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: LIF, adaptive-threshold LIF, higher-order LIF, and Izhikevich somas; single-exponential and weighted synapses
• STDP Support: Built-in pair-wise, bounded, quantized and memristive spike-timing-dependent plasticity, plus user-registered learning rules
• Train/Eval Switch: model.train() / model.eval() and set_learning_enabled() toggle plasticity globally or per synapse
• Named Parameters: get_hyperparameters() / set_hyperparameters() (and their learning counterparts) address parameters by name rather than by position
• Bulk and Distributed Construction: build a whole network in one call with create_from_lists(), or have each MPI rank build only its own partition with load_post_owned() / load_from_adjacency() — no global graph is ever materialized
• Network Generation: Brunel balanced random networks via brunel_partition() with topology="global" | "bounded" | "torus2d" | "torus3d", plus a spatially embedded economical small-world variant in spatial_smallworld_partition() (superneuroabm/brunel.py)

Requirements

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

Validated stack: ROCm 7.2.0 with CuPy 14.0.1 on AMD MI250X (see SAGESim's docs/frontier_setup_rocm720_cupy1401.md).

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

This pulls in sagesim==0.7.0.

Quick Start

from superneuroabm.model import NeuromorphicModel

model = NeuromorphicModel()

# Two LIF somas
pre = model.create_soma(breed="lif_soma", config_name="config_0")
post = model.create_soma(breed="lif_soma", config_name="config_0")

# External drive into `pre` (pre_soma_id=-1 means "external input"), and pre -> post
drive = model.create_synapse(
    breed="single_exp_synapse", pre_soma_id=-1, post_soma_id=pre, config_name="config_0"
)
model.create_synapse(
    breed="single_exp_synapse", pre_soma_id=pre, post_soma_id=post, config_name="config_0"
)

# Compile step functions and allocate GPU buffers
model.setup()

for tick in (2, 20, 40):
    model.add_spike(synapse_id=drive, tick=tick, value=1)

model.simulate(ticks=200)

print("pre  spikes:", model.get_spike_times(soma_id=pre))
print("post spikes:", model.get_spike_times(soma_id=post))

Breed and config names come from superneuroabm/component_base_config.yaml; pass your own YAML with NeuromorphicModel(user_config=...) to override or add parameter sets.

Tutorials

Tutorial 01 brings its own components — tutorials/user_customized_lif.py and tutorials/user_customized_stdp.py — registered on the model at runtime, with no changes to the installed package. See docs/CUSTOM_COMPONENTS.md for the full pattern.

Runnable scripts live in examples/, including a Brunel network generator and a Masquelier 2008 STDP replication.

Unit Tests

python -m pytest tests/

The MPI consistency test compares multi-rank spike times against the single-rank baseline and needs more than one GPU:

srun -A <account> -q debug -N1 -n2 -c7 --gpu-bind=closest \
     python -m pytest tests/test_mpi_comparison.py

Performance

Measured on Frontier (OLCF), one MPI rank per MI250X GCD, using a Brunel network on a periodic 3D lattice with a bounded connection radius:

• Weak scaling — at 12,500 neurons per GPU held constant, per-step parallel efficiency stays within 99–100 % from 64 to 2048 GPUs (a 32x span) at in-degrees K = 1000, 2000 and 4000. The largest point is 25.6 M neurons on 2048 GPUs.
• Strong scaling — a fixed 204,800-neuron problem reaches 13.4x speedup in wall time on 64x the GPUs (16 to 2048).

Methodology, the complete measured record, and the caveats are in scaling_analysis/paper_figures/README.md; the design discussion behind the wiring convention is in docs/BRUNEL_SCALING.md. Reproduce with scaling_analysis/weak_3d_chunk.sh and scaling_analysis/strong_3d_chunk.sh.

Documentation

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