superneuroabm 1.0.1

A GPU-based multi-agent simulation framework for neuromorphic computing.

SuperNeuroABM

SuperNeuroABM is a GPU-based multi-agent simulation framework for neuromorphic computing. Built on top of SAGESim, it enables fast and scalable simulation of spiking neural networks on both NVIDIA and AMD GPUs.

Key Features

• GPU Acceleration: Leverages CUDA (NVIDIA) or ROCm (AMD) for high-performance simulation
• Scalable: From single GPU to multi-GPU HPC clusters via MPI
• Flexible Neuron Models: Support for various soma and synapse step functions
• STDP Support: Built-in spike-timing-dependent plasticity mechanisms
• Network I/O: Import/export neural network topologies

Requirements

• Python 3.11+
• NVIDIA GPU with CUDA drivers or AMD GPU with ROCm 5.7.1+
• MPI implementation (OpenMPI, MPICH, etc.) for multi-GPU execution

Installation

Your system might require specific steps to install mpi4py and/or cupy depending on your hardware. In that case, use your system's recommended instructions to install these dependencies first.

pip install superneuroabm

Quick Start

from superneuroabm.model import SuperNeuroModel

# Create model
model = SuperNeuroModel()

# Create neurons
n1 = model.create_neuron()
n2 = model.create_neuron()

# Connect with synapse
model.create_synapse(n1, n2, weight=1.0)

# Setup and run
model.setup(use_gpu=True)
model.simulate(ticks=100)

Unit Tests

To run unit tests:

python -m unittest tests.test_synapse_and_soma_models

Publications

Date, Prasanna, Chathika Gunaratne, Shruti R. Kulkarni, Robert Patton, Mark Coletti, and Thomas Potok. "SuperNeuro: A fast and scalable simulator for neuromorphic computing." In Proceedings of the 2023 International Conference on Neuromorphic Systems, pp. 1-4. 2023.

License

BSD-3-Clause License - Oak Ridge National Laboratory