GPU-Based Visualization
for Flight Simulation
Tim Woodard
Director of Research and Development
Diamond Visionics
www.dvcsim.com
GPU Technology Conference 2014
Flight Simulation Takes Off
Flight Simulation Takes Off
 Link’s Blue Box, 1943
Flight Simulation Takes Off
 Link’s Blue Box, 1943
 Terrain model-board and
film, 50’s and 60’s
Flight Simulation Takes Off
 Link’s Blue Box, 1943
 Terrain model-board and
film, 50’s and 60’s
 “Image Generators”, 70’s
to present
“Big Iron”
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McDonnell Douglas VITAL
Evans and Sutherland ESIG
GE Compu-scene
Link DIG
SGI Onyx
Improved rapidly
“Big Iron”
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McDonnell Douglas VITAL
Evans and Sutherland ESIG
GE Compu-scene
Link DIG
SGI Onyx
Improved rapidly, but
costly…
Rise of the PC
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McDonnell Douglas (VITAL)
Evans and Sutherland (ESIG)
GE (Compu-scene)
Link (DIG)
Improved rapidly, but VERY costly…
1999, NVIDIA GeForce 256
Rise (and fall?) of the PC
 Moving to the PC-based GPU brought huge cost savings
Rise (and fall?) of the PC
 Moving to the PC-based GPU brought huge cost savings
 But meant giving up a lot of capability and control
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Real-time determinism
Anti-aliasing
Gen-lock
Unified memory
Computational power
Flight Simulation vs. Gaming
 Instructor-controlled conditions (time, weather, etc.)
 Subjective tuning
 Fidelity
No aliasing
No Z-fighting
No LOD popping
 Performance
Never drop frames
 20+ channels
 Data…
Flight Simulation vs. Gaming
 Large gaming areas – “database”
(E.g. continental, even planetary)
 Legacy database standards
(Government-defined)
 Typical gaming approaches do not
scale to massive datasets... (nor do
traditional simulation approaches)
Throw Hardware At It!
 Moore’s Law continues…
 …Via parallelism, not speed
(for nearly 10 years now)
 First with CPU, now with
GPU
Not So Fast
 Free lunch is over…
 Software must be architected for
parallelism: Amdahl’s Law
𝑆=
1
𝑃
(1 − 𝑃) +
𝑛
S = speed up
P = % of parallelization
n = number of threads
The Long Pole
Prepare
Source Data
Compile
Database
Re-work
Intermediate Database
(LODs)
Load/Render
Database
Compute ALL the LODs!
 Pre-compute LODs for all possible
paths into “polygon soup”
 Very little of the result is typically
used
Compute ALL the LODs!
 Pre-compute LODs for all possible
paths into “polygon soup”
 Very little of the result is typically
used
 Uses tremendous computing
resources
 Uses tremendous amount of
storage space
Stuck in Time
 Static databases do not adapt to new data or GPU technology
Stuck in Time
 Static databases do not adapt to new data or GPU technology
Stuck in Time
 Static databases do not adapt to new data or GPU technology
Underutilizes GPU
Leveraging the GPU
 Large/fast memory buffers
 Programmability…
 Vertex, fragment, geometry
 Compute
Run-time Scene Generation
Prepare
Source Data
Source Data
User-Defined
Processing Rules/
Shaders
Load/Render
Source Data on
GPU
Deferred Commitment
Case Study: Hardware Consolidation
 One 4K projector from one GPU
 Multiple GPUs per system
Case Study: Lights
 ~50 tunable
parameters
 100’s of thousands
of lights possible at
60Hz
Case Study: Grass
 10’s of thousands
of grass blades
 Matches imagery
color
 Local wind model
(helo rotor wash)
Summary
 Visualization in flight simulation continues to evolve rapidly.
 GPU advancements have fundamentally changed how we do
things and what is possible:
 Improved fidelity
 Improved performance
 Deferred commitment, instant feedback
Questions?
Tim Woodard
timw@dvcsim.com
@TimAtDVC #GTC14
Thank you!