def GetSemaLock(useglobal=True): """ Return the semaphore and lock objects needed for multi threading.""" global NCORES global MAXCORES global Global_Sema if min(NCORES, MAXCORES)>1: if useglobal: # use a global semaphore if Global_Sema is None: Global_Sema = mpthread.BoundedSemaphore(min(NCORES, MAXCORES)) else: # use a local semaphore at each call to BranchPop sema = mpthread.BoundedSemaphore(min(NCORES, MAXCORES)) lock = mpthread.Lock() Parallel = True else: # If parallel execution is not possible (as just one core could be used), concurrent execution is performed using threads. if useglobal: if Global_Sema is None: Global_Sema = threading.BoundedSemaphore(NCORES) else: sema = threading.BoundedSemaphore(NCORES) lock = threading.Lock() Parallel = False if useglobal: return Global_Sema, lock , Parallel else: return sema, lock , Parallel
def build_db(self, use_symbol):
global pool_sem
pool_sem = multiprocessing.BoundedSemaphore(value=1)
self.create_db()
""" 表functions里面创建了这些字段 project , compiler , optimization , file_name , function_name ,cfg """
"""acfg表： id , acfg .
lstm_cfg表： id, lstm_cfg"""
file_list = self.scan_for_file(self.root_path) #遍历文件获取.o结尾的文件
print('Found ' + str(len(file_list)) + ' during the scan')
file_list = self.remove_override(
file_list) # 去除重复的（去掉新扫描到的文件中包含的数据库中已经存在的文件名）
print('Find ' + str(len(file_list)) + ' files to analyze')
random.shuffle(file_list) #序列中所有元素随机排序
t_args = [(f, self.db_name, use_symbol)
for f in file_list] ##为每个文件创建一个元组（包含文件名，数据库名，对数据库的说明）
# Start a parallel pool to analyze files
p = Pool(processes=1, maxtasksperchild=1) #多进程来处理文件
for _ in tqdm(p.imap_unordered(DatabaseFactory.worker, t_args),
total=len(file_list)):
pass
p.close()
p.join()
def main():
assert len(sys.argv) > 1
in_path = sys.argv[1]
assert in_path.endswith('.in')
IN = open(in_path, 'rt')
os.chdir(os.path.dirname(sys.argv[0]))
T = int(IN.readline())
data = []
for t in range(T): data.append(get_single_test_data(IN))
IN.close()
out_dirname = in_path[:-3]
if os.path.exists(out_dirname): shutil.rmtree(out_dirname)
os.makedirs(out_dirname)
test_filenames = [os.path.join(out_dirname, 'test%.5d.out' % (t + 1)) for t in range(T)]
LOCK = mp.BoundedSemaphore(5)
processes = []
for t in range(T): processes.append(mp.Process(target=solve_single_test, args=(LOCK, t, data[t], test_filenames[t])))
for p in processes: p.start()
for p in processes: p.join()
out_path = '%s.out' % (out_dirname)
FULL_OUT = open(out_path, 'wt')
for t in range(T):
TEST_RESULT = open(test_filenames[t], 'rt')
for l in TEST_RESULT:
FULL_OUT.write(l)
TEST_RESULT.close()
FULL_OUT.close()
def simulate(self, srcPath, nbJobs, args):
if args.count("-q") and nbJobs > 1:
print "Cannot use -q option in simulations when using more than one job."
return;
if not os.path.exists(os.path.join(srcPath, "trace")):
print "Simulation error : trace directory does not exists."
return 2
if not os.listdir(os.path.join(srcPath, "trace")):
print "Simulation error : trace directory is empty."
return 2
self.sema = multiprocessing.BoundedSemaphore(value=nbJobs)
results = multiprocessing.Manager().list()
jobs = [multiprocessing.Process(target=self.simulateMulti, args=(srcPath, processor, results, args))
for processor in self.processors]
for job in jobs: job.start()
for job in jobs: job.join()
retcode = max(results)
if retcode == 2:
raise Exception("Problem during the simulation")
return retcode
def test_partially_pickleables_multiprocessing():
'''
"Partially-pickleable" means an object which is atomically pickleable but
not pickleable.
'''
if not import_tools.exists('multiprocessing'):
raise nose.SkipTest('`multiprocessing` is not installed.')
import multiprocessing
x = PickleableObject()
x.lock = threading.Lock()
partially_pickleables = [
x, [multiprocessing.BoundedSemaphore()], {
1: multiprocessing.Lock(),
2: 3
},
set([multiprocessing.Queue(), x])
]
for thing in partially_pickleables:
assert pickle_tools.is_atomically_pickleable(thing)
assert not is_pickle_successful(thing)
def __init__(self, log_name, experiments, cpu_limit=2**16):
"""
:param experiments: A list of pypuf.experiments.experiment.base.Experiment
:param log_name: A unique file path where to output should be logged.
:param cpu_limit: Maximum number of parallel processes that run experiments.
"""
# Store experiments list
self.experiments = experiments
# Setup logging to both file and console
file_handler = logging.FileHandler(filename='%s.log' % log_name,
mode='w')
file_handler.setLevel(logging.INFO)
stream_handler = logging.StreamHandler()
stream_handler.setLevel(logging.INFO)
self.logger = logging.getLogger(log_name)
self.logger.setLevel(logging.INFO)
self.logger.addHandler(file_handler)
self.logger.addHandler(stream_handler)
# Setup parallel execution limit
self.cpu_limit = min(cpu_limit, multiprocessing.cpu_count())
self.semaphore = multiprocessing.BoundedSemaphore(self.cpu_limit)
def run(self):
if 'remotes' in self._config:
self._remote_pipe(block=False)
if self._server:
threading.Thread(target=self._serve_forever).start()
if self._server_mode == 'idle':
print('[!] going idle mode')
return
# make sure all threads are initialized before start working
init_barrier = multiprocessing.Barrier(len(self._stages) + 1)
thread_sem = multiprocessing.BoundedSemaphore(
multiprocessing.cpu_count())
ps = []
for s in self._stages:
print('[+] intializing process:', s.name)
target = pipeline_stage_worker
if s.get_max_parallel() == 1:
target = no_fork_pipeline_stage_worker
kwargs = {
'init_barrier': init_barrier,
'thread_sem': thread_sem,
'stage': s,
'context': self._context,
}
p = multiprocessing.Process(target=target, kwargs=kwargs)
ps.append(p)
print('[+] starting processes')
[p.start() for p in ps]
init_barrier.wait()
def __init__(self,
interface: str,
wordlist='',
scan_time='7',
time_out='15',
hash_file='hashes.22000',
pot_file='found.potfile'):
self._wlan_mac = ['cat', f'/sys/class/net/{interface}/address']
self._commsupp = [
'timeout', scan_time, 'wpa_supplicant', '-c', 'wpa_supp.conf',
'-i', interface, '-dd'
] # using timeout, just works...
self._commsupp2 = [
'timeout', time_out, 'wpa_supplicant', '-c', 'wpa_supp.conf', '-i',
interface, '-dd'
]
self.hash_file = hash_file
self.pot_file = pot_file
self.wordlist = wordlist
self._ls = LineScrapper()
self.sudo_require = elevator()
try:
self._sem = mp.BoundedSemaphore(value=mp.cpu_count())
except FileNotFoundError:
print(
'Python multiprocessing BoundedSemaphore does not seem to work on your system. Starting '
'capture-only mode ...')
self.wordlist = ''
def build_db(self, use_symbol, depth):
global pool_sem
pool_sem = multiprocessing.BoundedSemaphore(value=1)
instruction_converter = InstructionsConverter('data/i2v/word2id.json')
self.create_db()
file_list = self.scan_for_file(self.root_path)
print('Found ' + str(len(file_list)) + ' during the scan')
file_list = self.remove_override(file_list)
print('Find ' + str(len(file_list)) + ' files to analyze')
random.shuffle(file_list)
t_args = [(f, self.db_name, use_symbol, depth, instruction_converter)
for f in file_list]
# Start a parallel pool to analyze files
p = Pool(processes=None, maxtasksperchild=20)
for _ in tqdm(p.imap_unordered(DatabaseFactory.worker, t_args),
total=len(file_list)):
pass
p.close()
p.join()
def __init__(self, address, handler, bind_and_activate=True):
"""Create server."""
ipv6 = ":" in address[0]
if ipv6:
self.address_family = socket.AF_INET6
# Do not bind and activate, as we might change socket options
super().__init__(address, handler, False)
if ipv6:
# Only allow IPv6 connections to the IPv6 socket
self.socket.setsockopt(socket.IPPROTO_IPV6, socket.IPV6_V6ONLY, 1)
if self.max_connections:
self.connections_guard = multiprocessing.BoundedSemaphore(
self.max_connections)
else:
# use dummy context manager
self.connections_guard = contextlib.ExitStack()
if bind_and_activate:
try:
self.server_bind()
self.server_activate()
except BaseException:
self.server_close()
raise
def _execute(self, deadline):
semaphores = {
k: NopSemaphore()
if v is None else multiprocessing.BoundedSemaphore(v)
for k, v in self.lit_config.parallelism_groups.items()
}
self._increase_process_limit()
# Start a process pool. Copy over the data shared between all test runs.
# FIXME: Find a way to capture the worker process stderr. If the user
# interrupts the workers before we make it into our task callback, they
# will each raise a KeyboardInterrupt exception and print to stderr at
# the same time.
pool = multiprocessing.Pool(self.workers, lit.worker.initialize,
(self.lit_config, semaphores))
self._install_win32_signal_handler(pool)
async_results = [
pool.apply_async(lit.worker.execute,
args=[test],
callback=self._process_completed)
for test in self.tests
]
pool.close()
try:
self._wait_for(async_results, deadline)
except:
pool.terminate()
raise
finally:
pool.join()
def _execute(self, deadline):
self._increase_process_limit()
semaphores = {
k: multiprocessing.BoundedSemaphore(v)
for k, v in self.lit_config.parallelism_groups.items()
if v is not None
}
pool = multiprocessing.Pool(self.workers, lit.worker.initialize,
(self.lit_config, semaphores))
async_results = [
pool.apply_async(lit.worker.execute,
args=[test],
callback=self.progress_callback)
for test in self.tests
]
pool.close()
try:
self._wait_for(async_results, deadline)
except:
pool.terminate()
raise
finally:
pool.join()
def test_non_atomically_pickleables_multiprocessing():
'''
Test `is_atomically_pickleable` on non-atomically pickleable objects.
Not including `multiprocessing` objects.
'''
if not import_tools.exists('multiprocessing'):
raise nose.SkipTest('`multiprocessing` is not installed.')
import multiprocessing
non_pickleables = [
multiprocessing.Lock(),
multiprocessing.BoundedSemaphore(),
multiprocessing.Condition(),
multiprocessing.JoinableQueue(),
multiprocessing.Pool(),
multiprocessing.Queue(),
multiprocessing.RLock(),
multiprocessing.Semaphore(),
]
for thing in non_pickleables:
assert not pickle_tools.is_atomically_pickleable(thing)
assert not is_pickle_successful(thing)
assert not pickle_tools.is_atomically_pickleable(NonPickleableObject())
def __init__(self):
super(FSScanProcessor, self).__init__()
self.eventManager = FSEventManager.instance()
self.settings = Settings.instance()
self.config = Config.instance()
self._logger = logging.getLogger(__name__)
self._logger.setLevel(logging.DEBUG)
self._prefix = None
self._resolution = 16
self._number_of_pictures = 0
self._total = 0
self._laser_positions = 1
self._progress = 0
self._is_color_scan = True
self.point_cloud = None
self.image_task_q = multiprocessing.Queue(self.config.process_numbers +
1)
self.current_position = 0
self._laser_angle = 33.0
self._stop_scan = False
self._current_laser_position = 1
self.semaphore = multiprocessing.BoundedSemaphore()
self.event_q = self.eventManager.get_event_q()
self._worker_pool = FSImageWorkerPool(self.image_task_q, self.event_q)
self.hardwareController = HardwareController.instance()
self.eventManager.subscribe(FSEvents.ON_IMAGE_PROCESSED,
self.image_processed)
self._scan_brightness = self.settings.camera.brightness
self._scan_contrast = self.settings.camera.contrast
self._scan_saturation = self.settings.camera.saturation
def __init__(
self, maxsize=0, encode=pickle.dumps, decode=pickle.loads, bufsize=1024
):
if maxsize <= 0:
maxsize = multiprocessing.synchronize.SEM_VALUE_MAX
self._encode = encode
self._decode = decode
self._bufsize = bufsize
self._sem = multiprocessing.BoundedSemaphore(maxsize)
# Items are enqueued in this buffer before the feeder thread sends them over the pipe
self._buffer = collections.deque()
self._reader, self._writer = Pipe(duplex=False)
# Notify _feeder that an item is in the buffer
self._notempty = threading.Condition()
# _feeder thread
self._thread = None
self._writelock = multiprocessing.Lock()
self._readlock = multiprocessing.Lock()
def __init__(self,
max_workers=None,
mp_context=None,
initializer=None,
initargs=()):
super().__init__(max_workers, mp_context, initializer, initargs)
self.semaphore = multiprocessing.BoundedSemaphore(max_workers)
def _get_worker_sidedata_adder(srcrepo, destrepo):
"""The parallel version of the sidedata computation
This code spawn a pool of worker that precompute a buffer of sidedata
before we actually need them"""
# avoid circular import copies -> scmutil -> worker -> copies
from . import worker
nbworkers = worker._numworkers(srcrepo.ui)
tokens = multiprocessing.BoundedSemaphore(nbworkers * BUFF_PER_WORKER)
revsq = multiprocessing.Queue()
sidedataq = multiprocessing.Queue()
assert srcrepo.filtername is None
# queue all tasks beforehand, revision numbers are small and it make
# synchronisation simpler
#
# Since the computation for each node can be quite expensive, the overhead
# of using a single queue is not revelant. In practice, most computation
# are fast but some are very expensive and dominate all the other smaller
# cost.
for r in srcrepo.changelog.revs():
revsq.put(r)
# queue the "no more tasks" markers
for i in range(nbworkers):
revsq.put(None)
allworkers = []
for i in range(nbworkers):
args = (srcrepo, revsq, sidedataq, tokens)
w = multiprocessing.Process(target=_sidedata_worker, args=args)
allworkers.append(w)
w.start()
# dictionnary to store results for revision higher than we one we are
# looking for. For example, if we need the sidedatamap for 42, and 43 is
# received, when shelve 43 for later use.
staging = {}
def sidedata_companion(revlog, rev):
data = {}, False
if util.safehasattr(revlog, b'filteredrevs'): # this is a changelog
# Is the data previously shelved ?
sidedata = staging.pop(rev, None)
if sidedata is None:
# look at the queued result until we find the one we are lookig
# for (shelve the other ones)
r, data = sidedataq.get()
while r != rev:
staging[r] = data
r, sidedata = sidedataq.get()
tokens.release()
sidedata, has_copies_info = data
new_flag = 0
if has_copies_info:
new_flag = sidedataflag.REVIDX_HASCOPIESINFO
return False, (), sidedata, new_flag, 0
return sidedata_companion
def __init__(self, lit_config, tests):
self.lit_config = lit_config
self.tests = tests
# Set up semaphores to limit parallelism of certain classes of tests.
self.parallelism_semaphores = {
k : NopSemaphore() if v is None else
multiprocessing.BoundedSemaphore(v)
for k, v in lit_config.parallelism_groups.items()}
def read_data(path, var, tests, month, rslt):
threadlock = multiprocessing.BoundedSemaphore(4)
for test in tests:
threadlock.acquire()
t = threading.Thread(target=read_data_thread,
args=(path, var, test, month, rslt, threadlock))
t.start()
t.join()
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
if self.max_connections:
self.connections_guard = multiprocessing.BoundedSemaphore(
self.max_connections)
else:
# use dummy context manager
self.connections_guard = contextlib.ExitStack()
def run_clients(options, db_table_set):
# Spawn one client for each db.table
exit_event = multiprocessing.Event()
processes = []
error_queue = multiprocessing.queues.SimpleQueue()
interrupt_event = multiprocessing.Event()
stream_semaphore = multiprocessing.BoundedSemaphore(options["clients"])
signal.signal(signal.SIGINT, lambda a,b: abort_export(a, b, exit_event, interrupt_event))
try:
progress_info = [ ]
for (db, table) in db_table_set:
progress_info.append((multiprocessing.Value(ctypes.c_longlong, -1),
multiprocessing.Value(ctypes.c_longlong, 0)))
processes.append(multiprocessing.Process(target=export_table,
args=(options["host"],
options["port"],
options["auth_key"],
db, table,
options["directory_partial"],
options["fields"],
options["format"],
error_queue,
progress_info[-1],
stream_semaphore,
exit_event)))
processes[-1].start()
# Wait for all tables to finish
while len(processes) > 0:
time.sleep(0.1)
if not error_queue.empty():
exit_event.set() # Stop rather immediately if an error occurs
processes = [process for process in processes if process.is_alive()]
update_progress(progress_info)
# If we were successful, make sure 100% progress is reported
# (rows could have been deleted which would result in being done at less than 100%)
if error_queue.empty() and not interrupt_event.is_set():
print_progress(1.0)
# Continue past the progress output line
print ""
finally:
signal.signal(signal.SIGINT, signal.SIG_DFL)
if interrupt_event.is_set():
raise RuntimeError("Interrupted")
if not error_queue.empty():
# multiprocessing queues don't handling tracebacks, so they've already been stringified in the queue
while not error_queue.empty():
error = error_queue.get()
print >> sys.stderr, "Traceback: %s" % (error[2])
print >> sys.stderr, "%s: %s" % (error[0].__name__, error[1])
raise RuntimeError("Errors occurred during export")
def __init__(self, name, processors, memories, targetAltera, family, device, package):
self.name = name
self.family = family
self.device = device
self.package = package
self.processors = processors
self.memories = memories
self.targetAltera = targetAltera
self.sema = multiprocessing.BoundedSemaphore(value=1)
def __init__(self, wordlist_name: str, hash_file='hashes.22000', pot_file='found.potfile'):
self.hash_file = fm.try_open_read_file(hash_file)
self.pot_file = pot_file
self._wordlist_name = wordlist_name
try:
self._sem = mp.BoundedSemaphore(value=mp.cpu_count())
except FileNotFoundError:
print('Python multiprocessing BoundedSemaphore does not seem to work on your system. Exiting...')
exit(1)
def __init__(self, lit_config, tests):
self.lit_config = lit_config
self.tests = tests
# Set up semaphores to limit parallelism of certain classes of tests.
# For example, some ASan tests require lots of virtual memory and run
# faster with less parallelism on OS X.
self.parallelism_semaphores = \
{k: multiprocessing.BoundedSemaphore(v) for k, v in
self.lit_config.parallelism_groups.items()}
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"-c",
"--bazel-config",
help=
("Bazel --config flag values to use. "
"Configs are sourced from the .bazelrc defined in the BuildBuddy repo at HEAD (on GitHub) "
"as well as the local ~/.bazelrc."),
action="append",
)
parser.add_argument(
"-p",
"--max-parallel-invocations",
help="Max number of parallel bazel invocations.",
type=int,
default=2,
)
parser.add_argument(
"-n",
"--num-developers",
help="Number of developers in the simulation.",
type=int,
default=5,
)
args = parser.parse_args()
os.chdir(sys.path[0])
data_dir = "/tmp/tmp_buildbuddy_simulate_traffic"
print(f"Data directory: {data_dir}")
if not os.path.exists(data_dir):
sh(f"mkdir -p {data_dir}")
os.chdir(data_dir)
if not os.path.exists("buildbuddy"):
sh("git clone https://github.com/buildbuddy-io/buildbuddy")
bazel_lock = multiprocessing.BoundedSemaphore(
args.max_parallel_invocations)
bazel_configs = args.bazel_config
def spawn_developer(data_dir):
Developer(data_dir, bazel_configs, bazel_lock).develop()
num_developers = args.num_developers
procs = []
for developer_id in range(1, num_developers + 1):
developer_data_dir = os.path.join(data_dir,
f"developer_{developer_id}")
p = multiprocessing.Process(target=spawn_developer,
args=(developer_data_dir, ))
procs.append(p)
p.start()
for p in procs:
p.join()
def InvokeClangPluginForEachFile(
filenames,
plugin,
plugin_args,
arch_cfg,
flags,
clang_bin_dir,
clang_plugins_dir,
):
cmd_line = MakeClangCommandLine(plugin, plugin_args, arch_cfg,
clang_bin_dir, clang_plugins_dir)
verbose = flags["verbose"]
outputs = {}
if flags["sequential"]:
log("** Sequential execution.")
for filename in filenames:
returncode, stdout, stderr = InvokeClangPluginForFile(
filename, cmd_line, verbose)
if returncode != 0:
sys.stderr.write(stderr)
sys.exit(returncode)
outputs[filename] = (stdout, stderr)
else:
log("** Parallel execution.")
cpus = multiprocessing.cpu_count()
pool = multiprocessing.Pool(cpus)
try:
# Track active invokes with a semaphore, to prevent submitting too many
# concurrent invokes to the pool.
execution_slots = multiprocessing.BoundedSemaphore(cpus)
async_outputs = {}
for filename in filenames:
execution_slots.acquire()
def callback(output):
execution_slots.release()
async_outputs[filename] = pool.apply_async(
InvokeClangPluginForFile, (filename, cmd_line, verbose),
callback=callback)
for filename, output in async_outputs.items():
returncode, stdout, stderr = output.get()
if returncode != 0:
sys.stderr.write(stderr)
sys.exit(returncode)
outputs[filename] = (stdout, stderr)
except KeyboardInterrupt as e:
pool.terminate()
pool.join()
raise e
finally:
pool.close()
return outputs
def __init__(self, vecDim, directory, corpusPath, corpusName,
vocabInputFile, vectorInputFile, debug, funcWordFile):
self.vecDim = vecDim
self.corpusPath = directory + corpusPath
self.corpusName = directory + corpusName
self.vocabFile = directory + vocabInputFile
self.vecFile = directory + vectorInputFile
self.debug = debug
self.readVocabFromFile()
self.readVectorFromFile()
self.readFuncWords(funcWordFile)
if debug:
self.sem = multiprocessing.BoundedSemaphore(1)
else:
self.sem = multiprocessing.BoundedSemaphore(
multiprocessing.cpu_count() - 1)
def __init__(self, max_items, **kwargs):
"""Initialize a process pool.
:param max_items: maximum number of simultaneous work items.
Calls to `.submit` will block if there are too many unprocessed
items.
:param kwargs: key-word arguments to `ProcessPoolExecutor`.
"""
super().__init__(**kwargs)
self.semaphore = multiprocessing.BoundedSemaphore(max_items)
def _execute(self, deadline):
semaphores = {
k: NopSemaphore()
if v is None else multiprocessing.BoundedSemaphore(v)
for k, v in self.lit_config.parallelism_groups.items()
}
# Start a process pool. Copy over the data shared between all test runs.
# FIXME: Find a way to capture the worker process stderr. If the user
# interrupts the workers before we make it into our task callback, they
# will each raise a KeyboardInterrupt exception and print to stderr at
# the same time.
pool = multiprocessing.Pool(self.workers, lit.worker.initializer,
(self.lit_config, semaphores))
# Install a console-control signal handler on Windows.
if lit.util.win32api is not None:
def console_ctrl_handler(type):
print('\nCtrl-C detected, terminating.')
pool.terminate()
pool.join()
lit.util.abort_now()
return True
lit.util.win32api.SetConsoleCtrlHandler(console_ctrl_handler, True)
try:
async_results = [
pool.apply_async(
lit.worker.run_one_test,
args=(test, ),
callback=lambda r, t=test: self._consume_test_result(t, r))
for test in self.tests
]
pool.close()
# Wait for all results to come in. The callback that runs in the
# parent process will update the display.
for a in async_results:
timeout = deadline - time.time()
a.wait(timeout)
if not a.successful():
# TODO(yln): this also raises on a --max-time time
a.get() # Exceptions raised here come from the worker.
if self.hit_max_failures:
break
except:
# Stop the workers and wait for any straggling results to come in
# if we exited without waiting on every async result.
pool.terminate()
raise
finally:
pool.join()
def __init__(self, port=9525):
self._data_pipe_r, self._data_pipe_w = multiprocessing.Pipe(False)
self._ack_pipe_r, self._ack_pipe_w = multiprocessing.Pipe(False)
self._semaphore = multiprocessing.BoundedSemaphore(1) # no concurrency allowed
self._server_process = process.ServerProcess("metadata frame net socket reading process",
net_sock_server.frame_net_socket_reader_server,
self._data_pipe_w, self._ack_pipe_r, self._semaphore, port)
ret = self._server_process.start()
if ret:
print("%s is running" % self._server_process.name())
print("net socket based frame reader created, port = %d" % port)