def experiment(task, name, queue):
curproc = multiprocessing.current_process().name
print("Process %s started"% curproc)
actionth = task["actionth"]
model = "models/%s" % data["data"]
rate = data["rate"]
action = data["action"]
values = data["values"]
print("Experiment %s with %d values" % ( model, len(values)))
if "map" not in data:
pm = PEPAModel({"file": model, "solver": "sparse"})
pm.derive()
result = rate_experiment(rate, values, action, pm)
# return result
return "ok"
else:
cpus = multiprocessing.cpu_count()
queue = multiprocessing.Queue()
tasks = _carousel(values, cpus)
for t in tasks:
task = {"actionth": action, "rate": rate, "values" : t, "model": model}
p = multiprocessing.Process(target=_job, args=(task, "TASKNAME", queue))
p.start()
vals = []
for i in range(len(tasks)):
result = queue.get()
# print(result)
#vals.append(result)
vals.append("ok")
return vals
def _job(task, name, queue):
curproc = multiprocessing.current_process().name
print("Process %s started" % curproc)
actionth = task["actionth"]
rate = task["rate"]
values = task["values"]
print("Process %s Calculating %d values " % (curproc, len(values)))
pargs = {"file": task["model"], "solver": "sparse"}
pm = PEPAModel(pargs)
pm.derive()
result = rate_experiment(rate, values, actionth, pm)
queue.put(result)
def _job(task, name, queue):
curproc = multiprocessing.current_process().name
print("Process %s started"% curproc)
actionth = task["actionth"]
rate = task["rate"]
values = task["values"]
print("Process %s Calculating %d values " % (curproc, len(values)))
pargs = { "file": task["model"], "solver": "sparse" }
pm = PEPAModel(pargs)
pm.derive()
result = rate_experiment(rate, values, actionth, pm)
queue.put(result)
def experiment(task, name, queue):
curproc = multiprocessing.current_process().name
print("Process %s started" % curproc)
actionth = task["actionth"]
model = "models/%s" % data["data"]
rate = data["rate"]
action = data["action"]
values = data["values"]
print("Experiment %s with %d values" % (model, len(values)))
if "map" not in data:
pm = PEPAModel({"file": model, "solver": "sparse"})
pm.derive()
result = rate_experiment(rate, values, action, pm)
# return result
return "ok"
else:
cpus = multiprocessing.cpu_count()
queue = multiprocessing.Queue()
tasks = _carousel(values, cpus)
for t in tasks:
task = {
"actionth": action,
"rate": rate,
"values": t,
"model": model
}
p = multiprocessing.Process(target=_job,
args=(task, "TASKNAME", queue))
p.start()
vals = []
for i in range(len(tasks)):
result = queue.get()
# print(result)
#vals.append(result)
vals.append("ok")
return vals
