def move_to_next_solution(iteration, tg, ctg, ag, noc_rg, shm, critical_rg,
noncritical_rg, logging):
random_seed = Config.mapping_random_seed
random.seed(Config.mapping_random_seed)
for i in range(0, iteration):
random_seed = random.randint(1, 100000)
random.seed(random_seed)
logging.info("Moving to next solution: random_seed: " + str(random_seed) +
" iteration: " + str(iteration))
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg,
critical_rg, noncritical_rg,
cluster_to_move, current_node,
logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[
destination_node]['Region']:
destination_node = random.choice(ag.nodes())
try_counter = 0
while not Mapping_Functions.add_cluster_to_node(
tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, destination_node, logging):
# If add_cluster_to_node fails it automatically removes all the connections...
# we need to add the cluster to the old place...
Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg,
critical_rg, noncritical_rg,
cluster_to_move, current_node,
logging)
try_counter += 1
if try_counter >= 3 * len(ag.nodes()):
print(
"CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH..."
)
return tg, ctg, ag
# choosing another cluster to move
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg,
critical_rg, noncritical_rg,
cluster_to_move,
current_node, logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[
destination_node]['Region']:
destination_node = random.choice(ag.nodes())
return tg, ctg, ag
def move_to_next_solution(iteration, tg, ctg, ag, noc_rg, shm, critical_rg, noncritical_rg, logging):
random_seed = Config.mapping_random_seed
random.seed(Config.mapping_random_seed)
for i in range(0, iteration):
random_seed = random.randint(1, 100000)
random.seed(random_seed)
logging.info("Moving to next solution: random_seed: "+str(random_seed)+" iteration: "+str(iteration))
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(ag.nodes())
try_counter = 0
while not Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, destination_node, logging):
# If add_cluster_to_node fails it automatically removes all the connections...
# we need to add the cluster to the old place...
Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
try_counter += 1
if try_counter >= 3*len(ag.nodes()):
print ("CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH...")
return tg, ctg, ag
# choosing another cluster to move
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(ag.nodes())
return tg, ctg, ag
def mapping_opt_local_search(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg, shm,
iteration_num, report, detailed_report, logging,
cost_data_file_name, mapping_process_file_name, random_seed,
initial_mapping_string=None):
random.seed(random_seed)
if report:
print("===========================================")
print("STARTING MAPPING OPTIMIZATION...USING LOCAL SEARCH...")
print("NUMBER OF ITERATIONS: "+str(iteration_num))
if type(cost_data_file_name) is str:
mapping_cost_file = open('Generated_Files/Internal/'+cost_data_file_name+'.txt', 'a')
else:
raise ValueError("cost_data_file_name name is not string: "+str(cost_data_file_name))
if type(mapping_process_file_name) is str:
mapping_process_file = open('Generated_Files/Internal/'+mapping_process_file_name+'.txt', 'a')
else:
raise ValueError("mapping_process_file name is not string: "+str(mapping_process_file_name))
best_tg = copy.deepcopy(tg)
best_ag = copy.deepcopy(ag)
best_ctg = copy.deepcopy(ctg)
best_cost = Mapping_Functions.mapping_cost_function(tg, ag, shm, False, initial_mapping_string=initial_mapping_string)
starting_cost = best_cost
for iteration in range(0, iteration_num):
logging.info(" ITERATION:"+str(iteration))
cluster_to_move = random.choice(list(ctg.nodes()))
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(list(ag.nodes()))
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(list(ag.nodes()))
# print(ctg.node[cluster_to_move]['Criticality'],AG.node[destination_node]['Region'])
try_counter = 0
while not Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, destination_node, logging):
# If add_cluster_to_node fails it automatically removes all the connections...
# we need to add the cluster to the old place...
Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
# choosing another cluster to move
cluster_to_move = random.choice(list(ctg.nodes()))
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(list(ag.nodes()))
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(list(ag.nodes()))
# print(ctg.node[cluster_to_move]['Criticality'],AG.node[destination_node]['Region'])
if try_counter >= 3*len(ag.nodes()):
if report:
print("CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH...")
logging.info("CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH...")
tg = copy.deepcopy(best_tg)
ag = copy.deepcopy(best_ag)
ctg = copy.deepcopy(ctg)
if report:
Scheduling_Reports.report_mapped_tasks(ag, logging)
Mapping_Functions.mapping_cost_function(tg, ag, shm, True, initial_mapping_string=initial_mapping_string)
return best_tg, best_ctg, best_ag
try_counter += 1
Scheduling_Functions.clear_scheduling(ag)
Scheduler.schedule_all(tg, ag, shm, False, logging)
current_cost = Mapping_Functions.mapping_cost_function(tg, ag, shm, detailed_report, initial_mapping_string= initial_mapping_string)
mapping_process_file.write(Mapping_Functions.mapping_into_string(tg)+"\n")
mapping_cost_file.write(str(current_cost)+"\n")
if current_cost <= best_cost:
if current_cost < best_cost:
if report:
print("\033[32m* NOTE::\033[0mBETTER SOLUTION FOUND WITH COST: "+str(current_cost) +
"\t ITERATION:"+str(iteration))
logging.info("NOTE:: MOVED TO SOLUTION WITH COST: "+str(current_cost)+"ITERATION: "+str(iteration))
else:
logging.info("NOTE:: MOVED TO SOLUTION WITH COST: "+str(current_cost)+"ITERATION: "+str(iteration))
best_tg = copy.deepcopy(tg)
best_ag = copy.deepcopy(ag)
best_ctg = copy.deepcopy(ctg)
best_cost = current_cost
else:
tg = copy.deepcopy(best_tg)
ag = copy.deepcopy(best_ag)
ctg = copy.deepcopy(best_ctg)
mapping_process_file.write(Mapping_Functions.mapping_into_string(tg)+"\n")
Scheduling_Functions.clear_scheduling(ag)
Scheduler.schedule_all(tg, ag, shm, False, logging)
mapping_process_file.close()
mapping_cost_file.close()
if report:
print("-------------------------------------")
print("STARTING COST: "+str(starting_cost)+"\tFINAL COST: "+str(best_cost) +
"\tAFTER "+str(iteration_num)+" ITERATIONS")
print("IMPROVEMENT:"+str("{0:.2f}".format(100*(starting_cost-best_cost)/starting_cost))+" %")
return best_tg, best_ctg, best_ag
def mapping_opt_local_search(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg, shm,
iteration_num, report, detailed_report, logging,
cost_data_file_name, mapping_process_file_name, random_seed,
initial_mapping_string=None):
random.seed(random_seed)
if report:
print ("===========================================")
print ("STARTING MAPPING OPTIMIZATION...USING LOCAL SEARCH...")
print ("NUMBER OF ITERATIONS: "+str(iteration_num))
if type(cost_data_file_name) is str:
mapping_cost_file = open('Generated_Files/Internal/'+cost_data_file_name+'.txt', 'a')
else:
raise ValueError("cost_data_file_name name is not string: "+str(cost_data_file_name))
if type(mapping_process_file_name) is str:
mapping_process_file = open('Generated_Files/Internal/'+mapping_process_file_name+'.txt', 'a')
else:
raise ValueError("mapping_process_file name is not string: "+str(mapping_process_file_name))
best_tg = copy.deepcopy(tg)
best_ag = copy.deepcopy(ag)
best_ctg = copy.deepcopy(ctg)
best_cost = Mapping_Functions.mapping_cost_function(tg, ag, shm, False, initial_mapping_string=initial_mapping_string)
starting_cost = best_cost
for iteration in range(0, iteration_num):
logging.info(" ITERATION:"+str(iteration))
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(ag.nodes())
# print (ctg.node[cluster_to_move]['Criticality'],AG.node[destination_node]['Region'])
try_counter = 0
while not Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, destination_node, logging):
# If add_cluster_to_node fails it automatically removes all the connections...
# we need to add the cluster to the old place...
Mapping_Functions.add_cluster_to_node(tg, ctg, ag, shm, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
# choosing another cluster to move
cluster_to_move = random.choice(ctg.nodes())
current_node = ctg.node[cluster_to_move]['Node']
Mapping_Functions.remove_cluster_from_node(tg, ctg, ag, noc_rg, critical_rg, noncritical_rg,
cluster_to_move, current_node, logging)
destination_node = random.choice(ag.nodes())
if Config.EnablePartitioning:
while ctg.node[cluster_to_move]['Criticality'] != ag.node[destination_node]['Region']:
destination_node = random.choice(ag.nodes())
# print (ctg.node[cluster_to_move]['Criticality'],AG.node[destination_node]['Region'])
if try_counter >= 3*len(ag.nodes()):
if report:
print ("CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH...")
logging.info("CAN NOT FIND ANY FEASIBLE SOLUTION... ABORTING LOCAL SEARCH...")
tg = copy.deepcopy(best_tg)
ag = copy.deepcopy(best_ag)
ctg = copy.deepcopy(ctg)
if report:
Scheduling_Reports.report_mapped_tasks(ag, logging)
Mapping_Functions.mapping_cost_function(tg, ag, shm, True, initial_mapping_string=initial_mapping_string)
return best_tg, best_ctg, best_ag
try_counter += 1
Scheduling_Functions.clear_scheduling(ag)
Scheduler.schedule_all(tg, ag, shm, False, logging)
current_cost = Mapping_Functions.mapping_cost_function(tg, ag, shm, detailed_report, initial_mapping_string= initial_mapping_string)
mapping_process_file.write(Mapping_Functions.mapping_into_string(tg)+"\n")
mapping_cost_file.write(str(current_cost)+"\n")
if current_cost <= best_cost:
if current_cost < best_cost:
if report:
print ("\033[32m* NOTE::\033[0mBETTER SOLUTION FOUND WITH COST: "+str(current_cost) +
"\t ITERATION:"+str(iteration))
logging.info("NOTE:: MOVED TO SOLUTION WITH COST: "+str(current_cost)+"ITERATION: "+str(iteration))
else:
logging.info("NOTE:: MOVED TO SOLUTION WITH COST: "+str(current_cost)+"ITERATION: "+str(iteration))
best_tg = copy.deepcopy(tg)
best_ag = copy.deepcopy(ag)
best_ctg = copy.deepcopy(ctg)
best_cost = current_cost
else:
tg = copy.deepcopy(best_tg)
ag = copy.deepcopy(best_ag)
ctg = copy.deepcopy(best_ctg)
mapping_process_file.write(Mapping_Functions.mapping_into_string(tg)+"\n")
Scheduling_Functions.clear_scheduling(ag)
Scheduler.schedule_all(tg, ag, shm, False, logging)
mapping_process_file.close()
mapping_cost_file.close()
if report:
print ("-------------------------------------")
print ("STARTING COST: "+str(starting_cost)+"\tFINAL COST: "+str(best_cost) +
"\tAFTER "+str(iteration_num)+" ITERATIONS")
print ("IMPROVEMENT:"+str("{0:.2f}".format(100*(starting_cost-best_cost)/starting_cost))+" %")
return best_tg, best_ctg, best_ag
