Raul, This is *fantastic work*. While many optimizations were done 6 years ago as people started to convert their code, that kind of report has trailed off in the last few years. I have not seen this kind of speed-comparison for some time --- but I think it's definitely beneficial.
NumPy still has quite a bit that can be optimized. I think your example is really great. Perhaps it's worth making a C-API macro out of the short-cut to the attribute string so it can be used by others. It would be interesting to see where your other slow-downs are. I would be interested to see if the slow-math of float64 is hurting you. It would be possible, for example, to do a simple subclass of the ndarray that overloads a[<integer>] to be the same as array.item(<integer>). The latter syntax returns python objects (i.e. floats) instead of array scalars. Also, it would not be too difficult to add fast-math paths for int64, float32, and float64 scalars (so they don't go through ufuncs but do scalar-math like the float and int objects in Python. A related thing we've been working on lately which might help you is Numba which might help speed up functions that have code like: "a[0] < 4" : http://numba.pydata.org. Numba will translate the expression a[0] < 4 to a machine-code address-lookup and math operation which is *much* faster when a is a NumPy array. Presently this requires you to wrap your function call in a decorator: from numba import autojit @autojit def function_to_speed_up(...): pass In the near future (2-4 weeks), numba will grow the experimental ability to basically replace all your function calls with @autojit versions in a Python function. I would love to see something like this work: python -m numba filename.py To get an effective autojit on all the filename.py functions (and optionally on all python modules it imports). The autojit works out of the box today --- you can get Numba from PyPI (or inside of the completely free Anaconda CE) to try it out. Best, -Travis On Dec 2, 2012, at 7:28 PM, Raul Cota wrote: > Hello, > > First a quick summary of my problem and at the end I include the basic > changes I am suggesting to the source (they may benefit others) > > I am ages behind in times and I am still using Numeric in Python 2.2.3. > The main reason why it has taken so long to upgrade is because NumPy > kills performance on several of my tests. > > I am sorry if this topic has been discussed before. I tried parsing the > mailing list and also google and all I found were comments related to > the fact that such is life when you use NumPy for small arrays. > > In my case I have several thousands of lines of code where data > structures rely heavily on Numeric arrays but it is unpredictable if the > problem at hand will result in large or small arrays. Furthermore, once > the vectorized operations complete, the values could be assigned into > scalars and just do simple math or loops. I am fairly sure the core of > my problems is that the 'float64' objects start propagating all over the > program data structures (not in arrays) and they are considerably slower > for just about everything when compared to the native python float. > > Conclusion, it is not practical for me to do a massive re-structuring of > code to improve speed on simple things like "a[0] < 4" (assuming "a" is > an array) which is about 10 times slower than "b < 4" (assuming "b" is a > float) > > > I finally decided to track down the problem and I started by getting > Python 2.6 from source and profiling it in one of my cases. By far the > biggest bottleneck came out to be PyString_FromFormatV which is a > function to assemble a string for a Python error caused by a failure to > find an attribute when "multiarray" calls PyObject_GetAttrString. This > function seems to get called way too often from NumPy. The real > bottleneck of trying to find the attribute when it does not exist is not > that it fails to find it, but that it builds a string to set a Python > error. In other words, something as simple as "a[0] < 3.5" internally > result in a call to set a python error . > > I downloaded NumPy code (for Python 2.6) and tracked down all the calls > like this, > > ret = PyObject_GetAttrString(obj, "__array_priority__"); > > and changed to > if (PyList_CheckExact(obj) || (Py_None == obj) || > PyTuple_CheckExact(obj) || > PyFloat_CheckExact(obj) || > PyInt_CheckExact(obj) || > PyString_CheckExact(obj) || > PyUnicode_CheckExact(obj)){ > //Avoid expensive calls when I am sure the attribute > //does not exist > ret = NULL; > } > else{ > ret = PyObject_GetAttrString(obj, "__array_priority__"); > > > > ( I think I found about 7 spots ) > > > I also noticed (not as bad in my case) that calls to PyObject_GetBuffer > also resulted in Python errors being set thus unnecessarily slower code. > > > With this change, something like this, > for i in xrange(1000000): > if a[1] < 35.0: > pass > > went down from 0.8 seconds to 0.38 seconds. > > A bogus test like this, > for i in xrange(1000000): > a = array([1., 2., 3.]) > > went down from 8.5 seconds to 2.5 seconds. > > > > Altogether, these simple changes got me half way to the speed I used to > get in Numeric and I could not see any slow down in any of my cases that > benefit from heavy array manipulation. I am out of ideas on how to > improve further though. > > Few questions: > - Is there any interest for me to provide the exact details of the code > I changed ? > > - I managed to compile NumPy through setup.py but I am not sure how to > force it to generate pdb files from my Visual Studio Compiler. I need > the pdb files such that I can run my profiler on NumPy. Anybody has any > experience with this ? (Visual Studio) > > - The core of my problems I think boil down to things like this > s = a[0] > assigning a float64 into s as opposed to a native float ? > Is there any way to hack code to change it to extract a native float > instead ? (probably crazy talk, but I thought I'd ask :) ). > I'd prefer to not use s = a.item(0) because I would have to change too > much code and it is not even that much faster. For example, > for i in xrange(1000000): > if a.item(1) < 35.0: > pass > is 0.23 seconds (as opposed to 0.38 seconds with my suggested changes) > > > I apologize again if this topic has already been discussed. > > > Regards, > > Raul > > > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
