1. Computer-Generated Force Acceleration using GPUs: Next Steps
Dinesh Manocha
UNC-Chapel Hill

2. GPU Performance
Rasterization performance will continue to grow at a rate faster than Moore’s law
Improved precision and programmability
Better GPU architecture: improved performance of texture cache, blending operations, occlusion queries etc.
PCs with multiple GPUs

3. OOS: Performance issues
Complex urban environments
Large number of entities: 50K to 2M
Environment complexity: 27GB database; 300K buildings
Need 2 orders of magnitude improvement to handle LOS, collision handling and route planning
Higher-resolution physical models
Shadows in urban environments
Acoustics in urban environments
Simulation of building damage and collapsed structures
Atmospheric and force model simulations
GPUs can be used to accelerate all these computations

4. Next Goal: Larger Terrain Environments
Current:
GPU-based algorithms has limited capabilities to handle large terrain environments
Propose:
GPU functionality to be able to handle
Databases with millions of buildings
Distance between buildings in meters
Buildings with unique full interiors and furniture

5. Line of Sight Computations
Application to complex urban environments
Region-based visibility for complex 3D environments
Handle UHRBs
Dynamic terrains
Integrate into OneSAF

6. Path/Route Planning Route planning in urban environments
Route planning in ultra-hi-resolution buildings
Generalization to 3D routes

7. GPU Clusters Why multiple GPUs?
scale computation rate: interactive performance
scale problem size: complex terrain; high entity count
retain price/performance advantages for complex applications

8. GPU cluster Target system Node System
CPU: single or dual core AMD or P4
GPU: single or dual PCI-e 16x GPU cards
host channel adapter (HA): PCI-e 8x single or dual port IB
System
8-32 nodes with Infiniband switch
GPU HA
CPU
PCI - e IB
Infiniband switch
Narrative:
We have not started deploying a GPU cluster yet because (a) components are rapidly improving and (b) no funding
However we are tracking the technology carefully and plan to construct a system … (when)
Here is our current plan for the GPU cluster. These are currently or soon to be commodity components. Possible things worth waiting for:
new nVidia GPU (whatever is coming up)
dual core CPUs (probably not the key driver of performance)
30 Gb/s Infiniband links (current 10 Gb/s or 20 Gb/s using dual port HCA)

9. GPU Cluster Architecture
Commodity CPU/GPU cluster
GPU IB
CPU
PCI - e
Infiniband
Router

10. GPU – GPU communication: How?
Cutting out the middleman
Remote direct memory access (RDMA)
MPI for GPU cluster built on GPU RDMA
Infiniband Router IB IB IB IB IB IB
all steps use cut-through routing. No GPU involvement on either end.
GPU
GPU
GPU HA
GPU
GPU HA
GPU
GPU
GPU HA
PCI
PCI
PCI - - - e e e
PCI
PCI - - e e
PCI
PCI
PCI - - - e e e
CPU
Mem
CPU
Mem
CPU
Mem

11. Programming clusters Cluster programming models
C/C++/Fortran + MPI
UPC + Co-Array Fortran
Programming GPU clusters
GPU programming language
OpenGL, cg, Brook
Mated to cluster programming model
MPI, UPC
Programmability
many ways to lose performance
very limited tool support

12. Parallel Algorithms: Issues
Distribute the complex environment on multiple systems
Perform LOS queries in parallel on different systems

13. Handling Complex Datasets
Out of memory management
Disk to main memory
Main memory to GPU memory
Interactive display
Collision detection
Path planning and navigation

14. GPU-based algorithms and computations can have fundamental impact in:
Conclusions
GPU-based algorithms and computations can have fundamental impact in:
Simulations
Computer generated forces
Mission planning
Databases and data streaming
Scientific computation

15. Conclusions
GPU-based algorithms and computations are having fundamental impact in:
Simulations
Computer generated forces
Mission planning
Databases and data streaming
Scientific computation

16. The End