logbook=logbook,
stats=stats,
gen_curr=0,
gen_step=GEN,
invalidate_fitness=True,
initial_pop=None,
w=W,
c1=C1,
c2=C2,
n=N,
)
solution = construct_solution(best, sorted_tasks)
schedule = build_schedule(_wf, estimator, rm, solution)
Utility.validate_static_schedule(_wf, schedule)
makespan = Utility.makespan(schedule)
print("Final makespan: {0}".format(makespan))
print("Heft makespan: {0}".format(Utility.makespan(heft_schedule)))
return makespan
if __name__ == "__main__":
result = repeat(do_exp, 20)
sts = interval_statistics(result)
print("Statistics: {0}".format(interval_stat_string(sts)))
print(result)
pass
from heft.settings import __root_path__
_wf = wf("Montage_100")
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
estimator = ExperimentEstimator(None, ideal_flops=20, transfer_time=100)
heft_mapping = extract_mapping_from_ga_file("{0}/temp/heft_etalon_full_tr100_m100.json".format(__root_path__), rm)
heft_ordering = extract_ordering_from_ga_file("{0}/temp/heft_etalon_full_tr100_m100.json".format(__root_path__))
params = {
"cxpb": 0.9,
"mutpb": 0.9,
"ngen": 100,
"pop_size": 100
}
def do_exp():
result, logbook = run_dcga(_wf, estimator, rm, heft_mapping, heft_ordering, **params)
saver = UniqueNameSaver("{0}/temp/dcga_exp".format(__root_path__))
data = {
"final_makespan": result,
"iterations": logbook
}
saver(data)
return result
if __name__ == "__main__":
res = repeat(do_exp, 1)
print("RESULTS: ")
print(res)
"solstat": lambda sols: {"best_components": hamming_for_best_components(sols, ms_ideal_ind, os_ideal_ind),
"best_components_itself": best_components_itself(sols),
"best": -1*Utility.makespan(build_schedule(_wf, estimator, rm, max(sols, key=lambda x: x.fitness)))
},
"operators": {
# "choose": default_choose,
"build_solutions": default_build_solutions,
# "fitness": fitness_mapping_and_ordering,
"fitness": overhead_fitness_mapping_and_ordering,
# "assign_credits": default_assign_credits
# "assign_credits": max_assign_credits
"assign_credits": assign_from_transfer_overhead
}
}
return do_experiment(saver, config, _wf, rm, estimator)
if __name__ == "__main__":
res = repeat(do_exp, 1)
print("RESULTS: ")
print(res)
stats.register("min", lambda pop: numpy.min([p.fitness.mofit for p in pop]))
stats.register("avr", lambda pop: numpy.average([p.fitness.mofit for p in pop]))
stats.register("max", lambda pop: numpy.max([p.fitness.mofit for p in pop]))
stats.register("std", lambda pop: numpy.std([p.fitness.mofit for p in pop]))
logbook = Logbook()
logbook.header = ("gen", "G", "kbest", "min", "avr", "max", "std")
pop_size = 40
iter_number = 200
kbest = pop_size
ginit = 2
def do_exp():
pop, _logbook, best = run_gsa(toolbox, stats, logbook, pop_size, iter_number, kbest, ginit)
solution = {MAPPING_SPECIE: list(best.entity.items()), ORDERING_SPECIE: sorted_tasks}
schedule = build_schedule(_wf, estimator, rm, solution)
Utility.validate_static_schedule(_wf, schedule)
makespan = Utility.makespan(schedule)
print("Final makespan: {0}".format(makespan))
print("Final makespan: {0}".format(makespan))
return makespan
if __name__ == "__main__":
result = repeat(do_exp, 1)
print(result)
pass
_wf = wf(wf_names[0])
rm = ExperimentResourceManager(rg.r(PARAMS["nodes_conf"]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0, transf_time_cost=0, transferMx=None,
ideal_flops=PARAMS["ideal_flops"], transfer_time=PARAMS["transfer_time"])
heft_schedule = run_heft(_wf, rm, estimator)
heft_makespan = Utility.makespan(heft_schedule)
overall_transfer = Utility.overall_transfer_time(heft_schedule, _wf, estimator)
overall_execution = Utility.overall_execution_time(heft_schedule)
print("Heft makespan: {0}, Overall transfer time: {1}, Overall execution time: {2}".format(heft_makespan,
overall_transfer,
overall_execution))
if not only_heft:
result = repeat(do_exp_heft_schedule, 1)
mean = numpy.mean(result)
if mean > heft_makespan:
profit = mean / heft_makespan * 100
else:
profit = -heft_makespan / mean * 100
print(result)
print("Heft makespan: {0}, Overall transfer time: {1}, Overall execution time: {2}".format(heft_makespan,
overall_transfer,
overall_execution))
print("Mean: {0}".format(mean))
print("Profit: {0}".format(profit))
heft_particle = generate(_wf, rm, estimator, heft_schedule)
heft_gen = lambda n: [deepcopy(heft_particle) if random.random() > 1.00 else generate(_wf, rm, estimator) for _ in range(n)]
def componoud_update(w, c1, c2, p, best, pop, min=-1, max=1):
mapping_update(w, c1, c2, p.mapping, best.mapping, pop)
ordering_update(w, c1, c2, p.ordering, best.ordering, pop, min=min, max=max)
toolbox = Toolbox()
toolbox.register("population", heft_gen)
toolbox.register("fitness", fitness, _wf, rm, estimator)
toolbox.register("update", componoud_update)
return toolbox
pass
if __name__ == "__main__":
exp = OmpsoBaseExperiment(wf_name="Montage_75",
W=0.1, C1=0.6, C2=0.2,
GEN=300, N=20)
result = repeat(exp, 10)
# result = exp()
sts = interval_statistics(result)
print("Statistics: {0}".format(interval_stat_string(sts)))
#print("Average: {0}".format(numpy.mean(result)))
pass
print("Population size: " + str(PARAMS["ga_params"]["population"]))
_wf = wf(wf_names[0])
rm = ExperimentResourceManager(rg.r(PARAMS["nodes_conf"]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0,
transf_time_cost=0,
transferMx=None,
ideal_flops=PARAMS["ideal_flops"],
transfer_time=PARAMS["transfer_time"])
heft_schedule = run_heft(_wf, rm, estimator)
heft_makespan = Utility.makespan(heft_schedule)
overall_transfer = Utility.overall_transfer_time(heft_schedule, _wf,
estimator)
overall_execution = Utility.overall_execution_time(heft_schedule)
print(
"Heft makespan: {0}, Overall transfer time: {1}, Overall execution time: {2}"
.format(heft_makespan, overall_transfer, overall_execution))
if not only_heft:
result = repeat(do_exp_heft_schedule, 10)
mean = numpy.mean(result)
#profit = (1 - mean / heft_makespan) * 100
print(result)
print(
"Heft makespan: {0}, Overall transfer time: {1}, Overall execution time: {2}"
.format(heft_makespan, overall_transfer, overall_execution))
print("Mean: {0}".format(mean))
#print("Profit: {0}".format(profit))
logbook = tools.Logbook()
logbook.header = ["gen", "T", "val"]
stats = tools.Statistics(lambda ind: ind.energy.values[0])
stats.register("val", lambda arr: arr[0])
def do_exp():
best, log, current = run_sa(
toolbox=toolbox,
logbook=logbook,
stats=stats,
initial_solution=initial_state, T=T, N=N
)
solution = {MAPPING_SPECIE: [item for item in best.mapping.items()], ORDERING_SPECIE: best.ordering}
schedule = build_schedule(_wf, estimator, rm, solution)
Utility.validate_static_schedule(_wf, schedule)
makespan = Utility.makespan(schedule)
heft_makespan = Utility.makespan(heft_schedule)
print("Final makespan: {0}".format(makespan))
print("Heft makespan: {0}".format(heft_makespan))
return makespan
if __name__ == "__main__":
result = repeat(do_exp, 10)
print(result)
print("Mean: {0}".format(numpy.mean(result)))
pass