Current version: 1.5.0
FLAME GPU (Flexible Large-scale Agent Modelling Environment for Graphics Processing Units) is a high performance Graphics Processing Unit (GPU) extension to the FLAME framework.
It 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.
- 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.
- 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.
- Massive agent populations can be visualised in real time as agent data is already located on the GPU hardware.
The FLAME GPU documentation and user guide can be found at http://docs.flamegpu.com, with source hosted on GitHub at FLAMEGPU/docs.
Pre-compiled Windows binaries are available for the example projects in the FLAME-GPU-SDK, available as an archive for each release.
Source is available from GitHub, either as a zip download or via git:
git clone https://github.com/FLAMEGPU/FLAMEGPU.git
Or
git clone git@github.com:FLAMEGPU/FLAMEGPU.git
FLAME GPU can be built for Windows and Linux. MacOS should work, but is unsupported.
- CUDA 8.0 or later
- Compute Capability 2.0 or greater GPU (CUDA 8)
- Compute Capability 3.0 or greater GPU (CUDA 9)
- Windows
- Microsoft Visual Studio 2015 or later
- Visualisation:
freeglutandgleware included with FLAME GPU.
- Optional: make
- Linux
makeg++(which supports the cuda version used)xsltproc- Visualistion:
GL(deb:libgl1-mesa-dev, yum:mesa-libGL-devel)GLU(deb:libglu1-mesa-dev, yum:mesa-libGLU-devel)GLEW(deb:libglew-dev, yum:glew-devel)GLUT(deb:freeglut3-dev, yum:freeglut-devel)
- Optional:
xmllint
Visual Studio 2015 solutions are provided for the example FLAME GPU projects.
Release and Debug build configurations are provided, for both console mode and (optionally) visualisation mode.
Binary files are places in bin/x64/<OPT>_<MODE> where <OPT> is Release or Debug and <MODE> is Console or Visualisation.
An additional solution is provided in the examples directory, enabling batch building of all examples.
make can be used to build FLAME GPU simulations under linux and windows (via a windows implementation of make).
Makefiles are provided for each example project examples/project/Makefile), and for batch building all examples (examples/Makefile).
To build a console example in release mode:
cd examples/EmptyExample/
make console
Or for a visualisation example in release mode:
cd examples/EmptyExample/
make visualisation
Debug mode executables can be built by specifying debug=1 to make, i.e make console debug=1.
Binary files are places in bin/linux-x64/<OPT>_<MODE> where <OPT> is Release or Debug and <MODE> is Console or Visualisation.
For more information on building FLAME GPU via make, run make help in an example directory.
If you are using linux on a managed system (i.e you do not have root access to install packages) you can provide shared object files (.so) for the missing dependencies.
I.e. libglew and libglut.
Download the required shared object files specific to your system configuration, and place in the lib directory. This will be linked at compile time and the dynamic linker will check this directory at runtime.
Alternatively, to package FLAME GPU executables with a different file structure, the .so files can be placed adjacent to the executable file.
FLAME GPU can be executed as either a console application or as an interactive visualisation. Please see the documentation for further details.
# Console mode
usage: executable [-h] [--help] input_path num_iterations [cuda_device_id] [XML_output_override]
# Interactive visualisation
usage: executable [-h] [--help] input_path [cuda_device_id]
For further details, see the documentation or see executable --help.
Assuming the GameOfLife example has been compiled for visualisation, there are several options for running the example.
- Run the included batch script in
bin/x64/:GameOfLife_visualisation.bat - Run the executable directly with an initial states file
- Navigate to the
examples/GameOfLife/directory in a command prompt - Run
..\..\bin\x64\Release_Visualisation\GameOfLife.exe iterations\0.xml
- Navigate to the
Assuming the GameOfLife example has been compiled for visualisation, there are several options for running the example.
- Run the included bash script in
bin/linux-x64/:GameOfLife_visualisation.sh - Run the executable directly with an initial states file
- Navigate to the
examples/GameOfLife/directory - Run
../../bin/linux-x64/Release_Visualisation/GameOfLife iterations/0.xml
- Navigate to the
To report FLAME GPU bugs or request features, please file an issue directly using Github. If you wish to make any contributions, please issue a Pull Request on Github.
Please cite FLAME GPU using
@article{richmond2010high,
doi={10.1093/bib/bbp073},
title={High performance cellular level agent-based simulation with FLAME for the GPU},
author={Richmond, Paul and Walker, Dawn and Coakley, Simon and Romano, Daniela},
journal={Briefings in bioinformatics},
volume={11},
number={3},
pages={334--347},
year={2010},
publisher={Oxford Univ Press}
}
For an up to date list of publications related to FLAME GPU and it's use, visit the flamegpu.com website.
FLAME GPU is developed as an open-source project by the Visual Computing research group in the Department of Computer Science at the University of Sheffield. The primary author is Dr Paul Richmond.
FLAME GPU is copyright the University of Sheffield 2009 - 2018. Version 1.5.X is released under the MIT open source licence. Previous versions were released under a University of Sheffield End User licence agreement.
- Documentation now hosted on readthedocs, http://docs.flamegpu.com and https://github.com/flamegpu/docs
- Supports CUDA 8 and CUDA 9
- Removed SM20 and SM21 support from the default build settings (Deprecated / Removed by CUDA 8.0 / 9.0)
- Graph communication for agents with new example
- Updated Visual Studio version to 2015
- Improved linux support by upgraded Makefiles
- Additional example projects
Templateexample has been renamedEmptyExampletools/new_example.pyto quickly create a new example project.- Various bugfixes
- Adds step-functions
- Adds host-based agent creation for init and step functions
- Adds parallel reductions for use in init, step and exit functions
- Additional command line options
- Environmental variables can now be loaded from 0.xml
- Adds the use of
colouragent variable to control agent colour in the default visualisation - Additional controls for the default visualisation
- Macro definitions for default visualisation colours
- Macro definitions for message partitioning strategy
- Adds instrumentation for simple performance measurement via preprocessor macros
- Improved
functions.xsltoutput for generating template functions files. - Improved state model diagram generator
- Updated Circles Example
- Purged binaries form history, reducing repository size
- Updated Visual Studio Project files to 2013
- Improved Visual Studio build customisation
- Fixed double precision support within spatial partitioning
- Compile-time spatial partition configuration validation
- Added support for continuous agents reading discrete messages.
- Minor bug fixes and added missing media folder
- FLAME GPU 1.4 for CUDA 7 and Visual Studio 2012
##Problem reports
To report a bug in this documentation or in the software or propose an improvement, please use the FLAMEGPU GitHub issue tracker.