They're generated using a string-substitution tool (and thus hard to
search for). But the code is
https://github.com/cython/cython/blob/8609e0fa7d361f1392823ff6e1a618720cd62df3/Cython/Utility/MemoryView_C.c#L130.
I found it by searching for "ObjectToMemviewSlice ", which appears just
above the prototype in the generated C code.
My impression is that creating memoryview slices isn't always hugely
quick - I think the old np.ndarray syntax can be a little quicker to
create (although it's worse in other ways)
On 21/03/2021 18:23, Golden Rockefeller wrote:
I know there has been some work last year at improving the overhead in
creating cython.view.arrays (cvarray). But I have noticed a large
overhead in converting that cvarray to a memoryview (e.g double[:]).
I would like some advice in inspecting cython's source code.
For example, where can I find
"__Pyx_PyObject_to_MemoryviewSlice_ds_double"?
Through testing, my guess is that this function introduces most of the
overhead:
"""
@timefunc("cvarray_to_memoryview")
def _(Py_ssize_t L):
cdef Py_ssize_t i
cdef double* arrptr
cdef cvarray cvarr
cdef double[:] arr
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
cvarr = cvarray((L,),sizeof(double),'d', mode="c", allocate_buffer=False)
cvarr.callback_free_data = free
cvarr.data = <char *> arrptr
arr = cvarr # Only difference is this conversion
# Prevents dead code elimination
str(arr[0])
@timefunc("cvarray")
def _(Py_ssize_t L):
cdef Py_ssize_t i
cdef cvarray cvarr = cvarray((L,),sizeof(double),'d')
for i in range(loops):
arrptr = <double*> malloc(sizeof(double) * L)
cvarr = cvarray((L,),sizeof(double),'d', mode="c", allocate_buffer=False)
cvarr.callback_free_data = free
cvarr.data = <char *> arrptr
# Prevents dead code elimination
str(cvarr[0])
"""
Timings (loops = 100000; L in [1, 10, 100, 1000, 10000])
Running cvarray_to_memoryview
1.555391 1.510217 1.518601 1.503522 1.733853 μs
Running cvarray
0.342308 0.353832 0.335155 0.361952 0.518414 μs
Golden Rockefeller
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