Region-Scale Evacuation Modeling using GPUs
Towards Highly Interactive, GPU-based Evaluation of Evacuation Transport Scenarios at State-Scale
Kalyan S. Perumalla, PhD Brandon G. Aaby Srikanth B. Yoginath Sudip K. Seal, PhD
National Evacuation ConferenceNew Orleans, LA
Feb 5, 2010
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RealSim Program at ORNL
Real-Time Data Driven Simulation System• Kalyan Perumalla, Ph.D. (PI)
• Brian Worley, Ph.D. (Director)
• Vladimir Protopopescu, Ph.D.
• Srikanth Yoginath
• Sudip Seal, Ph.D.
• Brandon Aaby
• Clayton Thurmer
• Other collaborators
3 Managed by UT-Battellefor the U.S. Department of Energy National Evacuation Conference 2010
Evacuation Analysis: New
Capabilities
Real-time or Faster Speed
Accuracy and
ComplexityState-or Regional
Spatial Scale
Our FocusNext Generation Computational Capability
Millions of Grid Cells
GPUs
Field-based Mobility Modeling
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Affordable, off-the-shelf
Desktop-based
GPU: Graphical Processing Units
Example: NVIDIA
• Original GeForce 6000, 7000, 8000, and 9000 series
• Latest GTX 295
• Upcoming GTX 300
CPUCPU
GPUGPU DisplayDisplayMemoryMemory
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Graphics as Computation
Programmable graphics primitives
E.g., pixel shading such as bump mapping and patterned texture mapping
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A Few other Popular Instantiations
Commercial Offerings
• IBM Cell Processor
• NVIDIA GeForce, GTX
• NVIDIA Tesla
Supercomputing Scale
• LANL RoadRunner
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Vertical Grid ElementsIncoming Outgoing
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Horizontal Grid Elements
Incoming Outgoing
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FP
FP
FPFP
Texture Memory
FP=Fragment Processor
Typical Single PhaseExecution on GPU
FP
FP
FP
FP
Texture Memory
FP=Fragment Processor
FP
FP
FPFP
Texture Memory
FP=Fragment Processor
FP
FP
FPFP
Texture Memory
FP=Fragment Processor
FPFPFPFP
Texture Memory
FP=Fragment Processor
FP
FP
FP
FP
Texture Memory
FP=Fragment Processor
v vv
GARFIELD Configurable Display with
User Interaction
Our Multi-phase Execution Framework for Model Flexibility
FP
FP
FPFP
Texture Memory
FP=Fragment Processor
v
Field Model Execution Framework on GPU
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GPU Data Structures and Execution
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System Functional Architecture
Induced Graph Field Generator
Road NetworkDatabase (E.g.,
Tiger)
Field GridResolution (GPUconfiguration)
Texel GridGenerator
DestinationLocations
Sources andInitial LoadingDistributions
InteractiveSpecifications
GPU-basedExecution Engine
QueueingModel(Code)
RuntimeDisplay andVisualizatio
n
CongestionModel(Code)
CustomizedEncoding,
Coloring andAnimation (Code)
GeographicalRegions of
Interest (Latitude,Longitude)
Map ImagesServer orDatabase
Statistics and Reports
1 1
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Louisiana
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Lousiana (continued)
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Texas
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Texas (continued)
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Example Timing Results
Texture Evac Time
Run Time
State Nodes Links X×X Hours Sec
DC 9,559 14,884 1048576 35.20 54.90
LA 413,574 988,458 4194304 65.07 409.59
TN 583,484 1,335,586 3211264 157.91 353.89
FL 1,048,506 2,629,268 4194304 179.20 611.83
TX 2,073,870 5,116,492 3211264 217.60 777.65
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New Capabilities
First multi-million node/link road network simulation
First GPU-based execution of road network simulation
First field-based mobility formulation
For additional information
and animations,
visit our
RealSim webpage at
www.ornl.gov/~2ip