NESTML Benchmark with MPI

extras/benchmark/Running/plotting_options.py

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+import matplotlib.pyplot as plt
+
+# Plotting options
+
+# plt.style.use('tableau-colorblind10')
+palette = plt.get_cmap("tab10")
+
+# def palette(x):
+#     return plt.rcParams['axes.prop_cycle'].by_key()['color'][x]
+
+
+plt.rcParams['axes.grid'] = True
+
+plt.rcParams['grid.color'] = 'gray'
+plt.rcParams['grid.linestyle'] = '--'
+plt.rcParams['grid.linewidth'] = 0.5
+
+plt.rcParams['axes.labelsize'] = 16  # Size of the axis labels
+plt.rcParams['xtick.labelsize'] = 14  # Size of the x-axis tick labels
+plt.rcParams['ytick.labelsize'] = 14  # Size of the y-axis tick labels
+plt.rcParams['legend.fontsize'] = 14  # Size of the legend labels
+plt.rcParams['figure.titlesize'] = 16  # Size of the figure title

extras/benchmark/Running/sbatch_run.sh.jinja2

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+#!/bin/bash
+#SBATCH --job-name=nestml_benchmark
+#SBATCH --account=jinm60
+#SBATCH --partition=dc-cpu
+#SBATCH --time=05:00:00
+#SBATCH --nodes={{nodes}}
+#SBATCH --ntasks-per-node={{ntasks_per_node}}
+#SBATCH --cpus-per-task={{cpus_per_task}}
+#SBATCH --hint=nomultithread
+#SBTACH --exclusive
+#SBATCH --output={{combination["output_file"]}}
+#SBATCH --error={{combination["error_file"]}}
+#SBATCH --disable-perfparanoid
+
+{%- if enable_profile %}
+ml AMD-uProf
+{%- endif %}
+
+export OMP_PROC_BIND=TRUE
+export PROGRAM="python3 Running/brunel_alpha_nest.py --simulated_neuron {{combination["simulated_neuron"]}} --network_scale {{combination["network_scale"]}} --nodes {{ combination["nodes"] }} --threads {{combination["threads"]}}  --iteration {{combination["iteration"]}} --benchmarkPath {{combination["benchmarkPath"]}} --rng_seed {{combination["rng_seed"]}}"
+
+{%- if enable_profile %}
+srun AMDuProfCLI collect --config tbp --mpi -o cpuprof $PROGRAM
+{%- else %}
+srun $PROGRAM
+{%- endif %}

extras/benchmark/Running/start.sh

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+#!/bin/bash
+
+source "/home/charl/julich/nest-simulator-install/bin/nest_vars.sh"
+

extras/benchmark/buildAll.sh

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+#!/bin/bash
+
+ml Stages/2024 GCC Boost ParaStationMPI GSL jemalloc Python SciPy-Stack mpi4py CMake Autotools
+
+echo installing nestml-simulator
+
+#Install nestsimulator
+rm -rf nest-build
+rm -rf nest-simulator-install
+
+
+# Paths
+srcPath=$(pwd)/nest-simulator
+installPath=$(pwd)/nest-simulator-install
+
+mkdir nest-build
+mkdir nest-simulator-install
+
+cd nest-build
+
+cmake -DCMAKE_INSTALL_PREFIX:PATH=$installPath $srcPath -Dwith-mpi=OFF -Dwith-optimize="-O3" -Dwith-readline=OFF -Dwith-detailed-timers=ON 
+make -j24 install
+
+cd ..
+
+echo installing nestml
+#install nestml
+cd nestml
+python3 -m pip install .
+cd ..
+
+source nest-simulator-install/bin/nest_vars.sh
+
+echo installing custom neurons
+#install custom neurons
+
+source updateCustomNeurons.sh
+
+source run.sh

extras/benchmark/compile_nest.sh

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+#!/bin/bash -x
+
+ml Stages/2024 GCC Boost ParaStationMPI GSL jemalloc Python SciPy-Stack mpi4py CMake Autotools
+
+python3 -m pip install pytest-xdist pytest-timeout junitparser
+
+cd
+mv -vi nest-simulator /tmp
+git clone https://github.com/nest/nest-simulator
+cd nest-simulator
+git checkout v3.6
+cd ..
+
+mv -vi nest-simulator-build /tmp/
+mkdir nest-simulator-build
+cd nest-simulator-build
+
+# TODO: -Dwith-detailed-timers=ON required?
+cmake -DCMAKE_INSTALL_PREFIX:PATH=$HOME/nest-simulator-install -Dwith-mpi=ON -Dwith-optimize="-O3" -Dwith-readline=OFF ../nest-simulator
+
+make -j16
+make install

extras/benchmark/run.sh

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+#!/bin/bash
+
+ml Stages/2024 GCC Boost ParaStationMPI GSL jemalloc Python SciPy-Stack mpi4py CMake Autotools
+
+cd Running
+python3 benchmark.py
+cd ..

extras/benchmark/updateCustomNeurons.sh

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+#!/bin/bash
+source ../nest-simulator-install/bin/nest_vars.sh
+
+cd Running/targets_aeif_psc_alpha/target_optimized
+rm -rf CMakeCache.txt CMakeFiles
+cmake .
+make -j24 install
+cd ../../..
+
+cd Running/targets_aeif_psc_alpha/target
+rm -rf CMakeCache.txt CMakeFiles
+cmake .
+make -j24 install
+cd ../../..
+
+cd Running/targets_aeif_psc_alpha/target_plastic
+rm -rf CMakeCache.txt CMakeFiles
+cmake .
+make -j24 install
+cd ../../..
+
+
+cd Running/targets_aeif_psc_alpha/target_plastic_noco
+rm -rf CMakeCache.txt CMakeFiles
+cmake .
+make -j24 install
+cd ../../..

models/neurons/aeif_psc_alpha_neuron.nestml

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+"""
+aeif_psc_alpha - Conductance based exponential integrate-and-fire neuron model
+###############################################################################
+
+Description
++++++++++++
+
+aeif_psc_alpha is the adaptive exponential integrate and fire neuron according to Brette and Gerstner (2005), with post-synaptic conductances in the form of a bi-exponential ("alpha") function.
+
+The membrane potential is given by the following differential equation:
+
+.. math::
+
+   C_m \frac{dV_m}{dt} =
+   -g_L(V_m-E_L)+g_L\Delta_T\exp\left(\frac{V_m-V_{th}}{\Delta_T}\right) -
+ g_e(t)(V_m-E_e) \\
+                                                     -g_i(t)(V_m-E_i)-w + I_e
+
+and
+
+.. math::
+
+ \tau_w \frac{dw}{dt} = a(V_m-E_L) - w
+
+Note that the membrane potential can diverge to positive infinity due to the exponential term. To avoid numerical instabilities, instead of :math:`V_m`, the value :math:`\min(V_m,V_{peak})` is used in the dynamical equations.
+
+
+References
+++++++++++
+
+.. [1] Brette R and Gerstner W (2005). Adaptive exponential
+       integrate-and-fire model as an effective description of neuronal
+       activity. Journal of Neurophysiology. 943637-3642
+       DOI: https://doi.org/10.1152/jn.00686.2005
+
+
+See also
+++++++++
+
+iaf_psc_alpha, aeif_psc_exp
+"""
+model aeif_psc_alpha_neuron:
+
+    state:
+        V_m mV = E_L       # Membrane potential
+        w pA = 0 pA        # Spike-adaptation current
+        refr_t ms = 0 ms    # Refractory period timer
+        is_refractory integer = 0
+        g_exc nS = 0 nS      # AHP conductance
+        g_exc' nS/ms = 0 nS/ms   # AHP conductance
+        g_inh nS = 0 nS      # AHP conductance
+        g_inh' nS/ms = 0 nS/ms   # AHP conductance
+
+    equations:
+        inline V_bounded mV = min(V_m, V_peak) # prevent exponential divergence
+
+        g_exc'' = -2 * g_exc' / tau_exc - g_exc / tau_exc**2
+        g_inh'' = -2 * g_inh' / tau_inh - g_inh / tau_inh**2
+
+        # Add inlines to simplify the equation definition of V_m
+        inline exp_arg real = (V_bounded - V_th) / Delta_T
+        inline I_spike pA = g_L * Delta_T * exp(exp_arg)
+        inline I_syn_exc pA = g_exc / nS * pA
+        inline I_syn_inh pA = g_exc / nS * pA
+
+        V_m' = is_refractory * (-g_L * (V_bounded - E_L) + I_spike + I_syn_exc - I_syn_inh - w + I_e + I_stim) / C_m
+        w' = (a * (V_bounded - E_L) - w) / tau_w
+
+    parameters:
+        # membrane parameters
+        C_m pF = 281.0 pF         # Membrane Capacitance
+        refr_T ms = 2 ms         # Duration of refractory period
+        V_reset mV = -60.0 mV     # Reset Potential
+        g_L nS = 30.0 nS          # Leak Conductance
+        E_L mV = -70.6 mV         # Leak reversal Potential (aka resting potential)
+
+        # spike adaptation parameters
+        a nS = 4 nS               # Subthreshold adaptation
+        b pA = 80.5 pA            # Spike-triggered adaptation
+        Delta_T mV = 2.0 mV       # Slope factor
+        tau_w ms = 144.0 ms       # Adaptation time constant
+        V_th mV = -50.4 mV        # Threshold Potential
+        V_peak mV = 0 mV          # Spike detection threshold
+
+        # synaptic parameters
+        tau_exc ms = 0.2 ms    # Synaptic Time Constant Excitatory Synapse
+        tau_inh ms = 2.0 ms    # Synaptic Time Constant for Inhibitory Synapse
+
+        # constant external input current
+        I_e pA = 0 pA
+
+    input:
+        exc_spikes <- excitatory spike
+        inh_spikes <- inhibitory spike
+        I_stim pA <- continuous
+
+    output:
+        spike
+
+    update:
+        if is_refractory == 1:
+            # neuron is absolute refractory, do not evolve V_m
+            refr_t -= resolution()
+
+        # always evolve all ODEs; V_m 
+        integrate_odes()
+
+    onCondition(V_m >= V_th):
+        # threshold crossing
+        refr_t = refr_T    # start of the refractory period
+        is_refractory = 1
+        V_m = V_reset
+        w += b
+        emit_spike()
+
+    onCondition(is_refractory == 1 and refr_t <= resolution() / 2):
+        # end of refractory period
+        refr_t = 0 ms
+        is_refractory = 0