Linear algebra operators for GPU implementation of numerical algorithms
ACM SIGGRAPH 2003 Papers
Physically-based visual simulation on graphics hardware
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
Relational joins on graphics processors
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient Execution on GPUs of Field-Based Vehicular Mobility Models
Proceedings of the 22nd Workshop on Principles of Advanced and Distributed Simulation
Data parallel execution challenges and runtime performance of agent simulations on GPUs
Proceedings of the 2008 Spring simulation multiconference
A performance study of general-purpose applications on graphics processors using CUDA
Journal of Parallel and Distributed Computing
Molecular dynamics simulations on commodity GPUs with CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
Accelerating large graph algorithms on the GPU using CUDA
HiPC'07 Proceedings of the 14th international conference on High performance computing
Population parallel GP on the G80 GPU
EuroGP'08 Proceedings of the 11th European conference on Genetic programming
Particle-based fluid simulation on the GPU
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
| Hi-index | 0.00 |
Today's graphics processing units (GPU) have tremendous resources when it comes to raw computing power. The simulation of large groups of agents in transport simulation has a huge demand of computation time. Therefore it seems reasonable to try to harvest this computing power for traffic simulation. Unfortunately simulating a network of traffic is inherently connected with random memory access. This is not a domain that the SIMD (single instruction, multiple data) architecture of GPUs is known to work well with. In this paper the authors will try to achieve a speedup by computing multi-agent traffic simulations on the graphics device using NVIDIAs CUDA framework.