multiprocessing timestamp takes a lot of time
I created a program that spawn processes and run the provided algorithm. When I run the algorithm without multiprocessing it takes 1.7 sec, but when I spawn two processes that run the same algorithm it takes 18 sec.
Profiler result for one process without spawning:
68050 0.084 0.000 0.084 0.000 {method 'timestamp' of 'datetime.datetime' objects}
With spawning two processes for one of spawned processes:
34025 15.947 0.000 15.947 0.000 {method 'timestamp' of 'datetime.datetime' objects}
Why the date.timestamp takes 15.9 sec? Is this profiler error ?
Spawn:
# Start processes
for index, simulation in enumerate(self.simulations):
proc = Process(target=simulation_runner, args=(simulation, queues[index]))
jobs.append(proc)
proc.start()
Timestamp:
timestamp = math.floor(date.timestamp())
Also I wrote the program that do the same:
import time
from multiprocessing import Pool
from datetime import datetime
def worker(file_name):
with open(file_name, 'r+') as file:
for line in file:
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = ['FLT-TEST', 'FLT-TEST']
pool = Pool()
pool.map(worker, args)
# worker('FLT-TEST')
print(f'Executing in {time.time() - start} s.')
Without spawning it prints Executing in 0.16368794441223145 s.
With spawning it prints Executing in 66.31291604042053 s.
FLT-TEST contains 91849 lines
Without file reading
import time
from multiprocessing import Pool
from datetime import datetime
def worker(num):
print(f'Process {num}')
for _ in range(95000):
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = [1, 2]
pool = Pool()
pool.map(worker, args)
# worker(1)
print(f'Executing in {time.time() - start} s.')
I get the same results.
python multiprocessing
edited Nov 21 '18 at 7:22
martineau
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
add a comment |
I created a program that spawn processes and run the provided algorithm. When I run the algorithm without multiprocessing it takes 1.7 sec, but when I spawn two processes that run the same algorithm it takes 18 sec.
Profiler result for one process without spawning:
68050 0.084 0.000 0.084 0.000 {method 'timestamp' of 'datetime.datetime' objects}
With spawning two processes for one of spawned processes:
34025 15.947 0.000 15.947 0.000 {method 'timestamp' of 'datetime.datetime' objects}
Why the date.timestamp takes 15.9 sec? Is this profiler error ?
Spawn:
# Start processes
for index, simulation in enumerate(self.simulations):
proc = Process(target=simulation_runner, args=(simulation, queues[index]))
jobs.append(proc)
proc.start()
Timestamp:
timestamp = math.floor(date.timestamp())
Also I wrote the program that do the same:
import time
from multiprocessing import Pool
from datetime import datetime
def worker(file_name):
with open(file_name, 'r+') as file:
for line in file:
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = ['FLT-TEST', 'FLT-TEST']
pool = Pool()
pool.map(worker, args)
# worker('FLT-TEST')
print(f'Executing in {time.time() - start} s.')
Without spawning it prints Executing in 0.16368794441223145 s.
With spawning it prints Executing in 66.31291604042053 s.
FLT-TEST contains 91849 lines
Without file reading
import time
from multiprocessing import Pool
from datetime import datetime
def worker(num):
print(f'Process {num}')
for _ in range(95000):
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = [1, 2]
pool = Pool()
pool.map(worker, args)
# worker(1)
print(f'Executing in {time.time() - start} s.')
I get the same results.
python multiprocessing
edited Nov 21 '18 at 7:22
martineau
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
It's probably due to the processing involved of having the OS create and launch a separate task.multiprocessingcan involve a lot of overhead.
– martineau
Nov 21 '18 at 7:26
Comment: You should consider using atime.perf_counter()for timing stuff like this.
– martineau
Nov 21 '18 at 7:31
add a comment |
I created a program that spawn processes and run the provided algorithm. When I run the algorithm without multiprocessing it takes 1.7 sec, but when I spawn two processes that run the same algorithm it takes 18 sec.
Profiler result for one process without spawning:
68050 0.084 0.000 0.084 0.000 {method 'timestamp' of 'datetime.datetime' objects}
With spawning two processes for one of spawned processes:
34025 15.947 0.000 15.947 0.000 {method 'timestamp' of 'datetime.datetime' objects}
Why the date.timestamp takes 15.9 sec? Is this profiler error ?
Spawn:
# Start processes
for index, simulation in enumerate(self.simulations):
proc = Process(target=simulation_runner, args=(simulation, queues[index]))
jobs.append(proc)
proc.start()
Timestamp:
timestamp = math.floor(date.timestamp())
Also I wrote the program that do the same:
import time
from multiprocessing import Pool
from datetime import datetime
def worker(file_name):
with open(file_name, 'r+') as file:
for line in file:
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = ['FLT-TEST', 'FLT-TEST']
pool = Pool()
pool.map(worker, args)
# worker('FLT-TEST')
print(f'Executing in {time.time() - start} s.')
Without spawning it prints Executing in 0.16368794441223145 s.
With spawning it prints Executing in 66.31291604042053 s.
FLT-TEST contains 91849 lines
Without file reading
import time
from multiprocessing import Pool
from datetime import datetime
def worker(num):
print(f'Process {num}')
for _ in range(95000):
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = [1, 2]
pool = Pool()
pool.map(worker, args)
# worker(1)
print(f'Executing in {time.time() - start} s.')
I get the same results.
python multiprocessing
edited Nov 21 '18 at 7:22
martineau
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
I created a program that spawn processes and run the provided algorithm. When I run the algorithm without multiprocessing it takes 1.7 sec, but when I spawn two processes that run the same algorithm it takes 18 sec.
Profiler result for one process without spawning:
68050 0.084 0.000 0.084 0.000 {method 'timestamp' of 'datetime.datetime' objects}
With spawning two processes for one of spawned processes:
34025 15.947 0.000 15.947 0.000 {method 'timestamp' of 'datetime.datetime' objects}
Why the date.timestamp takes 15.9 sec? Is this profiler error ?
Spawn:
# Start processes
for index, simulation in enumerate(self.simulations):
proc = Process(target=simulation_runner, args=(simulation, queues[index]))
jobs.append(proc)
proc.start()
Timestamp:
timestamp = math.floor(date.timestamp())
Also I wrote the program that do the same:
import time
from multiprocessing import Pool
from datetime import datetime
def worker(file_name):
with open(file_name, 'r+') as file:
for line in file:
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = ['FLT-TEST', 'FLT-TEST']
pool = Pool()
pool.map(worker, args)
# worker('FLT-TEST')
print(f'Executing in {time.time() - start} s.')
Without spawning it prints Executing in 0.16368794441223145 s.
With spawning it prints Executing in 66.31291604042053 s.
FLT-TEST contains 91849 lines
Without file reading
import time
from multiprocessing import Pool
from datetime import datetime
def worker(num):
print(f'Process {num}')
for _ in range(95000):
date = datetime.now()
date.timestamp()
if __name__ == "__main__":
start = time.time()
args = [1, 2]
pool = Pool()
pool.map(worker, args)
# worker(1)
print(f'Executing in {time.time() - start} s.')
I get the same results.
python multiprocessing
python multiprocessing
edited Nov 21 '18 at 7:22
martineau
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
edited Nov 21 '18 at 7:22
martineau
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
edited Nov 21 '18 at 7:22
martineau
65.8k989177
edited Nov 21 '18 at 7:22
martineau
65.8k989177
edited Nov 21 '18 at 7:22
martineau
65.8k989177
65.8k989177
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
asked Nov 21 '18 at 6:41
Григорий Дикий
6610
6610
It's probably due to the processing involved of having the OS create and launch a separate task.multiprocessingcan involve a lot of overhead.
– martineau
Nov 21 '18 at 7:26
Comment: You should consider using atime.perf_counter()for timing stuff like this.
– martineau
Nov 21 '18 at 7:31
add a comment |
It's probably due to the processing involved of having the OS create and launch a separate task.multiprocessingcan involve a lot of overhead.
– martineau
Nov 21 '18 at 7:26
Comment: You should consider using atime.perf_counter()for timing stuff like this.
– martineau
Nov 21 '18 at 7:31
It's probably due to the processing involved of having the OS create and launch a separate task.
multiprocessing can involve a lot of overhead.– martineau
Nov 21 '18 at 7:26
It's probably due to the processing involved of having the OS create and launch a separate task.
multiprocessing can involve a lot of overhead.– martineau
Nov 21 '18 at 7:26
Comment: You should consider using a
time.perf_counter() for timing stuff like this.– martineau
Nov 21 '18 at 7:31
Comment: You should consider using a
time.perf_counter() for timing stuff like this.– martineau
Nov 21 '18 at 7:31
add a comment |
1 Answer
1
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timestamp = math.floor(date.replace(tzinfo=timezone.utc).timestamp())
Setting timezone fixed time duration.
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
add a comment |
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timestamp = math.floor(date.replace(tzinfo=timezone.utc).timestamp())
Setting timezone fixed time duration.
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
add a comment |
timestamp = math.floor(date.replace(tzinfo=timezone.utc).timestamp())
Setting timezone fixed time duration.
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
add a comment |
timestamp = math.floor(date.replace(tzinfo=timezone.utc).timestamp())
Setting timezone fixed time duration.
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
timestamp = math.floor(date.replace(tzinfo=timezone.utc).timestamp())
Setting timezone fixed time duration.
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
answered Nov 21 '18 at 8:20
Григорий Дикий
6610
6610
add a comment |
add a comment |
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It's probably due to the processing involved of having the OS create and launch a separate task.
multiprocessingcan involve a lot of overhead.– martineau
Nov 21 '18 at 7:26
Comment: You should consider using a
time.perf_counter()for timing stuff like this.– martineau
Nov 21 '18 at 7:31