def parallelization_default():
data = model_data()
general_opts = {
"logging_folder": "benchmarks/logs_parallelization_default",
}
for model_name in data:
model = data[model_name]["model"]
opts = data[model_name]["opts"]
opts.update(general_opts)
opts["name"] = model_name
py_augmecon = PyAugmecon(model(model_name), opts)
py_augmecon.solve()
def parallelization_simple():
data = model_data()
general_opts = {
"logging_folder": "benchmarks/logs_parallelization_simple",
"redivide_work": False,
"shared_flag": False,
}
for model_name in data:
model = data[model_name]["model"]
opts = data[model_name]["opts"]
opts.update(general_opts)
opts["name"] = model_name
py_augmecon = PyAugmecon(model(model_name), opts)
py_augmecon.solve()
def augmecon_2():
data = model_data()
general_opts = {
"logging_folder": "benchmarks/logs_augmecon_2",
"shared_flag": False,
"redivide_work": False,
"flag_array": False,
"cpu_count": 1,
"process_timeout": 3600 * 4, # timeout after 4 hours
}
for model_name in data:
model = data[model_name]["model"]
opts = data[model_name]["opts"]
opts.update(general_opts)
opts["name"] = model_name
py_augmecon = PyAugmecon(model(model_name), opts)
py_augmecon.solve()
def parallelization_cores():
data = model_data()
general_opts = {
"logging_folder": "benchmarks/logs_parallelization_cores",
}
# No parallelization for two-objective functions, so testing different
# numbers of cores is not interesting
to_remove = ("2kp50", "2kp100", "2kp250")
for k in to_remove:
data.pop(k, None)
for model_name in data:
for cores in range(2, 49, 2):
model = data[model_name]["model"]
opts = data[model_name]["opts"]
general_opts["cpu_count"] = cores
opts.update(general_opts)
opts["name"] = model_name
py_augmecon = PyAugmecon(model(model_name), opts)
py_augmecon.solve()