.. currentmodule:: brian2 .. float_32_64_benchmark: Example: float_32_64_benchmark ============================== .. only:: html .. |launchbinder| image:: http://mybinder.org/badge.svg .. _launchbinder: https://mybinder.org/v2/gh/brian-team/brian2-binder/master?filepath=examples/advanced/float_32_64_benchmark.ipynb .. note:: You can launch an interactive, editable version of this example without installing any local files using the Binder service (although note that at some times this may be slow or fail to open): |launchbinder|_ Benchmark showing the performance of float32 versus float64. :: from brian2 import * from brian2.devices.device import reset_device, reinit_devices # CUBA benchmark def run_benchmark(name): if name=='CUBA': taum = 20*ms taue = 5*ms taui = 10*ms Vt = -50*mV Vr = -60*mV El = -49*mV eqs = ''' dv/dt = (ge+gi-(v-El))/taum : volt (unless refractory) dge/dt = -ge/taue : volt dgi/dt = -gi/taui : volt ''' P = NeuronGroup(4000, eqs, threshold='v>Vt', reset='v = Vr', refractory=5*ms, method='exact') P.v = 'Vr + rand() * (Vt - Vr)' P.ge = 0*mV P.gi = 0*mV we = (60*0.27/10)*mV # excitatory synaptic weight (voltage) wi = (-20*4.5/10)*mV # inhibitory synaptic weight Ce = Synapses(P, P, on_pre='ge += we') Ci = Synapses(P, P, on_pre='gi += wi') Ce.connect('i<3200', p=0.02) Ci.connect('i>=3200', p=0.02) elif name=='COBA': # Parameters area = 20000 * umetre ** 2 Cm = (1 * ufarad * cm ** -2) * area gl = (5e-5 * siemens * cm ** -2) * area El = -60 * mV EK = -90 * mV ENa = 50 * mV g_na = (100 * msiemens * cm ** -2) * area g_kd = (30 * msiemens * cm ** -2) * area VT = -63 * mV # Time constants taue = 5 * ms taui = 10 * ms # Reversal potentials Ee = 0 * mV Ei = -80 * mV we = 6 * nS # excitatory synaptic weight wi = 67 * nS # inhibitory synaptic weight # The model eqs = Equations(''' dv/dt = (gl*(El-v)+ge*(Ee-v)+gi*(Ei-v)- g_na*(m*m*m)*h*(v-ENa)- g_kd*(n*n*n*n)*(v-EK))/Cm : volt dm/dt = alpha_m*(1-m)-beta_m*m : 1 dn/dt = alpha_n*(1-n)-beta_n*n : 1 dh/dt = alpha_h*(1-h)-beta_h*h : 1 dge/dt = -ge*(1./taue) : siemens dgi/dt = -gi*(1./taui) : siemens alpha_m = 0.32*(mV**-1)*4*mV/exprel((13*mV-v+VT)/(4*mV))/ms : Hz beta_m = 0.28*(mV**-1)*5*mV/exprel((v-VT-40*mV)/(5*mV))/ms : Hz alpha_h = 0.128*exp((17*mV-v+VT)/(18*mV))/ms : Hz beta_h = 4./(1+exp((40*mV-v+VT)/(5*mV)))/ms : Hz alpha_n = 0.032*(mV**-1)*5*mV/exprel((15*mV-v+VT)/(5*mV))/ms : Hz beta_n = .5*exp((10*mV-v+VT)/(40*mV))/ms : Hz ''') P = NeuronGroup(4000, model=eqs, threshold='v>-20*mV', refractory=3 * ms, method='exponential_euler') Pe = P[:3200] Pi = P[3200:] Ce = Synapses(Pe, P, on_pre='ge+=we') Ci = Synapses(Pi, P, on_pre='gi+=wi') Ce.connect(p=0.02) Ci.connect(p=0.02) # Initialization P.v = 'El + (randn() * 5 - 5)*mV' P.ge = '(randn() * 1.5 + 4) * 10.*nS' P.gi = '(randn() * 12 + 20) * 10.*nS' run(1 * second, profile=True) return sum(t for name, t in magic_network.profiling_info) def generate_results(num_repeats): results = {} for name in ['CUBA', 'COBA']: for target in ['numpy', 'cython']: for dtype in [float32, float64]: prefs.codegen.target = target prefs.core.default_float_dtype = dtype times = [run_benchmark(name) for repeat in range(num_repeats)] results[name, target, dtype.__name__] = amin(times) for name in ['CUBA', 'COBA']: for dtype in [float32, float64]: times = [] for _ in range(num_repeats): reset_device() reinit_devices() set_device('cpp_standalone', directory=None, with_output=False) prefs.core.default_float_dtype = dtype times.append(run_benchmark(name)) results[name, 'cpp_standalone', dtype.__name__] = amin(times) return results results = generate_results(3) bar_width = 0.9 names = ['CUBA', 'COBA'] targets = ['numpy', 'cython', 'cpp_standalone'] precisions = ['float32', 'float64'] figure(figsize=(8, 8)) for j, name in enumerate(names): subplot(2, 2, 1+2*j) title(name) index = arange(len(targets)) for i, precision in enumerate(precisions): bar(index+i*bar_width/len(precisions), [results[name, target, precision] for target in targets], bar_width/len(precisions), label=precision, align='edge') ylabel('Time (s)') if j: xticks(index+0.5*bar_width, targets, rotation=45) else: xticks(index+0.5*bar_width, ('',)*len(targets)) legend(loc='best') subplot(2, 2, 2+2*j) index = arange(len(precisions)) for i, target in enumerate(targets): bar(index+i*bar_width/len(targets), [results[name, target, precision] for precision in precisions], bar_width/len(targets), label=target, align='edge') ylabel('Time (s)') if j: xticks(index+0.5*bar_width, precisions, rotation=45) else: xticks(index+0.5*bar_width, ('',)*len(precisions)) legend(loc='best') tight_layout() show()