Stef Mientki wrote: > MatLab: 14 msec > Python: 2 msec
I have the same experience. NumPy is usually faster than Matlab. But it very much depends on how the code is structured. I wonder if it is possible to improve the performance of NumPy by having its fundamental types in the language, instead of depending on operator overloading. For example, in NumPy, a statement like array3[:] = array1[:] + array2[:] allocates an intermediate array that is not needed. This is because the operator overloading cannot know if it's evaluating a part of a larger statement like array1[:] = (array1[:] + array2[:]) * (array3[:] + array4[:]) If arrays had been a part of the language, as it is in Matlab and Fortran 95, the compiler could see this and avoid intermediate storage, as well as looping over the data only once. This is one of the main reasons why Fortran is better than C++ for scientific computing. I.e. instead of for (i=0; i<n; i++) array1[i] = (array1[i] + array2[i]) * (array3[i] + array4[i]); one actually gets something like three intermediates and four loops: tmp1 = malloc(n*sizeof(whatever)); for (i=0; i<n; i++) tmp1[i] = array1[i] + array2[i]; tmp2 = malloc(n*sizeof(whatever)); for (i=0; i<n; i++) tmp2[i] = array3[i] + array4[i]; tmp3 = malloc(n*sizeof(whatever)); for (i=0; i<n; i++) tmp3[i] = tmp1[i] + tmp2[i]; free(tmp1); free(tmp2); for (i=0; i<n; i++) array1[i] = tmp3[i]; free(tmp3); In C++ this is actually further bloated by constructor, destructor and copyconstructor calls. Why one should use Fortran over C++ is obvious. But it also applies to NumPy, and also to the issue of Numpy vs. Matlab, as Matlab know about arrays and has a compiler that can deal with this, whilst NumPy depends on bloated operator overloading. On the other hand, Matlab is fundamentally impaired on function calls and array slicing compared with NumPy (basically copies are created instead of views). Thus, which is faster - Matlab or NumPy - very much depends on how the code is written. Now for my question: operator overloading is (as shown) not the solution to efficient scientific computing. It creates serious bloat where it is undesired. Can NumPy's performance be improved by adding the array types to the Python language it self? Or are the dynamic nature of Python preventing this? Sturla Molden -- http://mail.python.org/mailman/listinfo/python-list
