Visualization Toolkit: Improving Rendering and Compute on GPUs GTC, San Jose, CA April, 2016 Robert Maynard
Kitware, Inc. • Founded in 1998 by five former GE Research employees • 115 current employees; 41 with PhDs Privately held, profitable from creation, no debt • • Rapidly Growing: >30% in 2011 • Offices • 2011 Small Business – Clifton Park, NY Administration’s Tibbetts Award – Carrboro, NC • HPCWire Readers and – Santa Fe, NM Editor’s Choice • Inc’s 5000 List since – Lyon, France 2008
Business Model: Open Source • Open-source Software – Normally BSD-licensed – Collaboration platforms • Collaborative Research and Development • Technology Integration • Services, support, and consulting • Training and webinars
Overview of Software Process • Openly developed, reusable frameworks – Open-source frameworks – Developed openly – Cross-platform compatibility – Tested and verified – Contribution model – Supported by Kitware experts • Liberally-licensed to facilitate research
The Visualization Toolkit • Founded in 1993 as example code for “The Visualization Textbook”. • Used in many projects developed all over the world: – ParaView, VisIt – Osirix, 3D Slicer – Mayavi, MOOSE
The Visualization Pipeline • A sequence of algorithms that operate on data objects to generate geometry Filter Mapper Actor Render on Data Data screen Source Filter Mapper Actor Data Data
VTK Update vtkCommonCore • NVIDIA and Kitware collaboration vtkRenderingCore vtkVolume • Major rewrite of VTK’s rendering code OpenGL OpenGL2 OpenGL OpenGL2 – Geometry and Volume rendering – Supports desktop and embedded systems – Take advantage of modern programmable GPU: migration from fixed pipeline to buffers and GLSL – Recast interaction with data pipelines to take full advantage of batching for improved transfer
VTK 7.X • New minimum OpenGL version – OpenGL 3.2, OpenGL ES 3.0 • Large Volume support • Tested on Android and iOS • Single code base for all platforms • Multi-touch interaction supported
Benchmarking: Old vs New • Looking at static scenes – Time to first render – Average time of rotated subsequent renders • Added some new benchmarking tools • Aim to provide systematic comparison
Time to Render For First Frame (K2200) 16 14 12 10 Time (s) 8 VTK 6 6 VTK 7 4 2 0 1 million 5 million 20 million 30 million Triangles
Time to Render Subsequent Frames(K2200) 3.5 3 2.5 Time (s) 2 VTK 6 1.5 VTK 7 1 0.5 0.0025333 0.00711968 0.010502 0.000806384 0 1 million 5 million 20 million 30 million Triangles
Time to Render For First Frame (K2200) 25 20 15 Time (s) VTK 6 10 VTK 7 5 0 10 million 50 million 100 million 500 million 1000 million Voxels
Time to Render Subsequent Frames (K2200) 0.02 0.018 0.016 0.014 0.012 Time (s) 0.01 VTK 6 0.008 VTK 7 0.006 0.004 0.002 0 10 million 50 million 100 million 500 million 1000 million Voxels
TL;DR It Is Fast • Geometry rendering about 100 Gmes faster – IniGal frame/updated input ten Gmes faster – Memory footprint about half for single large geometry Volume rendering twice as fast • – Improved capability to run across systems Capable of rendering much larger systems • Other advanced rendering techniques • – Glyphs, impostors, Gaussian splaRng for data
VTK Point Rendering Work • Hierarchical LOD • View Frustum Culling • Spatial Randomization • Post Render Screen Space Point Filling • 960M renders about 1billion points/second for a 200M point dataset • TTFR ~1sec for 200M points • Goal is 30fps/1Billion Points
VTK Point Cloud Work • Point Cloud Algorithmic Efforts – Modeling – Cleaning – Segmentation – Registration – Geometry – Fitting – Surface reconstruction – Subsampling / LOD
ParaView is an open-source, multi-platform, data analysis and visualization application for analyzing extremely large datasets using distributed memory computing resources
ParaView 5.X Features • New Rendering is the default – OpenGL 3.2, OpenGL ES 3.0 • Supports EGL on HPC machines • Faster Remote Image Transfer – More work still to come
NVIDIA IndeX Plugin • NVIDIA IndeX is a scalable volume renderer NVIDIA IndeX Plugin that provides interactive performance on GPU clusters and supercomputers • ParaView IndeX Plugin is now in beta NVIDIA IndeX • Data: 3D structured grids • Fields: Scalar float, 8, 16, 32bit u-integers • Overlay of opaque ParaView geometries (e.g. streamlines) • Supports client-server mode via MPI • Free plugin, requires commercial IndeX license
Computation On Node 125 PB/s VisualizaGon Node Memory 4.5 PB/s Getting faster on Summit Off Node VisualizaGon Interconnect (Largest Cross-Sectional Bandwidth) 24 TB/s Same bandwidth on Summit Storage Post Hoc 1.4 TB/s VisualizaGon
What is it? SimulaGon SimulaGon A set of tools for in situ Separate MPI analysis and visualizaGon Why use it? Brief answer – one soluGon to reduced relaGve IO performance on the road to Disk Disk exascale Storage Storage More details – provide flexible analysis opGons and beber usage of computaGonal resources to reduce scienGsts’ VisualizaGon VisualizaGon Gme to insight into given In Situ In Transit problems
Simulation Augmented # Create the reader and set the filename. reader = sm.sources.Reader(FileNames=path) Script Export script in view = sm.CreateRenderView() repr = sm.CreateRepresentation(reader, view) input deck. reader.UpdatePipeline() dataInfo = reader.GetDataInformation() pinfo = dataInfo.GetPointDataInformation() arrayInfo = pInfo.GetArrayInformation( "displacement9") Output Processed Data Polygonal Output Rendered Images Series Data with Field Data Live Visualization
Other Catalyst Export Options • Output to Cinema – Static vs Spherical Camera Θ φ
VTK-m A single place for the analysis and visualization community to collaborate, contribute, and leverage massively threaded algorithms
VTK-m Architecture Make it easier for simulation codes to take advantage these parallel visualization and analysis tasks on a wide range of current and next- generation hardware. Filters Post Processing DataModel Worklets In-Situ ExecuGon Arrays Data Parallel Algorithms
is not a replacement for Algorithm SimulaGon
is not a replacement for Algorithm Filter SimulaGon
Reader Filter Algorithm Filter SimulaGon Filter Rendering
Reader Filter Algorithm Filter SimulaGon Filter Rendering
VTK-m Use Case • Convert a traditional visualization pipeline into Catalyst Live GPU pipeline to provide computational monitoring of simulation results Live Per Timestep SimulaGon Format VisualizaGon Format
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Thank You! Checkout out: Robert Maynard Kitware @ www.kitware.com VTK @ www.vtk.org robert.maynard@kitware.com VTK-m @ m.vtk.org @robertjmaynard PyFR @ www.pyfr.org Other Presentations: S6633 - Navigating the In-Situ Visualization Landscape S6352 - Adapting the Visualization Toolkit for Many-Core Processors with the VTK-m Library S6329 - Petascale Computational Fluid Dynamics with Python on GPUs Please complete the Presenter Evaluation sent to you by email or through the GTC Mobile App. Your feedback is important!
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