背景
这个
blog
报告的使用速度效益
numpy.fromiter()
结束
numpy.array()
是的。使用提供的脚本作为基础,我希望看到
来自iter的numpy.fromiter()
在执行时
map()
和
submit()
python中的方法
concurrent.futures.ProcessPoolExecutor
上课。
以下是我2秒跑步的结果:
-
很明显
来自iter的numpy.fromiter()
比
numpy.array()
当阵列大小通常为<256时。
-
然而
来自iter的numpy.fromiter()
和
numpy.array()
当由
MAP()
和
提交()
python中的方法
并发.futures.processpoolexecutor
上课。
问题:
不一致和较差的表现
来自iter的numpy.fromiter()
和
numpy.array()
在使用时
MAP()
和
提交()
python中的方法
并发.futures.processpoolexecutor
避免上课?如何改进我的脚本?
下面给出了我用于此基准测试的python脚本。
map():
#!/usr/bin/env python3.5
import concurrent.futures
from itertools import chain
import time
import numpy as np
import pygal
from os import path
list_sizes = [2**x for x in range(1, 11)]
seconds = 2
def test(size_array):
pyarray = [float(x) for x in range(size_array)]
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.fromiter(pyarray, dtype=np.float32, count=size_array)
iterations += 1
fromiter_count = iterations
# array
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.array(pyarray, dtype=np.float32)
iterations += 1
array_count = iterations
#return array_count, fromiter_count
return size_array, array_count, fromiter_count
begin = time.time()
results = {}
with concurrent.futures.ProcessPoolExecutor(max_workers=6) as executor:
data = list(chain.from_iterable(executor.map(test, list_sizes)))
print('data = ', data)
for i in range( 0, len(data), 3 ):
res = tuple(data[i+1:i+3])
size_array = data[i]
results[size_array] = res
print("Result for size {} in {} seconds: {}".format(size_array,seconds,res))
out_folder = path.dirname(path.realpath(__file__))
print("Create diagrams in {}".format(out_folder))
chart = pygal.Line()
chart.title = "Performance in {} seconds".format(seconds)
chart.x_title = "Array size"
chart.y_title = "Iterations"
array_result = []
fromiter_result = []
x_axis = sorted(results.keys())
print(x_axis)
chart.x_labels = x_axis
chart.add('np.array', [results[x][0] for x in x_axis])
chart.add('np.fromiter', [results[x][1] for x in x_axis])
chart.render_to_png(path.join(out_folder, 'result_{}_concurrent_futures_map.png'.format(seconds)))
end = time.time()
compute_time = end - begin
print("Program Time = ", compute_time)
提交():
#!/usr/bin/env python3.5
import concurrent.futures
from itertools import chain
import time
import numpy as np
import pygal
from os import path
list_sizes = [2**x for x in range(1, 11)]
seconds = 2
def test(size_array):
pyarray = [float(x) for x in range(size_array)]
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.fromiter(pyarray, dtype=np.float32, count=size_array)
iterations += 1
fromiter_count = iterations
# array
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.array(pyarray, dtype=np.float32)
iterations += 1
array_count = iterations
return size_array, array_count, fromiter_count
begin = time.time()
results = {}
with concurrent.futures.ProcessPoolExecutor(max_workers=6) as executor:
future_to_size_array = {executor.submit(test, size_array):size_array
for size_array in list_sizes}
data = list(chain.from_iterable(
f.result() for f in concurrent.futures.as_completed(future_to_size_array)))
print('data = ', data)
for i in range( 0, len(data), 3 ):
res = tuple(data[i+1:i+3])
size_array = data[i]
results[size_array] = res
print("Result for size {} in {} seconds: {}".format(size_array,seconds,res))
out_folder = path.dirname(path.realpath(__file__))
print("Create diagrams in {}".format(out_folder))
chart = pygal.Line()
chart.title = "Performance in {} seconds".format(seconds)
chart.x_title = "Array size"
chart.y_title = "Iterations"
x_axis = sorted(results.keys())
print(x_axis)
chart.x_labels = x_axis
chart.add('np.array', [results[x][0] for x in x_axis])
chart.add('np.fromiter', [results[x][1] for x in x_axis])
chart.render_to_png(path.join(out_folder, 'result_{}_concurrent_futures_submitv2.png'.format(seconds)))
end = time.time()
compute_time = end - begin
print("Program Time = ", compute_time)
序列号:(对
original code
)
#!/usr/bin/env python3.5
import time
import numpy as np
import pygal
from os import path
list_sizes = [2**x for x in range(1, 11)]
seconds = 2
def test(size_array):
pyarray = [float(x) for x in range(size_array)]
# fromiter
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.fromiter(pyarray, dtype=np.float32, count=size_array)
iterations += 1
fromiter_count = iterations
# array
start = time.time()
iterations = 0
while time.time() - start <= seconds:
np.array(pyarray, dtype=np.float32)
iterations += 1
array_count = iterations
return array_count, fromiter_count
begin = time.time()
results = {}
for size_array in list_sizes:
res = test(size_array)
results[size_array] = res
print("Result for size {} in {} seconds: {}".format(size_array,seconds,res))
out_folder = path.dirname(path.realpath(__file__))
print("Create diagrams in {}".format(out_folder))
chart = pygal.Line()
chart.title = "Performance in {} seconds".format(seconds)
chart.x_title = "Array size"
chart.y_title = "Iterations"
x_axis = sorted(results.keys())
print(x_axis)
chart.x_labels = x_axis
chart.add('np.array', [results[x][0] for x in x_axis])
chart.add('np.fromiter', [results[x][1] for x in x_axis])
#chart.add('np.array', [x[0] for x in results.values()])
#chart.add('np.fromiter', [x[1] for x in results.values()])
chart.render_to_png(path.join(out_folder, 'result_{}_serial.png'.format(seconds)))
end = time.time()
compute_time = end - begin
print("Program Time = ", compute_time)
