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Marcus D. Hanwell
Robert Maynard
GTC, San Jose, CA
March, 2015
1
Visualization Toolkit:
Faster, Better, Open Scientific
Rendering and Compute
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Accelerating Visualization with Partnerships
• NVIDIA and Kitware collaborate to bring
advances in scientific visualization
• Collaboration focuses
– In-site visualization
– Advanced rendering
• Improved use of NVIDIA GPUs
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Kitware, Inc. • Founded in 1998 by five former GE Research employees
• 98 current employees; 34 with PhDs
• Privately held, profitable from creation, no debt
• Offices
– Clifton Park, NY
– Carrboro, NC
– Santa Fe, NM
– Lyon, France
• 2011 Small Business
Administration’s Tibbetts
Award
• HPCWire Readers and
Editor’s Choice
• Inc’s 5000 List since
2008
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Kitware’s customers & collaborators Over 75 academic institutions including… • Harvard
• Massachusetts Institute of Technology
• University of California, Berkeley
• Stanford University
• California Institute of Technology
• Imperial College London
• Johns Hopkins University
• Cornell University
• Columbia University
• Robarts Research Institute
• University of Pennsylvania
• Rensselaer Polytechnic Institute
• University of Utah
• University of North Carolina
Over 50 government agencies and labs including… • National Institutes of Health (NIH)
• National Science Foundation (NSF)
• National Library of Medicine (NLM)
• Department of Defense (DOD)
• Department of Energy (DOE)
• Defense Advanced Research Projects Agency (DARPA)
• Army Research Lab (ARL)
• Air Force Research Lab (AFRL)
• Sandia (SNL)
• Los Alamos National Labs (LANL)
• Argonne (ANL)
• Oak Ridge (ORNL)
• Lawrence Livermore (LLNL)
Over 100 commercial companies in fields including… • Automotive
• Aircraft
• Defense
• Energy technology
• Environmental sciences
• Finance
• Industrial inspection
• Oil & gas
• Pharmaceuticals
• Publishing
• 3D Mapping
• Medical devices
• Security
• Simulation
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Kitware: Core Technologies
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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
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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 7
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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
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Going From Data to Visualization
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VTK Visualizations
10
HPC Visualization
Large Displays and Virtual Reality
Mobile Visualization
Interactive Medical Application and
Visualization
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VTK Architecture
• Hybrid approach
– Compiled C++ core (faster algorithms)
– Interpreted applications (rapid development)
– Interpreted layer generated automatically
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C++
core
Interpreter
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The Visualization Pipeline
• A sequence of algorithms that operate on
data objects to generate geometry
12
Source
Data
Data
Filter
Filter
Data
Data
Mapper
Mapper Actor
Actor
Render on
screen
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VTK Organization
• Libraries with public APIs
• Cross-platform, open-source, for reuse
• Implementation modules use factories
– Rendering API uses OpenGL backend
– Core rendering does not link to/use OpenGL
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Basic Library Hierarchy
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vtkCommonCore
vtkRenderingCore
OpenGL OpenGL2
vtkFreeType
OpenGL OpenGL2
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Legacy Rendering
• Based on OpenGL 1.1 APIs
– Optionally uses some extensions
• Heavy use of display lists for interaction
• A “Painter” API to enable custom rendering
– Virtual functions, switches, …
• In tight loops for all vertices, normals, colors, etc
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Polygonal Rendering Rewrite
• New minimum OpenGL version
– OpenGL 2.1, OpenGL ES 2.0
• Rewrite to use minimal common subset
• Major overhaul of the rendering code
– Use VBOs, VAOs, shaders, “new” OpenGL
• Retain same high level API
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Volume Rendering Rewrite
• Improve portability of GPU code
– Works well on Linux, Mac, and Windows
– Uses less extensions, more core GL 2.1+
• Refactored to compute more in shaders
• Replicates important features
• Easier to develop new techniques
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Removing Old Calls
• Not using matrix stacks
• GLSL, using modern approaches
• Optional extensions detected at runtime
• Not a single glVertex call, highly batched
• Some data structures need further work
– vtkPolyData needs packed triangles
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Performance Improvements
• In many cases now GPU bound
– Previously large systems CPU bound
• Large polygonal models >100x faster!
• Much more portable depth peeling
• Reduced memory footprint significantly
• Initial render times reduced
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Performance: Old vs New
• Looking at static scenes
– Time to first render
– Average time of rotated subsequent renders
• Legacy rendering hits maximum size
– Memory errors/limits
– Only possible to compare smaller geometries
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Benchmarking Tools (Polygonal)
• Added some new benchmarking tools
• Aim to provide systematic comparison
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Time For First Frame (K6000)
0
2
4
6
8
10
12
14
16
1 million 5 million 20 million 30 million
Tim
e (
s)
Triangles
Legacy
Rewrite
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Time for Subsequent Frames (K6000)
0
0.5
1
1.5
2
2.5
3
3.5
1 million 5 million 20 million 30 million
Tim
e (
s)
Triangles
Legacy
Rewrite
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Rendering Speeds
• Two orders of magnitude faster!
• Legacy rendering maxes out at 30 million
– Not possible to compare above this
• Measured on a modern Linux system
– Same on Windows, and Mac
• Memory footprint about half for triangles
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Comparison of Cards (Rewrite)
25
0
0.5
1
1.5
2
2.5
3
3.5
1 2 3 5 10 20 30 50 100 200
Tria
ngl
es
pe
r Se
con
d (
B)
Number of Triangles (M)
K2200
K5200
K6000
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Benchmarking Tools (Volume)
• Uses same framework as polygonal
• Volumes of increasing size
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Time For First Frame (K40c)
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0
5
10
15
20
25
10 million 50 million 100 million 500 million 1000 million
Tim
e (
s)
Voxels
Legacy
Rewrite
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Time for Subsequent Frames (K40c)
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0
0.002
0.004
0.006
0.008
0.01
0.012
0.014
10 million 50 million 100 million 500 million 1000 million
Tim
e (
s)
Voxels
Legacy
Rewrite
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Mobile/Embedded
• New rendering can target ES 2.0+
• Some testing on Android and iOS
• Largely shared code with desktop code
• Simple multitouch interaction support
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Custom Rendering
• Shaders can be overridden in mappers
• VBOs/IBOs created by reusable helpers
• Override the vtkMapper class
• Several examples of different rendering
– Glyphing, impostors, composite data
– Offer a reasonable starting point
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Porting/Using New Rendering
• Many applications just change backend – VTK_RENDERING_BACKEND=OpenGL2
– Compile time option, with possible link change
– vtkRenderingOpenGL ->
vtkRendering${VTK_RENDERING_BACKEND}
• Custom OpenGL will need to be ported
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VTK-m Project Goals
• A single place for the visualization community to
collaborate, contribute, and leverage massively
threaded algorithms.
• Reduce the challenges of writing highly
concurrent algorithms by using data parallel
algorithms
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VTK-m Project Goals
• 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.
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VTK-m Architecture
In-Situ
Execution Data Parallel Algorithms Arrays
Post Processing
Worklets
DataModel
Filters
• Combines strengths of multiple projects: – EAVL, Oak Ridge National Laboratory
– DAX, Sandia National Laboratory
– PISTON, Los Alamos National Laboratory
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VTK-m Arbitrary Composition
• VTK-m allows clients to access different memory layouts through the Array Handle and Dynamic Array Handle.
–Allows for efficient in-situ integration
–Allows for reduced data transfer
Control Environment Execution Environment Transfer
Control Environment Execution Environment
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VTK-m Arbitrary Composition
Point Arrangement
Cells Coordinates Explicit Logical Implicit
Structured
Strided Separated
Unstructured
Strided Separated
VTK-m
Data Set
• VTK-m allows clients to construct data sets from cell and point arrangements that exactly match their original data
–In effect, this allows for hybrid and novel mesh types
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functor()
Functor Mapping Applied to Topologies
[Baker, et al. 2010]
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functor()
Functor Mapping Applied to Topologies
[Baker, et al. 2010]
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What We Have So Far
• Features
– Core Types
– Statically and Dynamically Typed Arrays
– Device Interface (Serial, Cuda, TBB under
development)
– Basic Worklet and Dispatcher
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What We Have So Far
• Compiles with – gcc (4.8+), clang, msvc (2010+), icc, and pgi
• User Guide
• Ready for larger collaboration
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2 x Intel Xeon CPU E5-2620 v3 @ 2.40GHz + NVIDIA Tesla K40c
Data: 1024^3 (floats)
17.28
30.2
1.514
0.524
0 5 10 15 20 25 30 35
Marching Cubes
VTK-m Cuda [No Transfer] VTK-m Cuda VTK-m Serial VTK Serial
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2 x Intel Xeon CPU E5-2620 v3 @ 2.40GHz + NVIDIA Tesla K40c
Data: 1024^3 (floats)
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
256^3 512^3 756^3 1024^3
Tim
e (
s)
Triangles
No Transfer
Transfer
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Future Directions
• Make custom rendering easier
• Improved support for mobile
• Improved support for multitouch
• Extend approaches to the web
• Optionally use new features (OpenGL 4.4)
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Coprocessing/In-situ
• Use of VTK and VTK-m
– Process data in place using VTK-m
– Visualize and analyze using VTK
• Bringing highly parallelized visualization
and analytics in science to all
• Create bridges between VTK and VTK-m
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Thank You!
Marcus D. Hanwell
• @mhanwell
• +MarcusHanwell
Robert Maynard
• @robertjmaynard
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