Python Numpy数据类型性能 [英] Python Numpy Data Types Performance
问题描述
所以我做了一些测试,结果很奇怪.
代码:
import numpy as np
import timeit
setup = """
import numpy as np
A = np.ones((1000,1000,3), dtype=datatype)
"""
datatypes = "np.uint8", "np.uint16", "np.uint32", "np.uint64", "np.float16", "np.float32", "np.float64"
stmt1 = """
A = A * 255
A = A / 255
A = A - 1
A = A + 1
"""
#~ np.uint8 : 1.04969205993
#~ np.uint16 : 1.19391073202
#~ np.uint32 : 1.37279821351
#~ np.uint64 : 2.99286961148
#~ np.float16 : 9.62375889588
#~ np.float32 : 0.884994368045
#~ np.float64 : 0.920502625252
stmt2 = """
A *= 255
A /= 255
A -= 1
A += 1
"""
#~ np.uint8 : 0.959514497259
#~ np.uint16 : 0.988570167659
#~ np.uint32 : 0.963571471946
#~ np.uint64 : 2.07768933333
#~ np.float16 : 9.40085450056
#~ np.float32 : 0.882363984225
#~ np.float64 : 0.910147440048
stmt3 = """
A = A * 255 / 255 - 1 + 1
"""
#~ np.uint8 : 1.05919667881
#~ np.uint16 : 1.20249978404
#~ np.uint32 : 1.58037744789
#~ np.uint64 : 3.47520357571
#~ np.float16 : 10.4792515701
#~ np.float32 : 1.29654744484
#~ np.float64 : 1.80735079168
stmt4 = """
A[:,:,:2] *= A[:,:,:2]
"""
#~ np.uint8 : 1.23270964172
#~ np.uint16 : 1.3260807837
#~ np.uint32 : 1.32571002402
#~ np.uint64 : 1.76836543305
#~ np.float16 : 2.83364821535
#~ np.float32 : 1.31282323872
#~ np.float64 : 1.44151875479
stmt5 = """
A[:,:,:2] = A[:,:,:2] * A[:,:,:2]
"""
#~ np.uint8 : 1.38166223494
#~ np.uint16 : 1.49569114821
#~ np.uint32 : 1.53105315419
#~ np.uint64 : 2.03457943366
#~ np.float16 : 3.01117795524
#~ np.float32 : 1.51807271679
#~ np.float64 : 1.7164808877
stmt6 = """
A *= 4
A /= 4
"""
#~ np.uint8 : 0.698176392658
#~ np.uint16 : 0.709560468038
#~ np.uint32 : 0.701653066443
#~ np.uint64 : 1.64199069295
#~ np.float16 : 4.86752675499
#~ np.float32 : 0.421001675475
#~ np.float64 : 0.433056710408
stmt7 = """
np.left_shift(A, 2, A)
np.right_shift(A, 2, A)
"""
#~ np.uint8 : 0.381521115341
#~ np.uint16 : 0.383545967785
#~ np.uint32 : 0.386147272415
#~ np.uint64 : 0.665969478824
for stmt in [stmt1, stmt2, stmt3, stmt4, stmt5, stmt6, stmt7]:
print stmt
for d in datatypes:
s = setup.replace("datatype", d)
T = timeit.Timer(stmt=stmt, setup=s)
print d,":", min(T.repeat(number=30))
print
print
为什么float16这么慢? 为什么float32这么快?它通常比整数运算要快.
如果您有任何相关的性能提示,我将很高兴听到它们.
这是Windows 8 64位上的python 2.6.6 32位. Numpy 1.6的数字与Numpy 1.7相似.现在将测试MKL优化版本: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
edit:事实证明,在某些浮点情况下,MKL版本稍快一些,但对于整数运算,有时会慢很多:
stmt2 = """
A *= 255
A /= 255
A -= 1
A += 1
"""
#np1.6
#~ np.uint8 : 0.959514497259
#~ np.uint16 : 0.988570167659
#~ np.uint32 : 0.963571471946
#~ np.uint64 : 2.07768933333
#~ np.float16 : 9.40085450056
#~ np.float32 : 0.882363984225
#~ np.float64 : 0.910147440048
# np1.7
#~ np.uint8 : 0.979
#~ np.uint16 : 1.010
#~ np.uint32 : 0.972
#~ np.uint64 : 2.081
#~ np.float16 : 9.362
#~ np.float32 : 0.882
#~ np.float64 : 0.918
# np1.7 mkl
#~ np.uint8 : 1.782
#~ np.uint16 : 1.145
#~ np.uint32 : 1.265
#~ np.uint64 : 2.088
#~ np.float16 : 9.029
#~ np.float32 : 0.800
#~ np.float64 : 0.866
我猜
半精度算术(float16)必须由numpy模拟",因为底层C语言中没有相应的类型(并且在相应的处理器说明).另一方面,使用本机数据类型可以非常有效地完成单精度(float32)和双精度(float64)操作.
从单精度运算的良好性能开始:现代处理器具有用于矢量化浮点算术(例如AVX)的高效单元,这也是实现良好的多媒体性能所必需的.
So I did some testing and got odd results.
Code:
import numpy as np
import timeit
setup = """
import numpy as np
A = np.ones((1000,1000,3), dtype=datatype)
"""
datatypes = "np.uint8", "np.uint16", "np.uint32", "np.uint64", "np.float16", "np.float32", "np.float64"
stmt1 = """
A = A * 255
A = A / 255
A = A - 1
A = A + 1
"""
#~ np.uint8 : 1.04969205993
#~ np.uint16 : 1.19391073202
#~ np.uint32 : 1.37279821351
#~ np.uint64 : 2.99286961148
#~ np.float16 : 9.62375889588
#~ np.float32 : 0.884994368045
#~ np.float64 : 0.920502625252
stmt2 = """
A *= 255
A /= 255
A -= 1
A += 1
"""
#~ np.uint8 : 0.959514497259
#~ np.uint16 : 0.988570167659
#~ np.uint32 : 0.963571471946
#~ np.uint64 : 2.07768933333
#~ np.float16 : 9.40085450056
#~ np.float32 : 0.882363984225
#~ np.float64 : 0.910147440048
stmt3 = """
A = A * 255 / 255 - 1 + 1
"""
#~ np.uint8 : 1.05919667881
#~ np.uint16 : 1.20249978404
#~ np.uint32 : 1.58037744789
#~ np.uint64 : 3.47520357571
#~ np.float16 : 10.4792515701
#~ np.float32 : 1.29654744484
#~ np.float64 : 1.80735079168
stmt4 = """
A[:,:,:2] *= A[:,:,:2]
"""
#~ np.uint8 : 1.23270964172
#~ np.uint16 : 1.3260807837
#~ np.uint32 : 1.32571002402
#~ np.uint64 : 1.76836543305
#~ np.float16 : 2.83364821535
#~ np.float32 : 1.31282323872
#~ np.float64 : 1.44151875479
stmt5 = """
A[:,:,:2] = A[:,:,:2] * A[:,:,:2]
"""
#~ np.uint8 : 1.38166223494
#~ np.uint16 : 1.49569114821
#~ np.uint32 : 1.53105315419
#~ np.uint64 : 2.03457943366
#~ np.float16 : 3.01117795524
#~ np.float32 : 1.51807271679
#~ np.float64 : 1.7164808877
stmt6 = """
A *= 4
A /= 4
"""
#~ np.uint8 : 0.698176392658
#~ np.uint16 : 0.709560468038
#~ np.uint32 : 0.701653066443
#~ np.uint64 : 1.64199069295
#~ np.float16 : 4.86752675499
#~ np.float32 : 0.421001675475
#~ np.float64 : 0.433056710408
stmt7 = """
np.left_shift(A, 2, A)
np.right_shift(A, 2, A)
"""
#~ np.uint8 : 0.381521115341
#~ np.uint16 : 0.383545967785
#~ np.uint32 : 0.386147272415
#~ np.uint64 : 0.665969478824
for stmt in [stmt1, stmt2, stmt3, stmt4, stmt5, stmt6, stmt7]:
print stmt
for d in datatypes:
s = setup.replace("datatype", d)
T = timeit.Timer(stmt=stmt, setup=s)
print d,":", min(T.repeat(number=30))
print
print
Why is float16 so slow? Why is float32 so fast? It is often faster than integer ops.
If you have any related performance tips I would be glad to hear them.
This is python 2.6.6 32bit on windows 8 64bit. Numbers for Numpy 1.6, Numpy 1.7 similar. Will test MKL optimized version now: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
edit: turns out the MKL version is slightly faster in some floating point cases but sometimes lots slower for integer ops:
stmt2 = """
A *= 255
A /= 255
A -= 1
A += 1
"""
#np1.6
#~ np.uint8 : 0.959514497259
#~ np.uint16 : 0.988570167659
#~ np.uint32 : 0.963571471946
#~ np.uint64 : 2.07768933333
#~ np.float16 : 9.40085450056
#~ np.float32 : 0.882363984225
#~ np.float64 : 0.910147440048
# np1.7
#~ np.uint8 : 0.979
#~ np.uint16 : 1.010
#~ np.uint32 : 0.972
#~ np.uint64 : 2.081
#~ np.float16 : 9.362
#~ np.float32 : 0.882
#~ np.float64 : 0.918
# np1.7 mkl
#~ np.uint8 : 1.782
#~ np.uint16 : 1.145
#~ np.uint32 : 1.265
#~ np.uint64 : 2.088
#~ np.float16 : 9.029
#~ np.float32 : 0.800
#~ np.float64 : 0.866
Half precision arithmetic (float16) is something which must be "emulated" by numpy I guess, as there are no corresponding types in the underlying C language (and in the appropriate processor instructions) for it. On the other hand, single precision (float32) and double precision (float64) operations can be done very efficiently using native data types.
As of the good performance for single precision operations: Modern processors have efficient units for vectorized floating point arithmetics (e.g. AVX) as it is also needed for good multimedia performance.
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