HOW TO VISUALIZE YOUR GPU-ACCELERATED SIMULATION RESULTS Peter Messmer, NVIDIA RANGE OF ANALYSIS AND VIZ TASKS  Analysis: Focus quantitative  Visualization: Focus qualitative  Monitoring, Steering TRADITIONAL HPC WORKFLOW Workstation Setup Analysis, Visualization Supercomputer Viz Cluster Dump, Checkpointing Visualization, Analysis File System TRADITIONAL WORKFLOW: CHALLENGES Lack of interactivity prevents “intuition” Workstation Setup Supercomputer High-end viz neglected due Analysis, Visualization to workflow complexity Viz Cluster I/O becomes main simulation bottleneck Dump, Checkpointing File System Viz resources need to scale with simulation Visualization, Analysis OUTLINE      Visualization applications CUDA/OpenGL interop Remote viz Parallel viz In-Situ viz High-level overview. Some parts platform dependent. Check with your sysadmin. VISUALIZATION APPLICATIONS NON-REPRESENTATIVE VIZ TOOLS SURVEY OF 25 HPC SITES Surveyed sites: LLNL LLNLORNLNERSC -OCF SCF LANL CCS DOD-ORC AFRLDSCR AFRL ARL NASAERDC NAVY MHPCC ORS CCAC NAS NASA-NCCS TACC CHPC RZG HLRN Julich CSCS CSC Hector Curie NON-REPRESENTATIVE VIZ TOOLS SURVEY OF 25 HPC SITES Surveyed sites: LLNL LLNLORNLNERSC -OCF SCF LANL CCS DOD-ORC AFRLDSCR AFRL ARL NASAERDC NAVY MHPCC ORS CCAC NAS NASA-NCCS TACC CHPC RZG HLRN Julich CSCS CSC Hector Curie VISIT  Scalar, vector and tensor field data features — Plots: contour, curve, mesh, pseudo-color, volume,.. — Operators: slice, iso-surface, threshold, binning,..  Quantitative and qualitative analysis/vis — Derived fields, dimension reduction, line-outs — Pick & query  Scalable architecture  Open source http://wci.llnl.gov/codes/visit/ VISIT  Cross-platform — Linux/Unix, OSX, Windows  Wide range of data formats — .vtk, .netcdf, .hdf5,..  Extensible — Plugin architecture  Embeddable  Python scriptable VISIT’S SCALABLE ARCHITECTURE     Client-server architecture Server MPI parallel Distributed filtering (multi-)GPU accelerated, parallel rendering* * requires X server on each node PARAVIEW  Scalar, vector and tensor field data features — Plots: contour, curve, mesh, pseudocolor, volume,.. — Operators: slice, iso-surface, threshold, binning,..  Quantitative and qualitative analysis/vis — Derived fields, dimension reduction, line-outs — Pick & query  Scalable architecture  Developed by Kitware, open source http://www.paraview.org PARAVIEW’S SCALABLE ARCHITECTURE  Client-server-server architecture  Server MPI parallel  Distributed filtering  GPU accelerated, parallel rendering* * requires X server on each node SOME OTHER TOOLS  Wide range of visualization tools  Often emerged from specialized application domain — Tecplot, EnSight: structural analysis, CFD — IndeX: seismic data processing & visualization — IDL: image processing  Early adopters of visual programming — AVS/Express, OpenDX FURTHER READING  Paraview Tutorial: http://www.paraview.org/Wiki/The_ParaView_Tutorial  VisIt Manuals/Tutorials: http://wci.llnl.gov/codes/visit/manuals.html VTK - THE VISUALIZATION TOOLKIT VISUALIZATION TOOLKIT     Focus on visualization, not (only) rendering Provides more complex operations on data (“filtering”) Introduces visualization pipeline At the core of many high-level viz tools http://www.vtk.org VTK PIPELINE Source Raw data, shapes Filter Transform raw data Mapper Map data to geometry Renderer Render the geometry OPENGL From the CUDA perspective OpenGL OPENGL: API FOR 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” (http://www.nvidia.com/content/GTC/documents/1055_GTC09.pdf) A SIMPLE OPENGL EXAMPLE glColor3f(1.0f,0,0); State-based API (sticky attributes) glBegin(GL_QUADS); glVertex3f(-1.0f, -1.0f, 0.0f); // The bottom left corner glVertex3f(-1.0f, 1.0f, 0.0f); // The top left corner glVertex3f(1.0f, 1.0f, 0.0f); // The top right corner glVertex3f(1.0f, -1.0f, 0.0f); // The bottom right corner Drawing glEnd(); glFlush(); Render to screen glColor3f(1.0f,0,0); glBegin(GL_QUADS); glVertex3f(-1.0f, -1.0f, 0.0f); glVertex3f(-1.0f, 1.0f, 0.0f); glVertex3f(1.0f, 1.0f, 0.0f); glVertex3f(1.0f, -1.0f, 0.0f); glEnd(); ? float* vert={-1.0f, -1.0f, ..}; float* d_vert; cudaMalloc(&d_vert, n); cudaMemcpy(d_vert, vert, n, cudaMemcpyHostToDevice); renderQuad<<<N/128, N>>>(d_vert); glFlush(); flushToScreen<<<..>>>(); CUDA-OPENGL INTEROP: MAPPING MEMORY • OpenGL: Opaque data buffer object • Virtex Buffer Object (VBO) • User has very limited control • CUDA: C-style memory management • User has full control • CUDA-OpenGL Interop: Map/Unmap OpenGL buffers into CUDA memory space RENDERING FROM A VBO Populate Render Display display() Initialize VBO init() Create VBO RENDERING FROM A VBO WITH CUDA INTEROP Initialize VBO Register VOB with CUDA init() Create VBO Populate Unmap VBO from CUDA Render Display display() Map VOB to CUDA MAIN OPENGL-CUDA INTEROP ROUTINES  Register VBO for CUDA cudaGraphicsGLRegisterBuffer(cuda_vbo, *vbo, flags);  Mapping of VBO to CUDA cudaGraphicsMapResources(1, cuda_vbo, 0); cudaGraphicsResourceGetMappedPointer(&d_ptr, size, *cuda_vbo);  Unmapping VBO from CUDA cudaGraphicsUnmapResources(1, cuda_vbo, 0); CAN ALL GPUS SUPPORT OPENGL?  GeForce : standard feature set including OpenGL 4.3/4.4  Quadro : + certain highly accelerated features (e.g. CAD)  Tesla: If GPU is in “All on” operation mode* Graphics capabilities disabled nvidia-smi –-query-gpu=gom.current nvidia-smi –q *requires “m” or “X” class devices Graphics capabilities enabled OPENGL SUMMARY + Relatively simple for basic viz + Existing/fixed/simple rendering pipeline - Low level - Triangles, points, rather than isosurfaces, height-fields - (currently) depends on Xserver for context creation - What about remote, parallel etc? MORE OPENGL INFORMATION AT GTC S4455 - Multi-GPU Rendering, 03/24, 14:30, 210A S4825 - Tegra K1 Developer Tools for Android: Unleashing the Power of the Kepler GPU with NVIDIA's latest Developer Tools Suite, 03/24, 14:30, 210E S4379 - OpenGL 4.4 Scene Rendering Techniques, 3/25, 13:00, 210C S4810 - NVIDIA Path Rendering: Accelerating Vector Graphics for the Mobile Web, 3/25, 13:30, LL21C S4610 - OpenGL: 2014 and Beyond, 3/25, 14:00, 210C S4385 - Order Independent Transparency in OpenGL, 3/25, 210C REMOTE VISUALIZATION OPENGL CONTEXT  State of an OpenGL instance — Incl. viewable surface — Interface to windowing system  Context creation: platform specific — Not part of OpenGL — Handled by Xserver in Linux/Unix-like systems  GLX: Interaction X<-> OpenGL LOCAL RENDERING Application X11 Events GLX OpenGL 2D/3D X Server Driver GPU, monitor attached Commands Xlib libGL X-FORWARDING: THE SIMPLEST FORM OF “REMOTE” RENDERING Application X11 Commands libGL Xlib X11 Events OpenGL/GLX On remote system: 2D/3D X Server export DISPLAY=59.151.136.110:0.0 Driver GPU, monitor attached Network X-FORWARDING + Simple! + ssh –X + No need to run X on remote machine - Lots of data crosses the network - All rendering performed on the local Xserver - Not useful for visualization of remote CUDA Interop apps (X-server doesn’t “see” remote GPU) SERVER-SIDE RENDERING + REMOTE VIZ APPLICATION Application Xlib libGL X11 Events GLX X11 Cmds OpenGL 2D/3D X Server Client Driver Driver GPU GPU, monitor attached Network SERVER-SIDE RENDERING + SCRAPING Application Xlib libGL X11 Events GLX OpenGL X11 Cmds 2D/3D X Server Driver Scraper Images GPU Client Driver GPU, monitor attached Network SERVER-SIDE RENDERING + SCRAPING + Full GPU acceleration + No X server on client Question: when to scrape? — Xserver not informed about direct rendering — Intercept glxSwapBuffers() - Not multi-user => Occasionally used for remote desktop tools GLX FORKING: OUT-OF-PROCESS Application Xlib libGL OpenGL/ GLX OpenGL X11 Events X11 Cmds Proxy X Server Images Images GLX 3D X Server Client App Driver Driver GPU GPU, monitor attached Network GLX FORKING: OUT-OF-PROCESS + Full GPU acceleration + No X server on client + Multi-User - All traffic through Proxy X server => Occasionally used for remote desktop tools GLX FORKING WITH INTERPOSER LIBRARY Application X11 Commands VirtualGL libGL X11 Events VGL transport (compressed) GLX OpenGL Xlib VirtualGL client Images Images 3D X Server 2D X Server Driver Driver GPU GPU, monitor attached Network GLX FORKING WITH INTERPOSER LIBRARY Application VirtualGL libGL X11 X11 Events Cmds Proxy X Server GLX OpenGL Xlib Images Images 3D X Server Client App Driver Driver GPU GPU, monitor attached Network VIRTUAL GL + TURBOVNC + Compressed image transport + Transparent to the application + Fully GPU accelerated OpenGL + Client with or without Xserver - Requires Xserver to access GPU http://www.virtualgl.org HOW TO SET UP VIRTUAL GL + TURBOVNC  Requires Xserver running on server — Root privileges for Xserver  Requires installation of VirtualGL, TurboVNC on server  Start VirtualGL-accelerated VNC server vncserver :3  TurboVNC viewer on client — Linux, Windows, Javascript CONNECTING CLIENT TO REMOTE SERVER CONNECTING TO REMOTE VNC SERVER VIA A GATEWAY  Establish tunnel on client ssh –L 3333:node01.cluster.net:5903 login.cluster.net  Connect client to localhost:3333 port 3333 port 5903 Gateway node01.cluster.net login.cluster.net client LAUNCHING REMOTE CUDA/OPENGL APPLICATIONS VIA VGLRUN vglrun :3 glxgears export DISPLAY=:3 vglrun simpleGL REMOTE VIZ IN PARAVIEW/VISIT  Approach 1: VirtualGL and VNC export DISPLAY=:3 vglrun paraview  Approach 2: Local Client — On remote server: pvserver On local workstation: paraview Paraview Client & Server under VirtualGL REMOTE VISUALIZATION SUMMARY  Multiple approaches to remote rendering — X forwarding, remote viz app, scraping, interposer process/library  Currently requires X server to generate context — EGL will fix this, but requires application changes PARALLEL VISUALIZATION Parallel Viz PARALLEL VISUALIZATION  Parallelism at multiple levels — Filtering — Rendering - Both supported by VisIt & Paraview - Heavy lifting already done! - Challenge: Setup in parallel environment - Both tools provide support for most common cases - Both VisIt & Paraview MPI parallel -> need custom build BASIC STEPS TO PARALLEL VISUALIZATION  Launch visualization server processes — Most likely through queuing system  Connect client to head node  Tunnel from workstation  Setting up virtualgl on remote node DOMAIN DECOMPOSITION OF DATA - Visualization algorithms can work on decomposed data - May require ghost cells - May lead to load imbalance No ghost cells required Ghost cells required PARALLEL COMPOSITING - Distributed geometry - Render with depth information - Composition using IceT http://icet.sandia.gov PARALLEL VISUALIZATION  Particularly important for large datasets  Visualization time often determined by filtering  Rendering important if — Highly complex visualization — Complex visualization effects — Low-power CPU  Transparent support in Paraview, VisIt IN-SITU VISUALIZATION & STEERING BENEFIT OF IN-SITU VISUALIZATION  Pipeline simulation cycle — Visualization/analysis integral part of simulation  Immediate feedback  Reduce pressure on file system  In some (future) cases: only way to analyze/visualize data LIBSIM IN VISIT: VISIT SERVER AS A LIBRARY CONNECTING TO A RUNNING APPLICATION BUILD VISUALIZATION PIPELINE FOR RUNNING APPLICATION BASIC INTERACTION/STEERING WITH RUNNING APPLICATION INTERACTIVE VIZ APPLICATION WITH LIBSIM  User implements callbacks for VisIt — meta-data, mesh, variables and domains  GetData: VisIt requests data for visualization — Work directly on simulation data — Transfer data from GPU  Command server: Interaction VisIt front-end <-> simulation — “Steering” http://www.visitusers.org/index.php?title=VisIttutorial-in-situ THE FUTURE The Future VISUALIZATION DATA FLOW Filtering CPU Simulation GPU Rendering VISUALIZATION WITH GPU ACCELERATED FILTER Filtering CPU Simulation GPU Filtering Rendering VISUALIZATION WITH GPU ACCELERATED FILTER AND HARDWARE RENDERING Filtering CPU Simulation GPU Filtering Rendering FULLY GPU ACCELERATED VISUALIZATION CPU Simulation GPU Filtering Rendering SDAV – SCIDAC-3 INSTITUTE ON VIZ/ANALYSIS AT SCALE (2011-2016)  Management, Analysis, Visualization — In-situ analysis, indexing/compression — I/O-, Viz frameworks — Supporting application teams  SDAV tools deployment: Paraview, VisIt, IceT, .. http://www.sdav-scidac.org/ SDAV – SCIDAC-3 INSTITUTE ON VIZ/ANALYSIS AT SCALE (2011-2016)  PISTON (LANL): Data Parallel Visualization Operators — Isosurface, cut, threshold — Built on top of Thrust — Support of most Paraview operators — Incorporation into VTK  DAX (Sandia), DIY (Argonne), EVAL (ORNL) S4553 - Productive Programming with Descriptive Data: Efficient Mesh-Based Algorithm Development in EAVL 4620 - DAX: A Massively Threaded Visualization and Analysis Toolkit for Extreme Scale VIZ TALKS AT GTC2014  S4571 - Applications of GPU Computing to Mission Design and Satellite Operations at NASA's Goddard Space Flight Center  Abel Brown ( Principal Systems Engineer, A.I. Solutions  S4632 - Exploring the Earth in 3D: Multiple GPUs for Accelerating Inverse Imaging  S4553 - Productive Programming with Descriptive Data: Efficient Mesh-Based Algorithm Development in EAVL  S4620 - Dax: A Massively Threaded Visualization and Analysis Toolkit for Extreme Scale  S4410 - Visualization and Analysis of Petascale Molecular Simulations with VMD  S4745 - Now You See It: Unmasking Nuclear and Radiological Threats Around the World  S4203 - Gesture-Based Interactive Visualization of Large-Scale Data using GPU and Latest Web Technologies  S4516 - Scientific Data Visualization on GPU-Enabled, Hybrid HPC Systems  S4599 - An Adventure in Porting: Adding GPU-Acceleration to Open-Source 3D Elastic Wave Modeling  S4778 - Interactive Processing and Visualization of Geospatial Imagery  S4140 - Live, Interactive, In-Situ, In-GPU Visualization of Plasma Simulations Running on GPU Supercomputers  S4400 - Petascale Molecular Ray Tracing: Accelerating VMD/Tachyon with OptiX  S4811 - Extreme Machine Learning with GPUs SUMMARY  Different methods of visualization at different levels — OpenGL, VTK, VisIt & Paraview  Remote visualization concepts — X forwarding, Viz app, scraping, GLX forking  Parallel rendering and compositing — Handled transparently in key tools  In-situ visualization concepts — Expose simulation variables to visualization tool, interactive viz  Future directions — GPU accelerated filtering and rendering Thanks to Gilles Fourestey (CSCS), Nina Suvanphim (Cray), Jean Favre (CSCS), Adam DeConinck (NVIDIA), Robert Crovella (NVIDIA), Dale Southard (NVIDIA), Hank Childs (U Oregon), Jeremy Meredith (ORNL), Ian Williams (NVIDIA), Steve Parker (NVIDIA), Kitware, Paraview, VisIt and VirtualGL developers for their support! ENJOY THE CONFERENCE AND SEE YOU AT GTC 2015! ABSTRACT (FOR REFERENCE ONLY) Learn how to take advantage of GPUs to visualize results of your GPU-accelerated simulation! This session will cover a broad range of visualization and analysis techniques allowing you to investigate your data on the fly. Starting with some basic CUDA/OpenGL interoperability, we will introduce more sophisticated data models allowing you to take advantage of widely used tools like ParaView and VisIt to visualize your GPU resident data. Questions like parallel compositing, remote visualization and application steering will be addressed in order to allow you to take full advantage of the GPUs installed in your supercomputing system.