def single_thread():
print 'single_thread start: %s' % seconds_to_human(time.time())
for i in xrange(START, STOP, STEP):
print sum_prime(i)
print 'single_thread end: %s' % seconds_to_human(time.time())
def single_thread():
print 'single_thread start: %s' % seconds_to_human(time.time())
for i in xrange(START, STOP, STEP):
print sum_prime(i)
print 'single_thread end: %s' % seconds_to_human(time.time())
def single_sum_zero():
start_time = seconds_to_human(time.time())
count = 0
for step in xrange(len(test_list)):
for i in xrange(len(test_list) - step):
if sum(test_list[i:i + step + 1]) == 0: count += 1
print 'single_sum_zero count: %s' % count
print 'single_sum_zero start: %s' % start_time
print 'single_sum_zero end: %s' % seconds_to_human(time.time())
def single_sum_zero():
start_time = seconds_to_human(time.time())
count = 0
for step in xrange(len(test_list)):
for i in xrange(len(test_list) - step):
if sum(test_list[i:i+step+1]) == 0: count += 1
print 'single_sum_zero count: %s' % count
print 'single_sum_zero start: %s' % start_time
print 'single_sum_zero end: %s' % seconds_to_human(time.time())
def multi_thread():
print 'multi_thread start: %s' % seconds_to_human(time.time())
work_queue = Queue()
for i in xrange(START, STOP, STEP):
work_queue.put(i)
threads = [Thread(target=do_work, args=(work_queue, )) for i in range(8)]
[t.start() for t in threads]
[t.join() for t in threads]
print 'multi_thread end: %s' % seconds_to_human(time.time())
def multi_process():
print 'multi_process start: %s' % seconds_to_human(time.time())
work_queue = multiprocessing.Queue()
for i in xrange(START, STOP, STEP):
work_queue.put(i)
processes = [multiprocessing.Process(target=do_work, args=(work_queue, )) for i in range(8)]
[t.start() for t in processes]
[t.join() for t in processes]
print 'multi_process end: %s' % seconds_to_human(time.time())
def multi_thread():
print 'multi_thread start: %s' % seconds_to_human(time.time())
work_queue = Queue()
for i in xrange(START, STOP, STEP):
work_queue.put(i)
threads = [Thread(target=do_work, args=(work_queue, )) for i in range(8)]
[t.start() for t in threads]
[t.join() for t in threads]
print 'multi_thread end: %s' % seconds_to_human(time.time())
def multiprocess_sum_zero():
start_time = seconds_to_human(time.time())
step_queue = multiprocessing.Queue()
for i in xrange(len(test_list) - 1):
step_queue.put(i)
processes = [multiprocessing.Process(target=sum_zero, args=(step_queue, )) for i in range(8)]
[p.start() for p in processes]
[p.join() for p in processes]
print 'multi_sum_zero start: %s' % start_time
print 'multi_sum_zero end: %s' % seconds_to_human(time.time())
def multi_process():
print 'multi_process start: %s' % seconds_to_human(time.time())
work_queue = multiprocessing.Queue()
for i in xrange(START, STOP, STEP):
work_queue.put(i)
processes = [
multiprocessing.Process(target=do_work, args=(work_queue, ))
for i in range(8)
]
[t.start() for t in processes]
[t.join() for t in processes]
print 'multi_process end: %s' % seconds_to_human(time.time())
def multiprocess_sum_zero():
start_time = seconds_to_human(time.time())
step_queue = multiprocessing.Queue()
for i in xrange(len(test_list) - 1):
step_queue.put(i)
processes = [
multiprocessing.Process(target=sum_zero, args=(step_queue, ))
for i in range(8)
]
[p.start() for p in processes]
[p.join() for p in processes]
print 'multi_sum_zero start: %s' % start_time
print 'multi_sum_zero end: %s' % seconds_to_human(time.time())
def multiprocess_pool_sum_zero():
result = []
count = 0
start_time = seconds_to_human(time.time())
p = multiprocessing.Pool(8)
for i in xrange(len(test_list) - 1):
result.append(p.apply_async(pool_sum_zero, (i, ))) # async实现异步, apply和map区别在于: apply可以多个函数, map同一个函数和iterator
p.close()
p.join()
for res in result:
count += res.get()
print 'pool_sum_zero count: %s' % count
print 'pool_sum_zero start: %s' % start_time
print 'pool_sum_zero end: %s' % seconds_to_human(time.time())
def multiprocess_pool_sum_zero():
result = []
count = 0
start_time = seconds_to_human(time.time())
p = multiprocessing.Pool(8)
for i in xrange(len(test_list) - 1):
result.append(p.apply_async(
pool_sum_zero,
(i, ))) # async实现异步, apply和map区别在于: apply可以多个函数, map同一个函数和iterator
p.close()
p.join()
for res in result:
count += res.get()
print 'pool_sum_zero count: %s' % count
print 'pool_sum_zero start: %s' % start_time
print 'pool_sum_zero end: %s' % seconds_to_human(time.time())