args = parser.parse_args()
dt = 1e-4
dur = 1.4
Nt = int(dur / dt)
file_name = None
screen = False
if args.log.lower() in ['file', 'both']:
file_name = 'neurokernel.log'
if args.log.lower() in ['screen', 'both']:
screen = True
logger = base.setup_logger(file_name, screen)
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
man = core.Manager(port_data, port_ctrl)
man.add_brok()
# Load configurations for lamina, medulla and antennal lobe models:
(n_dict_al, s_dict_al) = LPU.lpu_parser('./data/antennallobe.gexf.gz')
lpu_al = LPU(dt,
n_dict_al,
s_dict_al,
input_file='./data/olfactory_input.h5',
output_file='antennallobe_output.h5',
def emulate(n_lpu, n_spike, n_gpot, steps):
"""
Benchmark inter-LPU communication throughput.
Each LPU is configured to use a different local GPU.
Parameters
----------
n_lpu : int
Number of LPUs. Must be at least 2 and no greater than the number of
local GPUs.
n_spike : int
Total number of input and output spiking ports any
single LPU exposes to any other LPU. Each LPU will therefore
have 2*n_spike*(n_lpu-1) total spiking ports.
n_gpot : int
Total number of input and output graded potential ports any
single LPU exposes to any other LPU. Each LPU will therefore
have 2*n_gpot*(n_lpu-1) total graded potential ports.
steps : int
Number of steps to execute.
Returns
-------
average_throughput, total_throughput : float
Average per-step and total received data throughput in bytes/seconds.
exec_time : float
Execution time in seconds.
"""
# Time everything starting with manager initialization:
start_all = time.time()
# Check whether a sufficient number of GPUs are available:
drv.init()
if n_lpu > drv.Device.count():
raise RuntimeError('insufficient number of available GPUs.')
# Set up manager and broker:
man = Manager(get_random_port(), get_random_port(), get_random_port())
man.add_brok()
# Generate selectors for configuring modules and patterns:
mod_sels, pat_sels = gen_sels(n_lpu, n_spike, n_gpot)
# Set up modules:
for i in xrange(n_lpu):
lpu_i = 'lpu%s' % i
sel, sel_in, sel_out, sel_gpot, sel_spike = mod_sels[lpu_i]
m = MyModule(sel, sel_in, sel_out,
sel_gpot, sel_spike,
port_data=man.port_data, port_ctrl=man.port_ctrl,
port_time=man.port_time,
id=lpu_i, device=i, debug=args.debug)
man.add_mod(m)
# Set up connections between module pairs:
for i, j in itertools.combinations(xrange(n_lpu), 2):
lpu_i = 'lpu%s' % i
lpu_j = 'lpu%s' % j
sel_from, sel_to, sel_in_i, sel_out_i, sel_gpot_i, sel_spike_i, \
sel_in_j, sel_out_j, sel_gpot_j, sel_spike_j = pat_sels[(lpu_i, lpu_j)]
pat = Pattern.from_concat(sel_from, sel_to,
from_sel=sel_from, to_sel=sel_to, data=1)
pat.interface[sel_in_i, 'interface', 'io'] = [0, 'in']
pat.interface[sel_out_i, 'interface', 'io'] = [0, 'out']
pat.interface[sel_gpot_i, 'interface', 'type'] = [0, 'gpot']
pat.interface[sel_spike_i, 'interface', 'type'] = [0, 'spike']
pat.interface[sel_in_j, 'interface', 'io'] = [1, 'in']
pat.interface[sel_out_j, 'interface', 'io'] = [1, 'out']
pat.interface[sel_gpot_j, 'interface', 'type'] = [1, 'gpot']
pat.interface[sel_spike_j, 'interface', 'type'] = [1, 'spike']
man.connect(man.modules[lpu_i], man.modules[lpu_j], pat, 0, 1,
compat_check=False)
start_main = time.time()
man.start(steps=steps)
man.stop()
stop_main = time.time()
t = man.get_throughput()
return t[0], (time.time()-start_all), (stop_main-start_main), t[3]
GEXF_FILE = 'retina.gexf.gz'
INPUT_FILE = 'vision_input.h5'
IMAGE_FILE = 'image1.mat'
OUTPUT_FILE = 'retina_output.h5'
if args.input:
print('Generating input of model from image file')
generate_input(INPUT_FILE, IMAGE_FILE, args.num_layers)
if args.gexf:
print('Writing retina lpu')
n = args.num_layers
photoreceptor_num = 6*(3*n*(n+1)+1)
generate_gexf(GEXF_FILE, photoreceptor_num)
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
man = core.Manager(port_data, port_ctrl)
man.add_brok()
print('Parsing lpu data')
n_dict_ret, s_dict_ret = LPU.lpu_parser(GEXF_FILE)
print('Initializing LPU')
lpu_ret = LPU(dt, n_dict_ret, s_dict_ret,
input_file=INPUT_FILE,
output_file=OUTPUT_FILE, port_ctrl=port_ctrl,
port_data=port_data, device=args.ret_dev, id='retina',
def run(connected):
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
out_name = 'un' if not connected else 'co'
man = core.Manager(port_data, port_ctrl)
man.add_brok()
lpu_file_0 = './data/generic_lpu_0.gexf.gz'
lpu_file_1 = './data/generic_lpu_1.gexf.gz'
(n_dict_0, s_dict_0) = LPU.lpu_parser(lpu_file_0)
(n_dict_1, s_dict_1) = LPU.lpu_parser(lpu_file_1)
ge_0_id = 'ge_0'
ge_0 = LPU(dt, n_dict_0, s_dict_0,
input_file='./data/generic_input_0.h5',
output_file='generic_output_0_%s.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
device=args.gpu_dev[0], id=ge_0_id,
debug=args.debug)
man.add_mod(ge_0)
ge_1_id = 'ge_1'
ge_1 = LPU(dt, n_dict_1, s_dict_1,
input_file='./data/generic_input_1.h5',
output_file='generic_output_1_%s.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
device=args.gpu_dev[1], id=ge_1_id,
debug=args.debug)
man.add_mod(ge_1)
# Connect the public neurons in the two LPUs:
df_neu_0, df_syn_0 = neurokernel.tools.graph.graph_to_df(nx.read_gexf(lpu_file_0))
df_neu_1, df_syn_1 = neurokernel.tools.graph.graph_to_df(nx.read_gexf(lpu_file_1))
# Number of public neurons in each LPU:
N_spike_0 = len(df_neu_0[(df_neu_0['spiking']==True)&(df_neu_0['public']==True)])
N_gpot_0 = len(df_neu_0[(df_neu_0['spiking']==False)&(df_neu_0['public']==True)])
N_spike_1 = len(df_neu_1[(df_neu_1['spiking']==True)&(df_neu_1['public']==True)])
N_gpot_1 = len(df_neu_1[(df_neu_1['spiking']==False)&(df_neu_1['public']==True)])
# Alpha function synaptic parameters:
alphasynapse_type_params = {'AlphaSynapse': ['ad', 'ar', 'gmax', 'id', 'class', 'conductance',
'reverse']}
if connected:
conn = core.Connectivity(N_gpot_0, N_spike_0, N_gpot_1, N_spike_1, 1,
ge_0.id, ge_1.id, alphasynapse_type_params)
for id, (i, j) in enumerate(itertools.product(xrange(N_spike_0), xrange(N_spike_1))):
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j] = 1
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'name'] = 'int_0to1_%s_%s' % (i, j)
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'model'] = 'AlphaSynapse'
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'ad'] = 0.19*1000
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'ar'] = 1.1*100
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'class'] = 0
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'conductance'] = True
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'gmax'] = 0.003
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'id'] = id
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'reverse'] = 0.065
man.connect(ge_0, ge_1, conn)
man.start(steps=args.steps)
man.stop()
def run_test(m0_sel_in_gpot, m0_sel_in_spike, m0_sel_out_gpot,
m0_sel_out_spike, m1_sel_in_gpot, m1_sel_in_spike,
m1_sel_out_gpot, m1_sel_out_spike):
# Create test module classes with a queue installed in the destination
# module to check that data was correctly propagated:
class TestModule0(TestModule):
def __init__(self, *args, **kwargs):
super(TestModule0, self).__init__(*args, **kwargs)
self.q = Queue()
def run_step(self):
self.log_info('saving data to queue before run step')
if self.steps > 0:
self.q.put(
(self.pm['gpot'][self._out_port_dict['gpot']['m1']].copy(),
self.pm['spike'][self._out_port_dict['spike']
['m1']].copy()))
super(TestModule0, self).run_step()
class TestModule1(TestModule):
def __init__(self, *args, **kwargs):
super(TestModule1, self).__init__(*args, **kwargs)
self.q = Queue()
def run_step(self):
super(TestModule1, self).run_step()
self.log_info('saving data to queue after run step')
if self.steps > 0:
self.q.put(
(self.pm['gpot'][self._in_port_dict['gpot']['m0']].copy(),
self.pm['spike'][self._in_port_dict['spike']
['m0']].copy()))
m0_sel_gpot = m0_sel_in_gpot + m0_sel_out_gpot
m0_sel_spike = m0_sel_in_spike + m0_sel_out_spike
m0_sel = m0_sel_in_gpot + m0_sel_in_spike + m0_sel_out_gpot + m0_sel_out_spike
m0_data_gpot = np.ones(len(m0_sel_gpot), np.double)
m0_data_spike = np.ones(len(m0_sel_spike), np.int32)
m1_sel_gpot = m1_sel_in_gpot + m1_sel_out_gpot
m1_sel_spike = m1_sel_in_spike + m1_sel_out_spike
m1_sel = m1_sel_in_gpot + m1_sel_in_spike + m1_sel_out_gpot + m1_sel_out_spike
m1_data_gpot = np.zeros(len(m1_sel_gpot), np.double)
m1_data_spike = np.zeros(len(m1_sel_spike), np.int32)
# Instantiate manager and broker:
man = Manager(get_random_port(), get_random_port(), get_random_port())
man.add_brok()
# Add modules:
m0 = TestModule0(m0_sel,
m0_sel_in_gpot,
m0_sel_in_spike,
m0_sel_out_gpot,
m0_sel_out_spike,
m0_data_gpot,
m0_data_spike,
man.port_data,
man.port_ctrl,
man.port_time,
id='m0')
man.add_mod(m0)
m1 = TestModule1(m1_sel,
m1_sel_in_gpot,
m1_sel_in_spike,
m1_sel_out_gpot,
m1_sel_out_spike,
m1_data_gpot,
m1_data_spike,
man.port_data,
man.port_ctrl,
man.port_time,
id='m1')
man.add_mod(m1)
# Connect the modules:
pat = Pattern(m0_sel, m1_sel)
pat.interface[m0_sel_in_gpot] = [0, 'in', 'gpot']
pat.interface[m0_sel_out_gpot] = [0, 'out', 'gpot']
pat.interface[m0_sel_in_spike] = [0, 'in', 'spike']
pat.interface[m0_sel_out_spike] = [0, 'out', 'spike']
pat.interface[m1_sel_in_gpot] = [1, 'in', 'gpot']
pat.interface[m1_sel_out_gpot] = [1, 'out', 'gpot']
pat.interface[m1_sel_in_spike] = [1, 'in', 'spike']
pat.interface[m1_sel_out_spike] = [1, 'out', 'spike']
for sel_from, sel_to in zip(m0_sel_out_gpot, m1_sel_in_gpot):
if not (sel_from == ((), ) or sel_to == ((), )):
pat[sel_from, sel_to] = 1
for sel_from, sel_to in zip(m0_sel_out_spike, m1_sel_in_spike):
if not (sel_from == ((), ) or sel_to == ((), )):
pat[sel_from, sel_to] = 1
man.connect(m0, m1, pat, 0, 1)
# Execute exactly two steps; m0 transmits data during the first step, which
# should be received by m1 during the second step:
man.start(steps=2)
man.stop()
# Forcibly terminate all processes that are still alive:
if m0.is_alive():
m0.terminate()
if m1.is_alive():
m1.terminate()
for b in man.brokers.values():
if b.is_alive():
b.terminate()
# Check that data was propagated correctly:
m0_data_gpot_after, m0_data_spike_after = m0.q.get()
m1_data_gpot_after, m1_data_spike_after = m1.q.get()
assert all(m0_data_gpot_after == m1_data_gpot_after)
assert all(m0_data_spike_after == m1_data_spike_after)
def run_test(m0_sel_in_gpot, m0_sel_in_spike,
m0_sel_out_gpot, m0_sel_out_spike,
m1_sel_in_gpot, m1_sel_in_spike,
m1_sel_out_gpot, m1_sel_out_spike):
# Create test module classes with a queue installed in the destination
# module to check that data was correctly propagated:
class TestModule0(TestModule):
def __init__(self, *args, **kwargs):
super(TestModule0, self).__init__(*args, **kwargs)
self.q = Queue()
def run_step(self):
self.log_info('saving data to queue before run step')
if self.steps > 0:
self.q.put((self.pm['gpot'][self._out_port_dict['gpot']['m1']].copy(),
self.pm['spike'][self._out_port_dict['spike']['m1']].copy()))
super(TestModule0, self).run_step()
class TestModule1(TestModule):
def __init__(self, *args, **kwargs):
super(TestModule1, self).__init__(*args, **kwargs)
self.q = Queue()
def run_step(self):
super(TestModule1, self).run_step()
self.log_info('saving data to queue after run step')
if self.steps > 0:
self.q.put((self.pm['gpot'][self._in_port_dict['gpot']['m0']].copy(),
self.pm['spike'][self._in_port_dict['spike']['m0']].copy()))
m0_sel_gpot = m0_sel_in_gpot+m0_sel_out_gpot
m0_sel_spike = m0_sel_in_spike+m0_sel_out_spike
m0_sel = m0_sel_in_gpot+m0_sel_in_spike+m0_sel_out_gpot+m0_sel_out_spike
m0_data_gpot = np.ones(len(m0_sel_gpot), np.double)
m0_data_spike = np.ones(len(m0_sel_spike), np.int32)
m1_sel_gpot = m1_sel_in_gpot+m1_sel_out_gpot
m1_sel_spike = m1_sel_in_spike+m1_sel_out_spike
m1_sel = m1_sel_in_gpot+m1_sel_in_spike+m1_sel_out_gpot+m1_sel_out_spike
m1_data_gpot = np.zeros(len(m1_sel_gpot), np.double)
m1_data_spike = np.zeros(len(m1_sel_spike), np.int32)
# Instantiate manager and broker:
man = Manager(get_random_port(), get_random_port(), get_random_port())
man.add_brok()
# Add modules:
m0 = TestModule0(m0_sel, m0_sel_in_gpot, m0_sel_in_spike,
m0_sel_out_gpot, m0_sel_out_spike,
m0_data_gpot, m0_data_spike,
man.port_data, man.port_ctrl, man.port_time,
id='m0')
man.add_mod(m0)
m1 = TestModule1(m1_sel, m1_sel_in_gpot, m1_sel_in_spike,
m1_sel_out_gpot, m1_sel_out_spike,
m1_data_gpot, m1_data_spike,
man.port_data, man.port_ctrl, man.port_time,
id='m1')
man.add_mod(m1)
# Connect the modules:
pat = Pattern(m0_sel, m1_sel)
pat.interface[m0_sel_in_gpot] = [0, 'in', 'gpot']
pat.interface[m0_sel_out_gpot] = [0, 'out', 'gpot']
pat.interface[m0_sel_in_spike] = [0, 'in', 'spike']
pat.interface[m0_sel_out_spike] = [0, 'out', 'spike']
pat.interface[m1_sel_in_gpot] = [1, 'in', 'gpot']
pat.interface[m1_sel_out_gpot] = [1, 'out', 'gpot']
pat.interface[m1_sel_in_spike] = [1, 'in', 'spike']
pat.interface[m1_sel_out_spike] = [1, 'out', 'spike']
for sel_from, sel_to in zip(m0_sel_out_gpot,
m1_sel_in_gpot):
if not (sel_from == ((),) or sel_to == ((),)):
pat[sel_from, sel_to] = 1
for sel_from, sel_to in zip(m0_sel_out_spike,
m1_sel_in_spike):
if not (sel_from == ((),) or sel_to == ((),)):
pat[sel_from, sel_to] = 1
man.connect(m0, m1, pat, 0, 1)
# Execute exactly two steps; m0 transmits data during the first step, which
# should be received by m1 during the second step:
man.start(steps=2)
man.stop()
# Forcibly terminate all processes that are still alive:
if m0.is_alive():
m0.terminate()
if m1.is_alive():
m1.terminate()
for b in man.brokers.values():
if b.is_alive():
b.terminate()
# Check that data was propagated correctly:
m0_data_gpot_after, m0_data_spike_after = m0.q.get()
m1_data_gpot_after, m1_data_spike_after = m1.q.get()
assert all(m0_data_gpot_after == m1_data_gpot_after)
assert all(m0_data_spike_after == m1_data_spike_after)
def run(connected):
if args.port_data is None and args.port_ctrl is None:
port_data = get_random_port()
port_ctrl = get_random_port()
else:
port_data = args.port_data
port_ctrl = args.port_ctrl
out_name = 'un' if not connected else 'co'
man = core.Manager(port_data, port_ctrl)
man.add_brok()
lpu_file_0 = './data/generic_lpu_0.gexf.gz'
lpu_file_1 = './data/generic_lpu_1.gexf.gz'
(n_dict_0, s_dict_0) = LPU.lpu_parser(lpu_file_0)
(n_dict_1, s_dict_1) = LPU.lpu_parser(lpu_file_1)
ge_0_id = 'ge_0'
ge_0 = LPU(dt,
n_dict_0,
s_dict_0,
input_file='./data/generic_input_0.h5',
output_file='generic_output_0_%s.h5' % out_name,
port_ctrl=port_ctrl,
port_data=port_data,
device=args.gpu_dev[0],
id=ge_0_id,
debug=args.debug)
man.add_mod(ge_0)
ge_1_id = 'ge_1'
ge_1 = LPU(dt,
n_dict_1,
s_dict_1,
input_file='./data/generic_input_1.h5',
output_file='generic_output_1_%s.h5' % out_name,
port_ctrl=port_ctrl,
port_data=port_data,
device=args.gpu_dev[1],
id=ge_1_id,
debug=args.debug)
man.add_mod(ge_1)
# Connect the public neurons in the two LPUs:
df_neu_0, df_syn_0 = neurokernel.tools.graph.graph_to_df(
nx.read_gexf(lpu_file_0))
df_neu_1, df_syn_1 = neurokernel.tools.graph.graph_to_df(
nx.read_gexf(lpu_file_1))
# Number of public neurons in each LPU:
N_spike_0 = len(df_neu_0[(df_neu_0['spiking'] == True)
& (df_neu_0['public'] == True)])
N_gpot_0 = len(df_neu_0[(df_neu_0['spiking'] == False)
& (df_neu_0['public'] == True)])
N_spike_1 = len(df_neu_1[(df_neu_1['spiking'] == True)
& (df_neu_1['public'] == True)])
N_gpot_1 = len(df_neu_1[(df_neu_1['spiking'] == False)
& (df_neu_1['public'] == True)])
# Alpha function synaptic parameters:
alphasynapse_type_params = {
'AlphaSynapse':
['ad', 'ar', 'gmax', 'id', 'class', 'conductance', 'reverse']
}
if connected:
conn = core.Connectivity(N_gpot_0, N_spike_0, N_gpot_1, N_spike_1, 1,
ge_0.id, ge_1.id, alphasynapse_type_params)
for id, (i, j) in enumerate(
itertools.product(xrange(N_spike_0), xrange(N_spike_1))):
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j] = 1
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0,
'name'] = 'int_0to1_%s_%s' % (i, j)
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0,
'model'] = 'AlphaSynapse'
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0,
'ad'] = 0.19 * 1000
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'ar'] = 1.1 * 100
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'class'] = 0
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0,
'conductance'] = True
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'gmax'] = 0.003
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0, 'id'] = id
conn[ge_0_id, 'spike', i, ge_1_id, 'spike', j, 0,
'reverse'] = 0.065
man.connect(ge_0, ge_1, conn)
man.start(steps=args.steps)
man.stop()
def run(connected):
if args.port_data is None:
port_data = get_random_port()
else:
port_data = args.port_data
if args.port_ctrl is None:
port_ctrl = get_random_port()
else:
port_ctrl = args.port_ctrl
if args.port_time is None:
port_time = get_random_port()
else:
port_time = args.port_time
out_name = 'un' if not connected else 'co'
man = core.Manager(port_data, port_ctrl, port_time)
man.add_brok()
lpu_file_0 = './data/generic_lpu_0.gexf.gz'
lpu_file_1 = './data/generic_lpu_1.gexf.gz'
(n_dict_0, s_dict_0) = LPU.lpu_parser(lpu_file_0)
(n_dict_1, s_dict_1) = LPU.lpu_parser(lpu_file_1)
lpu_0_id = 'lpu_0'
lpu_0 = LPU(dt, n_dict_0, s_dict_0,
input_file='./data/generic_lpu_0_input.h5',
output_file='generic_lpu_0_%s_output.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
port_time=port_time,
device=args.gpu_dev[0], id=lpu_0_id,
debug=args.debug, time_sync=args.time_sync)
man.add_mod(lpu_0)
lpu_1_id = 'lpu_1'
lpu_1 = LPU(dt, n_dict_1, s_dict_1,
input_file='./data/generic_lpu_1_input.h5',
output_file='generic_lpu_1_%s_output.h5' % out_name,
port_ctrl=port_ctrl, port_data=port_data,
port_time=port_time,
device=args.gpu_dev[1], id=lpu_1_id,
debug=args.debug, time_sync=args.time_sync)
man.add_mod(lpu_1)
# Create random connections between the input and output ports if the LPUs
# are to be connected:
if connected:
# Find all output and input port selectors in each LPU:
out_ports_0 = lpu_0.interface.out_ports().to_selectors()
out_ports_1 = lpu_1.interface.out_ports().to_selectors()
in_ports_0 = lpu_0.interface.in_ports().to_selectors()
in_ports_1 = lpu_1.interface.in_ports().to_selectors()
out_ports_spk_0 = lpu_0.interface.out_ports().spike_ports().to_selectors()
out_ports_gpot_0 = lpu_0.interface.out_ports().gpot_ports().to_selectors()
out_ports_spk_1 = lpu_1.interface.out_ports().spike_ports().to_selectors()
out_ports_gpot_1 = lpu_1.interface.out_ports().gpot_ports().to_selectors()
in_ports_spk_0 = lpu_0.interface.in_ports().spike_ports().to_selectors()
in_ports_gpot_0 = lpu_0.interface.in_ports().gpot_ports().to_selectors()
in_ports_spk_1 = lpu_1.interface.in_ports().spike_ports().to_selectors()
in_ports_gpot_1 = lpu_1.interface.in_ports().gpot_ports().to_selectors()
# Initialize a connectivity pattern between the two sets of port
# selectors:
pat = pattern.Pattern(','.join(out_ports_0+in_ports_0),
','.join(out_ports_1+in_ports_1))
# Create connections from the ports with identifiers matching the output
# ports of one LPU to the ports with identifiers matching the input
# ports of the other LPU:
N_conn_spk_0_1 = min(len(out_ports_spk_0), len(in_ports_spk_1))
N_conn_gpot_0_1 = min(len(out_ports_gpot_0), len(in_ports_gpot_1))
for src, dest in zip(random.sample(out_ports_spk_0, N_conn_spk_0_1),
random.sample(in_ports_spk_1, N_conn_spk_0_1)):
pat[src, dest] = 1
pat.interface[src, 'type'] = 'spike'
pat.interface[dest, 'type'] = 'spike'
for src, dest in zip(random.sample(out_ports_gpot_0, N_conn_gpot_0_1),
random.sample(in_ports_gpot_1, N_conn_gpot_0_1)):
pat[src, dest] = 1
pat.interface[src, 'type'] = 'gpot'
pat.interface[dest, 'type'] = 'gpot'
man.connect(lpu_0, lpu_1, pat, 0, 1)
man.start(steps=args.steps)
man.stop()