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Graphics: GPU Computation
Volumetric transparency with Per-Pixel Fragment Lists |
Practical Binary Surface and Solid Voxelization with Direct3D 11 |
Interactive Ray Tracing Using the Compute Shader in DirectX 11 |
Procedural Content Generation on GPU |
2D Distance Field Generation with the GPU |
Order-Independent Transparency Using Per-Pixel Linked Lists in DirectX 11 |
Simple and Fast Fluid Flow Simulation on the GPU |
A Fast Poisson Solver for OpenCL using Multigrid Methods |
Baking Normal Maps on the GPU |
Rendering Vector Art on the GPU |
Object Detection by Color: Using the GPU for Real-Time Video Image Processing |
Real-Time Rigid Body Simulation on GPUs |
Fast Virus Signature Matching on the GPU |
AES Encryption and Decryption on the GPU |
Efficient Random Number Generation and Application Using CUDA |
Imaging Earth's Subsurface Using CUDA |
Parallel Prefix Sum (Scan) with CUDA |
Incremental Computation of the Gaussian |
Using the Geometry Shader for Compact and Variable-Length GPU Feedback |
Animating Vegetation Using GPU Programs |
Interactive Fluid Dynamics and Rendering on the GPU |
Practical Cloth Simulation on Modern GPUs |
Shader Implementation of Discrete Wavelet Transform |
Real-Time Character Animation on the GPU |
Hardware-Based Ambient Occlusion |
Real-Time Caustics by GPU |
Implementing Ray Tracing on the GPU |
GPU-Powered Pathfinding Using Preprocessed Navigation Mesh Approach |
GPU Computation in Projective Space Using Homogeneous Coordinates |
Preprocessed Pathfinding Using the GPU |
Abstract: This article proposes GPU-based implementations for two popular algorithms used to solve the all-pairs shortest paths problem: Dijkstra's algorithm, and the Floyd-Warshall algorithm. These algorithms are used to preprocess navigation mesh data for fast pathfinding. This approach can offload pathfinding-related CPU computations to the GPU at the expense of latency. However, once the solution table is generated, this approach minimizes the latency time for a specific path search, thus giving the game a better sense of interactivity. The biggest benefit of this approach is gained in systems with multiple agents simultaneously requesting paths in the same search space. Although the article describes a GPU-specific implementation for a navigation mesh, any other multi-processor environment or discrete search space representation can be used.
Streaming Architectures and Technology Trends |
The GeForce 6 Series GPU Architecture |
Mapping Computational Concepts to GPUs |
GPU Computation Strategies and Tips |
Implementing Efficient Parallel Data Structures on GPUs |
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Aaron Lefohn (University of California, Davis), Joe Kniss (University of Utah), John Owens (University of California, Davis)
GPU Gems 2, 2005. |
Stream Reduction Operations for GPGPU Applications |
Computer Vision on the GPU |
GPU Computing for Protein Structure Prediction |
A GPU Framework for Solving Systems of Linear Equations |
Options Pricing on the GPU |
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Peter Kipfer and R�diger Westermann (Technische Universit�t M�nchen)
GPU Gems 2, 2005. |
GPGPU: General-Purpose Computation on GPUs |
A Toolkit for Computation on GPUs |
Fast Fluid Dynamics Simulation on the GPU |
Artificial Neural Networks on Programmable Hardware |
Accessing and Modifying Topology on the GPU |
Massively Parallel Particle Systems on the GPU |
Tactical Path-Finding Using Stochastic Maps on the GPU |
Linear Algebra on the GPU |
Real-Time Simulation and Rendering of Particle Flows |
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