Lua—an extensible extension language
Software—Practice & Experience
ArtDefo: accurate real time deformable objects
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
Image-Based Techniques in a Hybrid Collision Detector
IEEE Transactions on Visualization and Computer Graphics
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Game Physics
UberFlow: a GPU-based particle engine
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Hardware-based simulation and collision detection for large particle systems
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
LU-GPU: Efficient Algorithms for Solving Dense Linear Systems on Graphics Hardware
SC '05 Proceedings of the 2005 ACM/IEEE conference on Supercomputing
SIGGRAPH '05 ACM SIGGRAPH 2005 Sketches
Parallel processing between GPU and CPU: Concepts in a game architecture
CGIV '07 Proceedings of the Computer Graphics, Imaging and Visualisation
Cache simulator based on GPU acceleration
Proceedings of the 2nd International Conference on Simulation Tools and Techniques
GCSim: A GPU-Based Trace-Driven Simulator for Multi-level Cache
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
An adaptative game loop architecture with automatic distribution of tasks between CPU and GPU
Computers in Entertainment (CIE) - SPECIAL ISSUE: Games
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This article concerns the use of a graphics processor unit (GPU) as a math co-processor in real-time applications in special games and physics simulations. To validate this approach, we present a new game loop architecture that employs GPUs for general-purpose computations (GPGPUs). A critical issue here is the process distribution between the CPU and the GPU. The architecture consists of a model for distribution, and our implementation offers many advantages in comparison to other approaches without the GPGPU stage. This architecture can be used either by a general-purpose language such as the Compute Unified Device Architecture (CUDA), or shader languages such as the High-Level Shader Language (HLSL) and the OpenGL Shading Language (GLSL). Although the architecture proposed here aims at supporting mathematics and physics on the GPU, it is possible to adapt any kind of generic computation. This article discusses the model implementation in an open-source game engine and presents the results of using this platform.