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GTC ON-DEMAND

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Abstract:

Learn how to simulate transportation systems and crowds for smart city applications at massive scale. This talk will give insights into novel algorithms and techniques which are being applied to: 1) National (entire UK) scale road network flow simulations, 2) City sized simulations of intelligent individually modelled vehicles, and 3) Integrated simulations of national infrastructure with Pedestrian crowds, vehicles and rail. Examples of techniques include low-density high-diameter graph traversal, multi agent simulation and virtual reality interaction using the OmniDeck treadmill and the Oculus Rift.

Learn how to simulate transportation systems and crowds for smart city applications at massive scale. This talk will give insights into novel algorithms and techniques which are being applied to: 1) National (entire UK) scale road network flow simulations, 2) City sized simulations of intelligent individually modelled vehicles, and 3) Integrated simulations of national infrastructure with Pedestrian crowds, vehicles and rail. Examples of techniques include low-density high-diameter graph traversal, multi agent simulation and virtual reality interaction using the OmniDeck treadmill and the Oculus Rift.

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Topics:
Intelligent Video Analytics, Autonomous Vehicles
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8223
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Abstract:
Complex systems are prevalent throughout various levels of biology from the molecular and cellular scales through to populations of interacting individuals. This talk discusses how formal state based representation of agents within a complex system can be simulated and visualized at large scales using the open source FLAME GPU framework. Methods of code generation from XML documents and use of CUDA streams for heterogeneous state execution are presented. Examples include cellular tissue modelling and large scale crowd dynamics.
Complex systems are prevalent throughout various levels of biology from the molecular and cellular scales through to populations of interacting individuals. This talk discusses how formal state based representation of agents within a complex system can be simulated and visualized at large scales using the open source FLAME GPU framework. Methods of code generation from XML documents and use of CUDA streams for heterogeneous state execution are presented. Examples include cellular tissue modelling and large scale crowd dynamics.  Back
 
Topics:
Big Data Analytics, Tools & Libraries, Life & Material Science
Type:
Talk
Event:
GTC Silicon Valley
Year:
2015
Session ID:
S5133
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Abstract:
The CUDA Runtime Variable Environment (cuRVE) is a simple library which provides CUDA global memory management through the registration and retrieval of variables via constant string variable names. Optimisations include compile time hashing and efficient hash collision avoidance. By using cuRVE, memory management and kernel launching can be simplified and kernel and host code can be compiled without any explicit dependencies. The cuRVE library will be integrated into the next version of FLAME GPU to improve the clarity and performance of user defined agent functions.
The CUDA Runtime Variable Environment (cuRVE) is a simple library which provides CUDA global memory management through the registration and retrieval of variables via constant string variable names. Optimisations include compile time hashing and efficient hash collision avoidance. By using cuRVE, memory management and kernel launching can be simplified and kernel and host code can be compiled without any explicit dependencies. The cuRVE library will be integrated into the next version of FLAME GPU to improve the clarity and performance of user defined agent functions.  Back
 
Topics:
Programming Languages
Type:
Poster
Event:
GTC Silicon Valley
Year:
2014
Session ID:
P4226
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Speakers:
Paul Richmond
- University of Sheffield
Abstract:
The Flexibile Large-scale Agent Modelling Environment for the GPU (FLAME GPU) addresses the performance and architecture limitations of previous work by presenting a flexible framework approach to ABM on the GPU. Most importantly it addresses the issue of agent heterogeneity through the use of state machine based agent representation. This representation allows agents to be separated into associated state lists which are processed in batches to allow very diverse population of agents whilst avoiding large divergence in parallel code kernels. The use of the GPU allows AB models to be visualised in real time, which further widens the application of ABM to real-time simulations.
The Flexibile Large-scale Agent Modelling Environment for the GPU (FLAME GPU) addresses the performance and architecture limitations of previous work by presenting a flexible framework approach to ABM on the GPU. Most importantly it addresses the issue of agent heterogeneity through the use of state machine based agent representation. This representation allows agents to be separated into associated state lists which are processed in batches to allow very diverse population of agents whilst avoiding large divergence in parallel code kernels. The use of the GPU allows AB models to be visualised in real time, which further widens the application of ABM to real-time simulations.  Back
 
Topics:
HPC and AI
Type:
Poster
Event:
GTC Silicon Valley
Year:
2010
Session ID:
P10I04
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