NEST GPU target

NESTML features supported: neurons

Introduction

NEST GPU is a GPU-MPI library for simulation of large-scale networks of spiking neurons, written in C++ and CUDA-C++ programming languages [Gol21].

NESTML code generation support for NEST GPU currently covers neuron models with linear dynamics that can be solved with propagators, as well as neurons that require a numeric solver, which is implemented with a Runge-Kutta-Fehlberg (RK45) solver in NEST GPU.

Generating code

  1. Install NEST GPU. Follow the installation steps in the NEST GPU docs.

  2. Create an environment variable NEST_GPU that points to the NEST GPU source code. For example,

    export NEST_GPU=$HOME/nest-gpu

  3. Install NESTML in $HOME/nestml. The NESTML installation instructions can be found here.

  4. Run the test from NESTML that generates and compiles the code for the neuron models with analytic and numeric solver for NEST GPU, and performs single-neuron simulations. The tests can be found in the directory tests/nest_gpu_tests

    # Test for a neuron model with analytic solver
pytest -s tests/nest_gpu_tests/test_nest_gpu_code_generator_analytic.py

# Test for neuron models with numeric solver
pytest -s tests/nest_gpu_tests/test_nest_gpu_code_generator_numeric.py

Models with numeric solvers

Neuron models that require numeric solvers use Runge-Kutta Fehlberg (rk45) method to solve the ODEs. The minimum step size h_min_rel and initial integration step h0_rel are variables set inside the model during code generation. They can also be modified at runtime as parameters of the model instance.

neuron = ngpu.Create("aeif_cond_alpha_neuron_nestml", 1)
ngpu.SetStatus(neuron, {"h_min_rel": 0.01, "h0_rel": 0.1})

References

[Gol21]

Golosio et al., Fast Simulations of Highly-Connected Spiking Cortical Models Using GPUs, 2021, https://doi.org/10.3389/fncom.2021.627620