def do_exp():
pop, log, best = run_pso(
toolbox=toolbox,
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
def test_fixed_ordering(self):
_wf = wf("Montage_25")
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0,
transf_time_cost=0,
transferMx=None,
ideal_flops=20,
transfer_time=100)
sorted_tasks = HeftHelper.heft_rank(_wf, rm, estimator)
heft_schedule = run_heft(_wf, rm, estimator)
heft_mapping = schedule_to_position(heft_schedule)
heft_gen = lambda: heft_mapping if random.random(
) > 0.95 else generate(_wf, rm, estimator)
toolbox = Toolbox()
# toolbox.register("generate", generate, _wf, rm, estimator)
toolbox.register("generate", heft_gen)
toolbox.register("fitness", fitness, _wf, rm, estimator, sorted_tasks)
toolbox.register("force_vector_matrix", force_vector_matrix, rm)
toolbox.register("velocity_and_position", velocity_and_position, _wf,
rm, estimator)
toolbox.register("G", G)
toolbox.register("kbest", Kbest)
statistics = Statistics()
statistics.register(
"min", lambda pop: numpy.min([p.fitness.mofit for p in pop]))
statistics.register(
"avr", lambda pop: numpy.average([p.fitness.mofit for p in pop]))
statistics.register(
"max", lambda pop: numpy.max([p.fitness.mofit for p in pop]))
statistics.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 = 100
iter_number = 100
kbest = pop_size
ginit = 5
final_pop = run_gsa(toolbox, statistics, logbook, pop_size,
iter_number, kbest, ginit)
best = min(final_pop, key=lambda x: toolbox.fitness(x).mofit)
solution = {
MAPPING_SPECIE: list(zip(sorted_tasks, best)),
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))
pass
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
def do_exp(wf_name):
_wf = wf(wf_name)
heft_schedule = run_heft(_wf, rm, estimator)
Utility.validate_static_schedule(_wf, heft_schedule)
makespan = Utility.makespan(heft_schedule)
return makespan
def do_exp(wf_name):
_wf = wf(wf_name)
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0,
transf_time_cost=0,
transferMx=None,
ideal_flops=20,
transfer_time=100)
empty_fixed_schedule_part = Schedule({node: [] for node in rm.get_nodes()})
heft_schedule = run_heft(_wf, rm, estimator)
ga_functions = GAFunctions2(_wf, rm, estimator)
generate = partial(ga_generate,
ga_functions=ga_functions,
fixed_schedule_part=empty_fixed_schedule_part,
current_time=0.0,
init_sched_percent=0.05,
initial_schedule=heft_schedule)
stats = tools.Statistics(lambda ind: ind.fitness.values[0])
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = ["gen", "evals"] + stats.fields
toolbox = Toolbox()
toolbox.register("generate", generate)
toolbox.register(
"evaluate",
fit_converter(
ga_functions.build_fitness(empty_fixed_schedule_part, 0.0)))
toolbox.register("clone", deepcopy)
toolbox.register("mate", ga_functions.crossover)
toolbox.register("sweep_mutation", ga_functions.sweep_mutation)
toolbox.register("mutate", ga_functions.mutation)
# toolbox.register("select_parents", )
# toolbox.register("select", tools.selTournament, tournsize=4)
toolbox.register("select", tools.selRoulette)
pop, logbook, best = run_ga(toolbox=toolbox,
logbook=logbook,
stats=stats,
**GA_PARAMS)
resulted_schedule = ga_functions.build_schedule(best,
empty_fixed_schedule_part,
0.0)
Utility.validate_static_schedule(_wf, resulted_schedule)
ga_makespan = Utility.makespan(resulted_schedule)
return ga_makespan
def do_exp():
pop, _logbook, best = run_gsa(toolbox, stats, logbook, pop_size, 0, iter_number, None, kbest, ginit, **{"w":W, "c":C})
schedule = build_schedule(_wf, rm, estimator, best)
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
def do_exp(wf_name):
_wf = wf(wf_name)
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0, transf_time_cost=0, transferMx=None,
ideal_flops=20, transfer_time=100)
empty_fixed_schedule_part = Schedule({node: [] for node in rm.get_nodes()})
heft_schedule = run_heft(_wf, rm, estimator)
ga_functions = GAFunctions2(_wf, rm, estimator)
generate = partial(ga_generate, ga_functions=ga_functions,
fixed_schedule_part=empty_fixed_schedule_part,
current_time=0.0, init_sched_percent=0.05,
initial_schedule=heft_schedule)
stats = tools.Statistics(lambda ind: ind.fitness.values[0])
stats.register("avg", numpy.mean)
stats.register("std", numpy.std)
stats.register("min", numpy.min)
stats.register("max", numpy.max)
logbook = tools.Logbook()
logbook.header = ["gen", "evals"] + stats.fields
toolbox = Toolbox()
toolbox.register("generate", generate)
toolbox.register("evaluate", fit_converter(ga_functions.build_fitness(empty_fixed_schedule_part, 0.0)))
toolbox.register("clone", deepcopy)
toolbox.register("mate", ga_functions.crossover)
toolbox.register("sweep_mutation", ga_functions.sweep_mutation)
toolbox.register("mutate", ga_functions.mutation)
# toolbox.register("select_parents", )
# toolbox.register("select", tools.selTournament, tournsize=4)
toolbox.register("select", tools.selRoulette)
pop, logbook, best = run_ga(toolbox=toolbox,
logbook=logbook,
stats=stats,
**GA_PARAMS)
resulted_schedule = ga_functions.build_schedule(best, empty_fixed_schedule_part, 0.0)
Utility.validate_static_schedule(_wf, resulted_schedule)
ga_makespan = Utility.makespan(resulted_schedule)
return ga_makespan
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
def test_fixed_ordering(self):
_wf = wf("Montage_25")
rm = ExperimentResourceManager(rg.r([10, 15, 25, 30]))
estimator = SimpleTimeCostEstimator(comp_time_cost=0, transf_time_cost=0, transferMx=None,
ideal_flops=20, transfer_time=100)
sorted_tasks = HeftHelper.heft_rank(_wf, rm, estimator)
heft_schedule = run_heft(_wf, rm, estimator)
heft_mapping = schedule_to_position(heft_schedule)
heft_gen = lambda: heft_mapping if random.random() > 0.95 else generate(_wf, rm, estimator)
toolbox = Toolbox()
# toolbox.register("generate", generate, _wf, rm, estimator)
toolbox.register("generate", heft_gen)
toolbox.register("fitness", fitness, _wf, rm, estimator, sorted_tasks)
toolbox.register("force_vector_matrix", force_vector_matrix, rm)
toolbox.register("velocity_and_position", velocity_and_position, _wf, rm, estimator)
toolbox.register("G", G)
toolbox.register("kbest", Kbest)
statistics = Statistics()
statistics.register("min", lambda pop: numpy.min([p.fitness.mofit for p in pop]))
statistics.register("avr", lambda pop: numpy.average([p.fitness.mofit for p in pop]))
statistics.register("max", lambda pop: numpy.max([p.fitness.mofit for p in pop]))
statistics.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 = 100
iter_number = 100
kbest = pop_size
ginit = 5
final_pop = run_gsa(toolbox, statistics, logbook, pop_size, iter_number, kbest, ginit)
best = min(final_pop, key=lambda x: toolbox.fitness(x).mofit)
solution = {MAPPING_SPECIE: list(zip(sorted_tasks, best)), 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))
pass
def __call__(self):
toolbox, stats, logbook = self.toolbox(), self.stats(), self.logbook()
_wf, rm, estimator = self.env()
heft_schedule = self.heft_schedule()
pop, log, best = run_gsa(toolbox=toolbox,
logbook=logbook,
statistics=stats,
n=self.N,
iter_number=self.GEN,
kbest=self.KBEST,
ginit=self.G)
schedule = build_schedule(_wf, rm, estimator, best)
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
def __call__(self):
toolbox, stats, logbook = self.toolbox(), self.stats(), self.logbook()
_wf, rm, estimator = self.env()
heft_schedule = self.heft_schedule()
pop, log, best = run_pso(
toolbox=toolbox,
logbook=logbook,
stats=stats,
gen_curr=0, gen_step=self.GEN, invalidate_fitness=True, initial_pop=None,
w=self.W, c1=self.C1, c2=self.C2, n=self.N,
)
schedule = build_schedule(_wf, rm, estimator, best)
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
def do_exp():
pop, log, best = run_pso(
toolbox=toolbox,
logbook=logbook,
stats=stats,
gen_curr=0, gen_step=GEN, invalidate_fitness=True, initial_pop=None,
w=W, c1=C1, c2=C2, n=N,
)
best_position = best.entity
solution = construct_solution(best_position, 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
def __call__(self):
toolbox, stats, logbook = self.toolbox(), self.stats(), self.logbook()
_wf, rm, estimator = self.env()
heft_schedule = self.heft_schedule()
pop, log, best = run_gsa(
toolbox=toolbox,
logbook=logbook,
statistics=stats,
pop_size=self.N,
iter_number=self.GEN,
kbest=self.KBEST,
ginit=self.G
)
schedule = build_schedule(_wf, rm, estimator, best)
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
def _validate(self, wf, estimator, schedule):
max_makespan = Utility.makespan(schedule)
seq_time_validaty = Utility.validateNodesSeq(schedule)
sched = deepcopy(schedule)
mark_finished(sched)
Utility.validate_static_schedule(wf, schedule)
def _validate(self, wf, estimator, schedule):
max_makespan = Utility.makespan(schedule)
seq_time_validaty = Utility.validateNodesSeq(schedule)
sched = deepcopy(schedule)
mark_finished(sched)
Utility.validate_static_schedule(wf, schedule)