Hi everyone, I'm pleased to announce that a new branch of NumExpr has been developed that will hopefully lead to a new major version release in the future. You can find the branch on the PyData github repository, and installation is as follows: git clone https://github.com/pydata/ cd numexpr git checkout numexpr-3.0 python setup.py install What's new? ========== Faster --------- The operations were re-written in such a way that gcc can auto-vectorize the loops to use SIMD instructions. Each operation now has a strided and aligned branch, which improves performance on aligned arrays by ~ 40 %. The setup time for threads has been reduced, by removing an unnecessary abstraction layer, and various other minor re-factorizations, resulting in improved thread scaling. The combination of speed-ups means that NumExpr3 often runs 200-500 % faster than NumExpr2.6 on a machine with AVX2 support. The break-even point with NumPy is now roughly arrays with 64k-elements, compared to 256-512k-elements for NE2. Plot of comparative performance for NumPy versus NE2 versus NE3 over a range of array sizes are available at: More NumPy Datatypes ------------------------------ The program was re-factorized from a ascii-encoded byte code to a struct array, so that the operation space is now 65535 instead of 128. As such, support for uint8, int8, uint16, int16, uint32, uint64, and complex64 data types was added. NumExpr3 now uses NumPy 'safe' casting rules. If an operation doesn't return the same result as NumPy, it's a bug. In the future other casting styles will be added if there is a demand for them. More complete function set ------------------------------ With the enhanced operation space, almost the entire C++11 cmath function set is supported (if the compiler library has them; only C99 is expected). Also bitwise operations were added for all integer datatypes. There are now 436 operations/functions in NE3, with more to come, compared to 190 in NE2. Also a library-enum has been added to the op keys which allows multiple backend libraries to be linked to the interpreter, and changed on a per-expression basis, rather than picking between GNU std and Intel VML at compile time, for example. More complete Python language support ------------------------------ The Python compiler was re-written from scratch to use the CPython `ast` module and a functional programming approach. As such, NE3 now compiles a wider subset of the Python language. It supports multi-line evaluation, and assignment with named temporaries. The new compiler spends considerably less time in Python to compile expressions, about 200 us for 'a*b' compared to 550 us for NE2. Compare for example: out_ne2 = ne2.evaluate( 'exp( -sin(2*a**2) - cos(2*b**2) - 2*a**2*b**2' ) to: neObj = NumExp( '''a2 = a*a; b2 = b*b out_magic = exp( -sin(2*a2) - cos(2*b2) - 2*a2*b2''' ) This is a contrived example but the multi-line approach will allow for cleaner code and more sophisticated algorithms to be encapsulated in a single NumExpr call. The convention is that intermediate assignment targets are named temporaries if they do not exist in the calling frame, and full assignment targets if they do, which provides a method for multiple returns. Single-level de-referencing (e.g. `self.data`) is also supported for increased convenience and cleaner code. Slicing still needs to be performed above the ne3.evaluate() or ne3.NumExpr() call. More maintainable ------------------------- The code base was generally refactored to increase the prevalence of single-point declarations, such that modifications don't require extensive knowledge of the code. In NE2 a lot of code was generated by the pre-processor using nested #defines. That has been replaced by a object-oriented Python code generator called by setup.py, which generates about 15k lines of C code with 1k lines of Python. The use of generated code with defined line numbers makes debugging threaded code simpler. The generator also builds the autotest portion of the test submodule, for checking equivalence between NumPy and NumExpr3 operations and functions. What's TODO compared to NE2? ------------------------------ * strided complex functions * Intel VML support (less necessary now with gcc auto-vectorization) * bytes and unicode support * reductions (mean, sum, prod, std) What I'm looking for feedback on ------------------------------ * String arrays: How do you use them? How would unicode differ from bytes strings? * Interface: We now have a more object-oriented interface underneath the familiar evaluate() interface. How would you like to use this interface? Francesc suggested generator support, as currently it's more difficult to use NumExpr within a loop than it should be. Ideas for the future ------------------------- * vectorize real functions (such as exp, sqrt, log) similar to the complex_functions.hpp vectorization. * Add a keyword (likely 'yield') to indicate that a token is intended to be changed by a generator inside a loop with each call to NumExpr.run() If you have any thoughts or find any issues please don't hesitate to open an issue at the Github repo. Although unit tests have been run over the operation space there are undoubtedly a number of bugs to squash. Sincerely, Robert Robert McLeod, Ph.D. Center for Cellular Imaging and Nano Analytics (C-CINA) Biozentrum der Universität Basel Mattenstrasse 26, 4058 Basel Work: <a href="tel:061%20387%2032%2025" value="+41613873225" target="_blank">+41.061.387.3225 [hidden email] _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.scipy.org/mailman/listinfo/numpy-discussion |
Yay! This looks really exciting. Thanks for all the hard work! Francesc 2017-02-17 12:15 GMT+01:00 Robert McLeod <[hidden email]>:
Francesc Alted
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This is very nice indeed!
On 17 February 2017 at 12:15, Robert McLeod <[hidden email]> wrote: > * bytes and unicode support > * reductions (mean, sum, prod, std) I use both a lot, maybe I can help you get them working. Also, regarding "Vectorization hasn't been done yet with cmath functions for real numbers (such as sqrt(), exp(), etc.), only for complex functions". What is the bottleneck? Is it in GCC or just someone has to sit down and adapt it? _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.scipy.org/mailman/listinfo/numpy-discussion |
Hi David,
Thanks for your comments, reply below the fold. On Fri, Feb 17, 2017 at 4:34 PM, Daπid <[hidden email]> wrote: -- This is very nice indeed! I just haven't done it yet. Basically I'm moving from Switzerland to Canada in a week so this was the gap to push something out that's usable if not perfect. Rather I just import cmath functions, which are inlined but I suspect what's needed is to break them down into their components. For example, the complex arccos function looks like this: static void nc_acos( npy_intp n, npy_complex64 *x, npy_complex64 *r) { npy_complex64 a; for( npy_intp I = 0; I < n; I++ ) { a = x[I]; _inline_mul( x[I], x[I], r[I] ); _inline_sub( Z_1, r[I], r[I] ); _inline_sqrt( r[I], r[I] ); _inline_muli( r[I], r[I] ); _inline_add( a, r[I], r[I] ); _inline_log( r[I] , r[I] ); _inline_muli( r[I], r[I] ); _inline_neg( r[I], r[I]); } } I haven't sat down and inspected whether the cmath versions get vectorized, but there's not a huge speed difference between NE2 and 3 for such a function on float (but their is for complex), so my suspicion is they aren't. Another option would be to add a library such as Yeppp! as LIB_YEPPP or some other library that's faster than glib. For example the glib function "fma(a,b,c)" is slower than doing "a*b+c" in NE3, and that's not how it should be. Yeppp is also built with Python generating C code, so it could either be very easy or very hard. On bytes and unicode, I haven't seen examples for how people use it, so I'm not sure where to start. Since there's practically not a limitation on the number of operations now (the library is 1.3 MB now, compared to 1.2 MB for NE2 with gcc 5.4) the string functions could grow significantly from what we have in NE2. With regards to reductions, NumExpr never multi-threaded them, and could only do outer reductions, so in the end there was no speed advantage to be had compared to having NumPy do them on the result. I suspect the primary value there was in PyTables and Pandas where the expression had to do everything. One of the things I've moved away from in NE3 is doing output buffering (rather it pre-allocates the output array), so for reductions the understanding NumExpr has of broadcasting would have to be deeper. In any event contributions would certainly be welcome. Robert Robert McLeod, Ph.D. Center for Cellular Imaging and Nano Analytics (C-CINA) Biozentrum der Universität Basel Mattenstrasse 26, 4058 Basel Work: <a href="tel:061%20387%2032%2025" value="+41613873225" target="_blank">+41.061.387.3225 [hidden email] _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.scipy.org/mailman/listinfo/numpy-discussion |
Hi everyone,
Thanks for this. It looks absolutely fantastic. I've been putting off using numexpr but it looks like I don't have a choice anymore. ;)
Regarding feature requests, I've always found it off putting that I have to wrap my expressions in a string to speed them up. Has anyone explored the possibility of using Python 3.6's frame evaluation API to do this? I remember a vague discussion on this list a while back but I don't know whether anything came of it.
Thanks!
Juan.
On 18 Feb 2017, 3:42 AM +1100, Robert McLeod <[hidden email]>, wrote:
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Hi Juan, A guy on reddit suggested looking at SymPy for just such a thing. I know that Dask also represents its process as a graph. I'll think about it some more but it seems a little abstract still. To a certain extent the NE3 compiler already works this way. The compiler has a dictionary in which keys are `ast.Node` types, and each value is a function pointer, which knows how to handle that particular node. Providing an external interface to this would be the most natural extension. There's quite a few things to do before I would think about a functional interface. The things I mentioned in my original mail; pickling of the C-object so that it can be using within modules like `multiprocessing`; having a pre-allocated shared memory region shared among threads for temporaries and parameters, etc. If someone else wants to dabble in it they are welcome to. Robert On Sun, Feb 19, 2017 at 4:19 AM, Juan Nunez-Iglesias <[hidden email]> wrote:
Robert McLeod, Ph.D. Center for Cellular Imaging and Nano Analytics (C-CINA) Biozentrum der Universität Basel _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.scipy.org/mailman/listinfo/numpy-discussion |
Hi All,
Just a side note that at a smaller scale some of the benefits of numexpr are coming to numpy: Julian Taylor has been working on identifying temporary arrays in https://github.com/numpy/numpy/pull/7997. Julian also commented (https://github.com/numpy/numpy/pull/7997#issuecomment-246118772) that with PEP 523 in python 3.6, this should indeed become a lot easier. All the best, Marten _______________________________________________ NumPy-Discussion mailing list [hidden email] https://mail.scipy.org/mailman/listinfo/numpy-discussion |
Yes, Julian is doing an amazing work on getting rid of temporaries inside NumPy. However, NumExpr still has the advantage of using multi-threading right out of the box, as well as integration with Intel VML. Hopefully these features will eventually arrive to NumPy, but meanwhile there is still value in pushing NumExpr. Francesc 2017-02-19 18:21 GMT+01:00 Marten van Kerkwijk <[hidden email]>: Hi All, -- Francesc Alted
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