index.md

title	layout	header	excerpt	feature_row
	splash	overlay_particles actions true label url Download /download/ label url GitHub https://github.com/FLAMEGPU/FLAMEGPU2/ label url Docs https://docs.flamegpu.com label url Try on Colab https://colab.research.google.com/github/FLAMEGPU/FLAMEGPU2-tutorial-python/blob/google-colab/FLAME_GPU_2_python_tutorial.ipynb	The Flexible Large-scale Agent Modelling Environment for the Graphics Processing Unit (GPU)	image_path alt title excerpt url btn_class btn_label /assets/images/gpu_birds_square.png About FLAME GPU About FLAME GPU Find out about FLAME GPU 2 and its new features including a recorded presentation and links to publications and citations. /about/ btn--primary Learn more image_path alt title excerpt url btn_class btn_label /assets/images/models_square.png FLAME GPU models Explore the examples Browse the model database for example models which highlight key features or demonstrate performance. Download them and try them for yourself. /models/ btn--primary Learn more image_path alt title excerpt url btn_class btn_label /assets/images/github_square.png Github Explore the Source Go to GitHub to browse the source code of the simulator, docs and this website. https://github.com/FLAMEGPU/ btn--primary Goto GitHub

![FLAME GPU 2 logo]({{ "/assets/images/fgpu2_icon_256.png" | relative_url }}){: .align-right}

FLAME GPU is a GPU accelerated simulator for domain independent complex systems simulations. Version 2 brings a complete re-write of the existing library offering greater flexibility, an improved interface for agent scripting, CUDA C++ & python3 interfaces and better research software engineering.

FLAME GPU provides a mapping between a formal agent specifications with C based scripting and optimised CUDA code. This includes a number of key ABM building blocks such as multiple agent types, agent communication and birth and death allocation.

The advantages of our contribution are three fold.

• Firstly Agent Based (AB) modellers are able to focus on specifying agent behaviour and run simulations without explicit understanding of CUDA programming or GPU optimisation strategies.

• Secondly simulation performance is significantly increased in comparison with desktop CPU alternatives. This allows simulation of far larger model sizes with high performance at a fraction of the cost of grid based alternatives.

• Finally massive agent populations can be visualised in real time as agent data is already located on the GPU hardware.

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