[Python.NET] Efficient copy of .NET Array to ctypes or numpy array.

[Jeffrey Bush]

I was tempted to code it up, and it turns out you can do it in pure python.

I thought of 4 ways to copy the data: using a for loop (like you did),
using numpy.fromiter, using numpy.fromstring, and using Marshal.Copy.

Obviously the for loop is the slowest. numpy.fromiter is still slow, but
~2.5x faster than the for loop (still has all .NET array checks since this
is in Python and the indexers cannot be optimized away). The last two do
direct memory copies (fromstring gets the memory pointer of the .NET array
while Marshal.Copy gets the memory pointer of the NumPy array). They are
both MUCH faster than a for loop, especially for larger arrays (>200x
faster). numpy.fromstring is faster for smaller arrays, but by the time I
got to 10000000 doubles it was twice as slow as Marshal.Copy.

Here is the code:

import clr
from System import Array, Double, IntPtr, Random
import numpy as np
import time

def check_arrays(a, b):
    if len(a) != len(b): print("Arrays are different size!")
    if any(A != B for A,B in zip(a, b)): print("Arrays have different
values!")

print("Creating source...")
r = Random()
src = Array.CreateInstance(Double, 10000000)
for i in xrange(len(src)): src[i] = r.NextDouble()

print('Copy using for loop'),
start = time.clock()
dest = np.empty(len(src))
for i in xrange(len(src)): dest[i] = src[i]
end = time.clock()
print('in %f sec' % (end-start))
check_arrays(src, dest)

print('Copy using fromiter'),
start = time.clock()
dest = np.fromiter(src, float)
end = time.clock()
print('in %f sec' % (end-start))
check_arrays(src, dest)

print('Copy using fromstring'),
from ctypes import string_at
from System.Runtime.InteropServices import GCHandle, GCHandleType
start = time.clock()
src_hndl = GCHandle.Alloc(src, GCHandleType.Pinned)
try:
    src_ptr = src_hndl.AddrOfPinnedObject().ToInt32()
    dest = np.fromstring(string_at(src_ptr, len(src)*8)) # note: 8 is size
of double...
finally:
    if src_hndl.IsAllocated: src_hndl.Free()
end = time.clock()
print('in %f sec' % (end-start))
check_arrays(src, dest)

print('Copy using Marshal.Copy'),
from System.Runtime.InteropServices import Marshal
start = time.clock()
dest = np.empty(len(src))
Marshal.Copy(src, 0,
IntPtr.__overloads__[int](dest.__array_interface__['data'][0]), len(src))
end = time.clock()
print('in %f sec' % (end-start))
check_arrays(src, dest)

Jeff


On Wed, May 21, 2014 at 10:58 AM, Jeffrey Bush <jeff at coderforlife.com>wrote:

> You could write a .NET function to do this with fixed pointers and
> "memcpy" from the .NET array to the numpy data (the raw data). This would
> be the absolute fastest way, but does involve a number of assumptions (for
> example that the data in the two arrays are laid out in the same way). If
> you want I could probably write something up real quick.
>
> Jeff
>
>
> On Wednesday, May 21, 2014, Brad Friedman <brad at fie.us> wrote:
>
>> An aside that may be useful:
>>
>> .net will skip array bounds checking within simple for-loops, as an
>> optimization. But only if the binaries have all their optimizations turned
>> on. A binary built for debug has them turned off. There is a huge speed up
>> for iterating over an array when these optimizations are used. So make sure
>> you are not looking at a compiler optimization configuration problem.
>>
>> > On May 21, 2014, at 3:21 AM, Dave Cook <daverz at gmail.com> wrote:
>> >
>> > I need to copy a .NET Array (e.g. Double[] or Byte[]) to a numpy array,
>> but it seems the only way to do so is element by element, which is very
>> slow.  Since we are copying a lot of data in real time, it creates a real
>> bottleneck.
>> >
>> > Alternatively, efficient conversion of the .NET array to a Python style
>> byte string would allow numpy.fromstring() to be used for creating the
>> numpy array.
>> >
>> > (I see a similar question went unanswered on the list in August 2011,
>> but I was hoping someone may have figured it out by now.)
>> >
>> > Thanks,
>> > Dave Cook
>> > _________________________________________________
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