How to employ something such as openMP in Cython?
Basically I have a problem that is pretty much embrassing parallel and I think I've hit the limits of how fast I can make it with plain python & multiprocessing so I'm now attempting to take it to a lower level via Cython and hopefully openMP.
So in short I am wondering how I can employ openMP with Cython, or if I'll have to wrap some raw C code and load/bind to it via Cython?
Or can I have Cython compile down to C code then modify the C code to add in the openMP pragmas in then compile to library and load it into Python?
According to the cython wiki, the developers have thought about a variety of options, but I don't believe they have implemented anything yet.
If your problem is embarrassingly parallel, and you already have a multi-processing solution, why not just get each worker process to call some cython code instead of python code?
How to employ something such as openMP in Cython?, Basically I have a problem that is pretty much embrassing parallel and I think I've hit the limits of how fast I can make it with plain python & multiprocessing so I'm� Cython has some support for this compiler extension. But you must be aware that you need to use compilers such as GCC or MSVC, which support OpenMP; Clang/LLVM has no OpenMP support yet. This isn't really a place to explain when and why to use OpenMP since it is really a vast subject, but you should check out the following link:
This question is from 3 years ago and nowadays Cython has available functions that support the OpenMP backend. See for example the documentation here. One very convenient function is the
prange. This is one example of how a (rather naive)
dot function could be implemented using
Don't forget to compile passing the
"/opemmp" argument to the C compiler.
import numpy as np cimport numpy as np import cython from cython.parallel import prange ctypedef np.double_t cDOUBLE DOUBLE = np.float64 def mydot(np.ndarray[cDOUBLE, ndim=2] a, np.ndarray[cDOUBLE, ndim=2] b): cdef np.ndarray[cDOUBLE, ndim=2] c cdef int i, M, N, K c = np.zeros((a.shape, b.shape), dtype=DOUBLE) M = a.shape N = a.shape K = b.shape for i in prange(M, nogil=True): multiply(&a[i,0], &b[0,0], &c[i,0], N, K) return c @cython.wraparound(False) @cython.boundscheck(False) @cython.nonecheck(False) cdef void multiply(double *a, double *b, double *c, int N, int K) nogil: cdef int j, k for j in range(N): for k in range(K): c[k] += a[j]*b[k+j*K]
python, so in short wondering how can employ openmp cython, or if i'll have wrap raw c code , load/bind via cython? or can have cython compile down c code modify c� 3 How to employ something such as openMP in Cython? Aug 22 '13. 2 Interpret string with batch-style environment variables in Powershell Jun 19.
If somebody stumbles over this question:
Now, there is direct support for OpenMP in cython via the cython.parallel module, see http://docs.cython.org/src/userguide/parallelism.html
Using Parallelism — Cython 3.0a6 documentation, Functionality in this module may only be used from the main thread or parallel regions due to OpenMP restrictions. cython.parallel. prange ([start,] stop[, step][,� Cython at a glance¶. Cython is a compiler which compiles Python-like code files to C code. Still, ‘’Cython is not a Python to C translator’‘. That is, it doesn’t take your full program and “turns it into C” – rather, the result makes full use of the Python runtime environment.
I've no experience with OpenMP, but you may have luck with trying zeromq (python bindings included):
Pymp – OpenMP-like Python Programming � ADMIN Magazine, to run computationally intensive code outside of Python with tools such as Cython and ctypes. OpenMP employs a few principles in its programming model. On OSX with MKL-enabled scipy, importing a Cython module that uses OpenMP will cause strange linear algebra behavior. For example, the SVD of a 50x50 identity matrix will have negative values. More specifically, if we do eye = np.eye (51, 51) u, s, vh = scipy.linalg.svd (eye)
This youtube talk by Stefan Behnel, one of the core developers of Cython, will give you an amazing intro. Multithreading of a loop is at the last 30 mins (
prange section). The code is a zipped set of ipython notebooks downloadable here.
In short, write your optimized unthreaded code, optimize with Cython types, and multithread by replacing
range and releasing the GIL.
Cython, Cython is an attempt to make a superset of python which has the high level This codes uses OpenMP multithreading # : also it employs the concept of memory Moreover, we wrote the same thing in pure C and, to our amazement, cython is� Modify cc_exe in numpy/numpy/distutils/intelccompiler.py to be something like: cc_exe = 'icc -O2 -g -openmp -avx' Here, we use default optimizations (-O2), OpenMP threading (-openmp), and Intel® AVX optimizations for Intel® Xeon E5 or E3 Series, which are based on Intel® SandyBridge Architecture (-avx).
the idea is if it's a bit of code that is parallel (with OpenMP in our cython code), we'd like it to behave as BLAS, i.e use as many cores as possible. On the other hand if it's the whole algorithm which is parallel (still OpenMP in our cython code), at the outermost loop, maybe we'd like to provide some control.
FFTW >= 3.3 (lower versions may work) libraries for single, double, and long double precision in serial and multithreading (pthreads or openMP) versions. Cython >= 0.29 (install these as much as possible with your preferred package manager). In practice, pyFFTW may work with older versions of these dependencies, but it is not tested against them.
Cython will generate and compile the rect.cpp file (from the rect.pyx), then it will compile Rectangle.cpp (implementation of the Rectangle class) and link both objects files together into rect.so, which you can then import in Python using import rect (if you forget to link the Rectangle.o, you will get missing symbols while importing the library in Python).
- That's what I did previously, and it works, but I was running into large memory consumption by each process copying the data over... Then I did: stackoverflow.com/questions/4750141/… which improved things via having a shared memory lockfree, but its still too slow. So its time for C I believe.
- In that case you're probably best off writing OpenMP enabled C (or fortran) code. I have found the instructions at crashtestastronomer.wordpress.com/2009/07/30/… work quite well for fortran, you can probably do something similar in C , then wrap it conveniently using cython. I prefer fortran 90 over C because you can write array operations just like you do in python with numpy.
- I've successfully implemented this in C and used cython to link it in.
- @Pharaun can you post snippets as an answer?
- +1 for the code example for @yanlend's answer.
numpy.dot()was faster in my time measurements. You could accept typed memoryviews as an input.
- @J.F.Sebastian thanks, this
dotversions is naive compared to the LAPACK (or similar) routines behind
numpy.dot, but is a good example. I don't believe memory views would be faster than this, have you tried that?
- I'm aware it is naive (it is the first word in
cydot.pyxdescription). Usually parallel computations are used to improve time performance. It is worth mentioning that it is not the case. About typed memoryviews: they produce simpler (no GIL for memoryview indexing, slicing), more general (non-numpy types are also accepted) and sometimes faster code (I haven't checked in this case).
- I've heard good thing about zeromq, should put it on my list of thing to do :) But my problem is I want to avoid interprocess communication because this adds overhead and it explodes memory usage. Which is why I'm wanting to move to openMP/pthreads so I can have a shared data array of numpy arrays (read only)