardware Graphics Pipeline 2/ 81 Hardware Graphics Pipeline GPU Video Memory CPU Vertex Processor Raster Unit Fragment Processor Render Target Screen Extended
More informationASYNCHRONOUS SHADERS WHITE PAPER 0
ASYNCHRONOUS SHADERS WHITE PAPER 0 INTRODUCTION GPU technology is constantly evolving to deliver more performance with lower cost and lower power consumption. Transistor scaling and Moore s Law have helped
More informationVisualization on BioHPC
Visualization on BioHPC [web] [email] portal.biohpc.swmed.edu biohpc-help@utsouthwestern.edu 1 Updated for 2015-09-16 Outline What is Visualization - Scientific Visualization - Work flow for Visualization
More informationCS230 : Computer Graphics Lecture 4. Tamar Shinar Computer Science & Engineering UC Riverside
CS230 : Computer Graphics Lecture 4 Tamar Shinar Computer Science & Engineering UC Riverside Shadows Shadows for each pixel do compute viewing ray if ( ray hits an object with t in [0, inf] ) then compute
More informationVisIt Libsim. An in-situ visualisation library
VisIt Libsim. An in-situ visualisation library December 2017 Jean M. Favre, CSCS Outline Motivations In-situ visualization In-situ processing strategies VisIt s libsim library Enable visualization in a
More informationComputer Graphics (CS 543) Lecture 13b Ray Tracing (Part 1) Prof Emmanuel Agu. Computer Science Dept. Worcester Polytechnic Institute (WPI)
Computer Graphics (CS 543) Lecture 13b Ray Tracing (Part 1) Prof Emmanuel Agu Computer Science Dept. Worcester Polytechnic Institute (WPI) Raytracing Global illumination-based rendering method Simulates
More informationThreading Hardware in G80
ing Hardware in G80 1 Sources Slides by ECE 498 AL : Programming Massively Parallel Processors : Wen-Mei Hwu John Nickolls, NVIDIA 2 3D 3D API: API: OpenGL OpenGL or or Direct3D Direct3D GPU Command &
More informationPortland State University ECE 588/688. Graphics Processors
Portland State University ECE 588/688 Graphics Processors Copyright by Alaa Alameldeen 2018 Why Graphics Processors? Graphics programs have different characteristics from general purpose programs Highly
More informationGeForce3 OpenGL Performance. John Spitzer
GeForce3 OpenGL Performance John Spitzer GeForce3 OpenGL Performance John Spitzer Manager, OpenGL Applications Engineering jspitzer@nvidia.com Possible Performance Bottlenecks They mirror the OpenGL pipeline
More informationChris Sewell Li-Ta Lo James Ahrens Los Alamos National Laboratory
Portability and Performance for Visualization and Analysis Operators Using the Data-Parallel PISTON Framework Chris Sewell Li-Ta Lo James Ahrens Los Alamos National Laboratory Outline! Motivation Portability
More informationACCELERATED COMPUTING: THE PATH FORWARD. Jensen Huang, Founder & CEO SC17 Nov. 13, 2017
ACCELERATED COMPUTING: THE PATH FORWARD Jensen Huang, Founder & CEO SC17 Nov. 13, 2017 COMPUTING AFTER MOORE S LAW Tech Walker 40 Years of CPU Trend Data 10 7 GPU-Accelerated Computing 10 5 1.1X per year
More informationCS 498 VR. Lecture 19-4/9/18. go.illinois.edu/vrlect19
CS 498 VR Lecture 19-4/9/18 go.illinois.edu/vrlect19 Review from previous lectures Image-order Rendering and Object-order Rendering Image-order Rendering: - Process: Ray Generation, Ray Intersection, Assign
More informationNVIDIA RESEARCH TALK: THE MAGIC BEHIND GAMEWORKS HYBRID FRUSTUM TRACED SHADOWS
NVIDIA RESEARCH TALK: THE MAGIC BEHIND GAMEWORKS HYBRID FRUSTUM TRACED SHADOWS Chris Wyman July 28, 2016 Left: Hybrid Frustum Traced Shadows (HFTS) Right: Percentage Closer Soft Shadows (PCSS) MARCH 2016:
More informationGraphics Pipeline. CS535 Fall Daniel G. Aliaga Department of Computer Science Purdue University
Graphics Pipeline CS535 Fall 2016 Daniel G. Aliaga Department of Computer Science Purdue University Ray-tracing Inverse mapping for every pixel construct a ray from the eye for every object in the scene
More informationSteve Scott, Tesla CTO SC 11 November 15, 2011
Steve Scott, Tesla CTO SC 11 November 15, 2011 What goal do these products have in common? Performance / W Exaflop Expectations First Exaflop Computer K Computer ~10 MW CM5 ~200 KW Not constant size, cost
More informationSIGHT. Benjamin Hernandez, PhD Advanced Data and Workflow(s) Group
SIGHT Benjamin Hernandez, PhD Advanced Data and Workflow(s) Group hernandezarb@ornl.gov ORNL is managed by UT-Battelle for the US Department of Energy name 1 Presentation This research used resources of
More informationLast Time. Reading for Today: Graphics Pipeline. Clipping. Rasterization
Last Time Modeling Transformations Illumination (Shading) Real-Time Shadows Viewing Transformation (Perspective / Orthographic) Clipping Projection (to Screen Space) Scan Conversion (Rasterization) Visibility
More informationModern Processor Architectures (A compiler writer s perspective) L25: Modern Compiler Design
Modern Processor Architectures (A compiler writer s perspective) L25: Modern Compiler Design The 1960s - 1970s Instructions took multiple cycles Only one instruction in flight at once Optimisation meant
More informationThe Rasterization Pipeline
Lecture 5: The Rasterization Pipeline (and its implementation on GPUs) Computer Graphics CMU 15-462/15-662, Fall 2015 What you know how to do (at this point in the course) y y z x (w, h) z x Position objects
More informationOpenMPSuperscalar: Task-Parallel Simulation and Visualization of Crowds with Several CPUs and GPUs
www.bsc.es OpenMPSuperscalar: Task-Parallel Simulation and Visualization of Crowds with Several CPUs and GPUs Hugo Pérez UPC-BSC Benjamin Hernandez Oak Ridge National Lab Isaac Rudomin BSC March 2015 OUTLINE
More informationS U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T
S U N G - E U I YO O N, K A I S T R E N D E R I N G F R E E LY A VA I L A B L E O N T H E I N T E R N E T Copyright 2018 Sung-eui Yoon, KAIST freely available on the internet http://sglab.kaist.ac.kr/~sungeui/render
More informationDeep Learning: Transforming Engineering and Science The MathWorks, Inc.
Deep Learning: Transforming Engineering and Science 1 2015 The MathWorks, Inc. DEEP LEARNING: TRANSFORMING ENGINEERING AND SCIENCE A THE NEW RISE ERA OF OF GPU COMPUTING 3 NVIDIA A IS NEW THE WORLD S ERA
More informationVisualization with ParaView
Visualization with Before we begin Make sure you have 3.10.1 installed so you can follow along in the lab section http://paraview.org/paraview/resources/software.html http://www.paraview.org/ Background
More informationReal-Time Shadows. Last Time? Schedule. Questions? Today. Why are Shadows Important?
Last Time? Real-Time Shadows The graphics pipeline Clipping & rasterization of polygons Visibility the depth buffer (z-buffer) Schedule Questions? Quiz 2: Thursday November 2 th, in class (two weeks from
More informationGraphics Hardware. Graphics Processing Unit (GPU) is a Subsidiary hardware. With massively multi-threaded many-core. Dedicated to 2D and 3D graphics
Why GPU? Chapter 1 Graphics Hardware Graphics Processing Unit (GPU) is a Subsidiary hardware With massively multi-threaded many-core Dedicated to 2D and 3D graphics Special purpose low functionality, high
More informationProgrammable GPUS. Last Time? Reading for Today. Homework 4. Planar Shadows Projective Texture Shadows Shadow Maps Shadow Volumes
Last Time? Programmable GPUS Planar Shadows Projective Texture Shadows Shadow Maps Shadow Volumes frame buffer depth buffer stencil buffer Stencil Buffer Homework 4 Reading for Create some geometry "Rendering
More informationCS452/552; EE465/505. Finale!
CS452/552; EE465/505 Finale! 4-23 15 Outline! Non-Photorealistic Rendering! What s Next? Read: Angel, Section 6.11 Nonphotorealistic Shading Color Plate 11 Cartoon-shaded teapot Final Exam: Monday, April
More informationINTEL HPC DEVELOPER CONFERENCE FUEL YOUR INSIGHT
INTEL HPC DEVELOPER CONFERENCE FUEL YOUR INSIGHT INTEL HPC DEVELOPER CONFERENCE FUEL YOUR INSIGHT UPDATE ON OPENSWR: A SCALABLE HIGH- PERFORMANCE SOFTWARE RASTERIZER FOR SCIVIS Jefferson Amstutz Intel
More informationVisualization and VR for the Grid
Visualization and VR for the Grid Chris Johnson Scientific Computing and Imaging Institute University of Utah Computational Science Pipeline Construct a model of the physical domain (Mesh Generation, CAD)
More informationAn In-Situ Visualization Approach for the K computer using Mesa 3D and KVS
An In-Situ Visualization Approach for the K computer using Mesa 3D and KVS Kengo Hayashi 1,2, Naohisa Sakamoto 1,2 Jorji Nonaka 2, Motohiko Mastuda 2, Fumiyoshi Shoji 1 Kobe Univesity, 2 RIKEN Center for
More informationInsight VisREU Site. Agenda. Introduction to Scientific Visualization Using 6/16/2015. The purpose of visualization is insight, not pictures.
2015 VisREU Site Introduction to Scientific Visualization Using Vetria L. Byrd, Director Advanced Visualization VisREU Site Coordinator REU Site Sponsored by NSF ACI Award 1359223 Introduction to SciVis(High
More informationGraphics and Imaging Architectures
Graphics and Imaging Architectures Kayvon Fatahalian http://www.cs.cmu.edu/afs/cs/academic/class/15869-f11/www/ About Kayvon New faculty, just arrived from Stanford Dissertation: Evolving real-time graphics
More informationIn-Situ Data Analysis and Visualization: ParaView, Calalyst and VTK-m
In-Situ Data Analysis and Visualization: ParaView, Calalyst and VTK-m GTC, San Jose, CA March, 2015 Robert Maynard Marcus D. Hanwell 1 Agenda Introduction to ParaView Catalyst Run example Catalyst Script
More informationCMPE 665:Multiple Processor Systems CUDA-AWARE MPI VIGNESH GOVINDARAJULU KOTHANDAPANI RANJITH MURUGESAN
CMPE 665:Multiple Processor Systems CUDA-AWARE MPI VIGNESH GOVINDARAJULU KOTHANDAPANI RANJITH MURUGESAN Graphics Processing Unit Accelerate the creation of images in a frame buffer intended for the output
More informationScientific Visualization An Introduction
Scientific Visualization An Introduction Featuring Vetria L. Byrd, PhD Assistant Professor Research and Technology Development Conference Missouri S&T September 13, 2016 RTD 2016 Thank You! Missouri S&T
More informationEVALUATING WINDOWS 10 LEARN WHY YOUR USERS NEED GPU ACCELERATION
May 8-11 2017 Silicon Valley EVALUATING WINDOWS 10 LEARN WHY YOUR USERS NEED GPU ACCELERATION Jason Kyungho Lee, Sr Performance Engineer, NVIDAI GRID @NVIDIA Hari Sivaraman, Staff Engineer @ VMware Introduction
More informationPowerVR Performance Recommendations. The Golden Rules
PowerVR Performance Recommendations Public. This publication contains proprietary information which is subject to change without notice and is supplied 'as is' without warranty of any kind. Redistribution
More informationTSBK03 Screen-Space Ambient Occlusion
TSBK03 Screen-Space Ambient Occlusion Joakim Gebart, Jimmy Liikala December 15, 2013 Contents 1 Abstract 1 2 History 2 2.1 Crysis method..................................... 2 3 Chosen method 2 3.1 Algorithm
More information