GPU-Accelerated Kinetic Wealth Exchange Models on Complex Networks
Project description
cuTradeNet
cuTradeNet is a library that provides classes to easily create & run kinetic wealth exchange models on complex networks.
Leads the user to set one (or ensemble) of complex networks as a contact structure agents use to trade about. The following wealth exchange models were implemented:
It is written in Python and uses Cuda module from Numba package to accelerate the simulation runnin in GPU, paralelizing some transaccions in the same graph and paralelizing runs in multiple graphs, leading to easier & faster averaging of system properties. It's completely abstracted from the CUDA knowledge for the user, so you can use it as a regular Python library.
How to use
There is a Demo notebook in the repository that can be tryed in it's Google Colab version too. There is also a General explanation of Kinetic Wealth Exchange Models used.
How to install
You can install it from PyPi with the following command:
pip install cuTradeNet
Repository&Questions
The repository is in GitHub, and you can ask questions or contact us in the Discussions section.
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