What is the recommended way of allocating memory for a typed memory view?
Question:
The Cython documentation on typed memory views list three ways of assigning to a typed memory view:
- from a raw C pointer,
- from a
np.ndarray and
- from a
cython.view.array.
Assume that I don’t have data passed in to my cython function from outside but instead want to allocate memory and return it as a np.ndarray, which of those options do I chose? Also assume that the size of that buffer is not a compile-time constant i.e. I can’t allocate on the stack, but would need to malloc for option 1.
The 3 options would therefore looke something like this:
from libc.stdlib cimport malloc, free
cimport numpy as np
from cython cimport view
np.import_array()
def memview_malloc(int N):
cdef int * m = <int *>malloc(N * sizeof(int))
cdef int[::1] b = <int[:N]>m
free(<void *>m)
def memview_ndarray(int N):
cdef int[::1] b = np.empty(N, dtype=np.int32)
def memview_cyarray(int N):
cdef int[::1] b = view.array(shape=(N,), itemsize=sizeof(int), format="i")
What is surprising to me is that in all three cases, Cython generates quite a lot of code for the memory allocation, in particular a call to __Pyx_PyObject_to_MemoryviewSlice_dc_int. This suggests (and I might be wrong here, my insight into the inner workings of Cython are very limited) that it first creates a Python object and then “casts” it into a memory view, which seems unnecessary overhead.
A simple benchmark doesn’t reveal much difference between the three methods, with 2. being the fastest by a thin margin.
Which of the three methods is recommended? Or is there a different, better option?
Follow-up question: I want to finally return the result as a np.ndarray, after having worked with that memory view in the function. Is a typed memory view the best choice or would I rather just use the old buffer interface as below to create an ndarray in the first place?
cdef np.ndarray[DTYPE_t, ndim=1] b = np.empty(N, dtype=np.int32)
Answers:
Look here for an answer.
The basic idea is that you want cpython.array.array and cpython.array.clone (not cython.array.*):
from cpython.array cimport array, clone
# This type is what you want and can be cast to things of
# the "double[:]" syntax, so no problems there
cdef array[double] armv, templatemv
templatemv = array('d')
# This is fast
armv = clone(templatemv, L, False)
EDIT
It turns out that the benchmarks in that thread were rubbish. Here’s my set, with my timings:
# cython: language_level=3
# cython: boundscheck=False
# cython: wraparound=False
import time
import sys
from cpython.array cimport array, clone
from cython.view cimport array as cvarray
from libc.stdlib cimport malloc, free
import numpy as numpy
cimport numpy as numpy
cdef int loops
def timefunc(name):
def timedecorator(f):
cdef int L, i
print("Running", name)
for L in [1, 10, 100, 1000, 10000, 100000, 1000000]:
start = time.clock()
f(L)
end = time.clock()
print(format((end-start) / loops * 1e6, "2f"), end=" ")
sys.stdout.flush()
print("μs")
return timedecorator
print()
print("INITIALISATIONS")
loops = 100000
@timefunc("cpython.array buffer")
def _(int L):
cdef int i
cdef array[double] arr, template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("cpython.array memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
cdef array template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("cpython.array raw C type")
def _(int L):
cdef int i
cdef array arr, template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("numpy.empty_like memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
template = numpy.empty((L,), dtype='double')
for i in range(loops):
arr = numpy.empty_like(template)
# Prevents dead code elimination
str(arr[0])
@timefunc("malloc")
def _(int L):
cdef int i
cdef double* arrptr
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
free(arrptr)
# Prevents dead code elimination
str(arrptr[0])
@timefunc("malloc memoryview")
def _(int L):
cdef int i
cdef double* arrptr
cdef double[::1] arr
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
arr = <double[:L]>arrptr
free(arrptr)
# Prevents dead code elimination
str(arr[0])
@timefunc("cvarray memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
for i in range(loops):
arr = cvarray((L,),sizeof(double),'d')
# Prevents dead code elimination
str(arr[0])
print()
print("ITERATING")
loops = 1000
@timefunc("cpython.array buffer")
def _(int L):
cdef int i
cdef array[double] arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("cpython.array memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("cpython.array raw C type")
def _(int L):
cdef int i
cdef array arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("numpy.empty_like memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = numpy.empty((L,), dtype='double')
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("malloc")
def _(int L):
cdef int i
cdef double* arrptr = <double*> malloc(sizeof(double) * L)
cdef double d
for i in range(loops):
for i in range(L):
d = arrptr[i]
free(arrptr)
# Prevents dead-code elimination
str(d)
@timefunc("malloc memoryview")
def _(int L):
cdef int i
cdef double* arrptr = <double*> malloc(sizeof(double) * L)
cdef double[::1] arr = <double[:L]>arrptr
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
free(arrptr)
# Prevents dead-code elimination
str(d)
@timefunc("cvarray memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = cvarray((L,),sizeof(double),'d')
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
Output:
INITIALISATIONS
Running cpython.array buffer
0.100040 0.097140 0.133110 0.121820 0.131630 0.108420 0.112160 μs
Running cpython.array memoryview
0.339480 0.333240 0.378790 0.445720 0.449800 0.414280 0.414060 μs
Running cpython.array raw C type
0.048270 0.049250 0.069770 0.074140 0.076300 0.060980 0.060270 μs
Running numpy.empty_like memoryview
1.006200 1.012160 1.128540 1.212350 1.250270 1.235710 1.241050 μs
Running malloc
0.021850 0.022430 0.037240 0.046260 0.039570 0.043690 0.030720 μs
Running malloc memoryview
1.640200 1.648000 1.681310 1.769610 1.755540 1.804950 1.758150 μs
Running cvarray memoryview
1.332330 1.353910 1.358160 1.481150 1.517690 1.485600 1.490790 μs
ITERATING
Running cpython.array buffer
0.010000 0.027000 0.091000 0.669000 6.314000 64.389000 635.171000 μs
Running cpython.array memoryview
0.013000 0.015000 0.058000 0.354000 3.186000 33.062000 338.300000 μs
Running cpython.array raw C type
0.014000 0.146000 0.979000 9.501000 94.160000 916.073000 9287.079000 μs
Running numpy.empty_like memoryview
0.042000 0.020000 0.057000 0.352000 3.193000 34.474000 333.089000 μs
Running malloc
0.002000 0.004000 0.064000 0.367000 3.599000 32.712000 323.858000 μs
Running malloc memoryview
0.019000 0.032000 0.070000 0.356000 3.194000 32.100000 327.929000 μs
Running cvarray memoryview
0.014000 0.026000 0.063000 0.351000 3.209000 32.013000 327.890000 μs
(The reason for the “iterations” benchmark is that some methods have surprisingly different characteristics in this respect.)
In order of initialisation speed:
malloc: This is a harsh world, but it’s fast. If you need to to allocate a lot of things and have unhindered iteration and indexing performance, this has to be it. But normally you’re a good bet for…
cpython.array raw C type: Well damn, it’s fast. And it’s safe. Unfortunately it goes through Python to access its data fields. You can avoid that by using a wonderful trick:
arr.data.as_doubles[i]
which brings it up to the standard speed while removing safety! This makes this a wonderful replacement for malloc, being basically a pretty reference-counted version!
cpython.array buffer: Coming in at only three to four times the setup time of malloc, this is looks a wonderful bet. Unfortunately it has significant overhead (albeit small compared to the boundscheck and wraparound directives). That means it only really competes against full-safety variants, but it is the fastest of those to initialise. Your choice.
cpython.array memoryview: This is now an order of magnitude slower than malloc to initialise. That’s a shame, but it iterates just as fast. This is the standard solution that I would suggest unless boundscheck or wraparound are on (in which case cpython.array buffer might be a more compelling tradeoff).
The rest. The only one worth anything is numpy‘s, due to the many fun methods attached to the objects. That’s it, though.
As a follow up to Veedrac’s answer: be aware using the memoryview support of cpython.array with python 2.7 appears to lead to memory leaks currently. This seems to be a long-standing issue as it is mentioned on the cython-users mailing list here in a post from November 2012. Running Veedrac’s benchmark scrip with Cython version 0.22 with both Python 2.7.6 and Python 2.7.9 leads to a large memory leak on when initialising a cpython.array using either a buffer or memoryview interface. No memory leaks occur when running the script with Python 3.4. I’ve filed a bug report on this to the Cython developers mailing list.
The Cython documentation on typed memory views list three ways of assigning to a typed memory view:
- from a raw C pointer,
- from a
np.ndarrayand - from a
cython.view.array.
Assume that I don’t have data passed in to my cython function from outside but instead want to allocate memory and return it as a np.ndarray, which of those options do I chose? Also assume that the size of that buffer is not a compile-time constant i.e. I can’t allocate on the stack, but would need to malloc for option 1.
The 3 options would therefore looke something like this:
from libc.stdlib cimport malloc, free
cimport numpy as np
from cython cimport view
np.import_array()
def memview_malloc(int N):
cdef int * m = <int *>malloc(N * sizeof(int))
cdef int[::1] b = <int[:N]>m
free(<void *>m)
def memview_ndarray(int N):
cdef int[::1] b = np.empty(N, dtype=np.int32)
def memview_cyarray(int N):
cdef int[::1] b = view.array(shape=(N,), itemsize=sizeof(int), format="i")
What is surprising to me is that in all three cases, Cython generates quite a lot of code for the memory allocation, in particular a call to __Pyx_PyObject_to_MemoryviewSlice_dc_int. This suggests (and I might be wrong here, my insight into the inner workings of Cython are very limited) that it first creates a Python object and then “casts” it into a memory view, which seems unnecessary overhead.
A simple benchmark doesn’t reveal much difference between the three methods, with 2. being the fastest by a thin margin.
Which of the three methods is recommended? Or is there a different, better option?
Follow-up question: I want to finally return the result as a np.ndarray, after having worked with that memory view in the function. Is a typed memory view the best choice or would I rather just use the old buffer interface as below to create an ndarray in the first place?
cdef np.ndarray[DTYPE_t, ndim=1] b = np.empty(N, dtype=np.int32)
Look here for an answer.
The basic idea is that you want cpython.array.array and cpython.array.clone (not cython.array.*):
from cpython.array cimport array, clone
# This type is what you want and can be cast to things of
# the "double[:]" syntax, so no problems there
cdef array[double] armv, templatemv
templatemv = array('d')
# This is fast
armv = clone(templatemv, L, False)
EDIT
It turns out that the benchmarks in that thread were rubbish. Here’s my set, with my timings:
# cython: language_level=3
# cython: boundscheck=False
# cython: wraparound=False
import time
import sys
from cpython.array cimport array, clone
from cython.view cimport array as cvarray
from libc.stdlib cimport malloc, free
import numpy as numpy
cimport numpy as numpy
cdef int loops
def timefunc(name):
def timedecorator(f):
cdef int L, i
print("Running", name)
for L in [1, 10, 100, 1000, 10000, 100000, 1000000]:
start = time.clock()
f(L)
end = time.clock()
print(format((end-start) / loops * 1e6, "2f"), end=" ")
sys.stdout.flush()
print("μs")
return timedecorator
print()
print("INITIALISATIONS")
loops = 100000
@timefunc("cpython.array buffer")
def _(int L):
cdef int i
cdef array[double] arr, template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("cpython.array memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
cdef array template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("cpython.array raw C type")
def _(int L):
cdef int i
cdef array arr, template = array('d')
for i in range(loops):
arr = clone(template, L, False)
# Prevents dead code elimination
str(arr[0])
@timefunc("numpy.empty_like memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
template = numpy.empty((L,), dtype='double')
for i in range(loops):
arr = numpy.empty_like(template)
# Prevents dead code elimination
str(arr[0])
@timefunc("malloc")
def _(int L):
cdef int i
cdef double* arrptr
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
free(arrptr)
# Prevents dead code elimination
str(arrptr[0])
@timefunc("malloc memoryview")
def _(int L):
cdef int i
cdef double* arrptr
cdef double[::1] arr
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
arr = <double[:L]>arrptr
free(arrptr)
# Prevents dead code elimination
str(arr[0])
@timefunc("cvarray memoryview")
def _(int L):
cdef int i
cdef double[::1] arr
for i in range(loops):
arr = cvarray((L,),sizeof(double),'d')
# Prevents dead code elimination
str(arr[0])
print()
print("ITERATING")
loops = 1000
@timefunc("cpython.array buffer")
def _(int L):
cdef int i
cdef array[double] arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("cpython.array memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("cpython.array raw C type")
def _(int L):
cdef int i
cdef array arr = clone(array('d'), L, False)
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("numpy.empty_like memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = numpy.empty((L,), dtype='double')
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
@timefunc("malloc")
def _(int L):
cdef int i
cdef double* arrptr = <double*> malloc(sizeof(double) * L)
cdef double d
for i in range(loops):
for i in range(L):
d = arrptr[i]
free(arrptr)
# Prevents dead-code elimination
str(d)
@timefunc("malloc memoryview")
def _(int L):
cdef int i
cdef double* arrptr = <double*> malloc(sizeof(double) * L)
cdef double[::1] arr = <double[:L]>arrptr
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
free(arrptr)
# Prevents dead-code elimination
str(d)
@timefunc("cvarray memoryview")
def _(int L):
cdef int i
cdef double[::1] arr = cvarray((L,),sizeof(double),'d')
cdef double d
for i in range(loops):
for i in range(L):
d = arr[i]
# Prevents dead-code elimination
str(d)
Output:
INITIALISATIONS
Running cpython.array buffer
0.100040 0.097140 0.133110 0.121820 0.131630 0.108420 0.112160 μs
Running cpython.array memoryview
0.339480 0.333240 0.378790 0.445720 0.449800 0.414280 0.414060 μs
Running cpython.array raw C type
0.048270 0.049250 0.069770 0.074140 0.076300 0.060980 0.060270 μs
Running numpy.empty_like memoryview
1.006200 1.012160 1.128540 1.212350 1.250270 1.235710 1.241050 μs
Running malloc
0.021850 0.022430 0.037240 0.046260 0.039570 0.043690 0.030720 μs
Running malloc memoryview
1.640200 1.648000 1.681310 1.769610 1.755540 1.804950 1.758150 μs
Running cvarray memoryview
1.332330 1.353910 1.358160 1.481150 1.517690 1.485600 1.490790 μs
ITERATING
Running cpython.array buffer
0.010000 0.027000 0.091000 0.669000 6.314000 64.389000 635.171000 μs
Running cpython.array memoryview
0.013000 0.015000 0.058000 0.354000 3.186000 33.062000 338.300000 μs
Running cpython.array raw C type
0.014000 0.146000 0.979000 9.501000 94.160000 916.073000 9287.079000 μs
Running numpy.empty_like memoryview
0.042000 0.020000 0.057000 0.352000 3.193000 34.474000 333.089000 μs
Running malloc
0.002000 0.004000 0.064000 0.367000 3.599000 32.712000 323.858000 μs
Running malloc memoryview
0.019000 0.032000 0.070000 0.356000 3.194000 32.100000 327.929000 μs
Running cvarray memoryview
0.014000 0.026000 0.063000 0.351000 3.209000 32.013000 327.890000 μs
(The reason for the “iterations” benchmark is that some methods have surprisingly different characteristics in this respect.)
In order of initialisation speed:
malloc: This is a harsh world, but it’s fast. If you need to to allocate a lot of things and have unhindered iteration and indexing performance, this has to be it. But normally you’re a good bet for…
cpython.array raw C type: Well damn, it’s fast. And it’s safe. Unfortunately it goes through Python to access its data fields. You can avoid that by using a wonderful trick:
arr.data.as_doubles[i]
which brings it up to the standard speed while removing safety! This makes this a wonderful replacement for malloc, being basically a pretty reference-counted version!
cpython.array buffer: Coming in at only three to four times the setup time of malloc, this is looks a wonderful bet. Unfortunately it has significant overhead (albeit small compared to the boundscheck and wraparound directives). That means it only really competes against full-safety variants, but it is the fastest of those to initialise. Your choice.
cpython.array memoryview: This is now an order of magnitude slower than malloc to initialise. That’s a shame, but it iterates just as fast. This is the standard solution that I would suggest unless boundscheck or wraparound are on (in which case cpython.array buffer might be a more compelling tradeoff).
The rest. The only one worth anything is numpy‘s, due to the many fun methods attached to the objects. That’s it, though.
As a follow up to Veedrac’s answer: be aware using the memoryview support of cpython.array with python 2.7 appears to lead to memory leaks currently. This seems to be a long-standing issue as it is mentioned on the cython-users mailing list here in a post from November 2012. Running Veedrac’s benchmark scrip with Cython version 0.22 with both Python 2.7.6 and Python 2.7.9 leads to a large memory leak on when initialising a cpython.array using either a buffer or memoryview interface. No memory leaks occur when running the script with Python 3.4. I’ve filed a bug report on this to the Cython developers mailing list.