Computer-Generated Force Acceleration using GPUs: Next Steps Dinesh Manocha UNC-Chapel Hill http://gamma.cs.unc.edu/

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

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

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

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

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

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

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)

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

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

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

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

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

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

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

The End