def test_model_dict_group():
data = [('a','b',10., 1.), ('a','b',100., 1.),
('b','a',10., 1.), ('b','a',100., 1.)]
d = model_dict_group(data)
mod_dict = d['a', 'b']
int, slope = mod_dict['intercept'], mod_dict['slope']
assert abs(int-1)<.000001 and abs(slope - 0) < .000001
def commtime_dict_mpi(network, nbytes=[10, 100, 1000, 10000]):
"""
inputs
network - dict like {(A, B): {'type': 'mpi'}}
nbytes - iterable of byte counts
outputs
network - dict like {(A, B): {'type': 'mpi', 'intercept':1, 'slope':2}}
"""
# TODO: This is incorrect. We're assuming that the network is a clique
hosts = set(host for (send, recv) in network for host in (send, recv)
if network[send, recv]['type'] is 'mpi')
performance = model_dict_group(comm_times_group(nbytes, hosts))
# inject new information into network dict
return {key: merge(network[key], performance[key]) for key in performance}
def commtime_dict_mpi(network, nbytes=[10, 100, 1000, 10000]):
"""
inputs
network - dict like {(A, B): {'type': 'mpi'}}
nbytes - iterable of byte counts
outputs
network - dict like {(A, B): {'type': 'mpi', 'intercept':1, 'slope':2}}
"""
# TODO: This is incorrect. We're assuming that the network is a clique
hosts = set(host for (send, recv) in network
for host in (send, recv)
if network[send, recv]['type'] is 'mpi')
performance = model_dict_group(comm_times_group(nbytes, hosts))
# inject new information into network dict
return {key: merge(network[key], performance[key]) for key in performance}
