On 20-9-2016 22:38, Irmen de Jong wrote: > Hi, > > I've stumbled across a peculiar performance issue with Pypy across some > different > platforms. It was very visible in some calculation heavy code that I wrote > that uses > Python's complex number type to calculate the well-known Mandelbrot set. > > Pypy running the code on my Windows machine is doing okay, but when running > the same > code on Pypy on different systems, the performance difference is so big it is > not even > funny. The other implementations are MUCH faster than the windows one. Which > is quite > unexpected because the other machines I've tested on have the same or much > lower > physical CPU specs than the windows machine. Here's the comparison: > > Machine specs: > Windows: 64 bits Windows 7, Intel Core 2 Quad 3.4 Ghz > Linux: 32 bits Mint 18, Virtualbox VM on above windows machine > Mac mini: OS X 10.11.6, Intel Core 2 Duo 2.53 Ghz > > The test code I've been using is here: > https://gist.github.com/irmen/c6b12b4cf88a6a4fcf5ff721c7089078 > > Test results: > function: mandel / iterations > Mac mini, Pypy 5.4.1 (64-bit): 0.81 sec / 0.65 sec > Linux, Pypy 5.1 (32-bit): 1.06 sec / 0.64 sec > Windows, Pypy 5.4.1 (32-bit): 5.59 sec / 2.87 sec > > > What could cause such a huge difference? > > Is it perhaps a compiler issue (where gcc/clang are MUCH better at optimizing > certain > things, although I wonder how much of a factor this is because Pypy is doing > JITting by > itself as far as I am aware)? Or is something strange going on with the way > the > complex number type is implemented? (the difference doesn't occur when > using only floats) > > > Regards > Irmen de Jong >
The problem boiled down to a performance issue in window's 32 bits implementation of the hypot() function (which abs(z) uses when z is a complex number type). The 64 bits windows crt lib version is much faster (on par with what is to be expected from the linux or osx version), but unfortunately there's no 64 bits pypy implementation for windows. Replacing abs(z) by sqrt(r*r+i*i) avoids the problem and is even faster still. More details here https://bitbucket.org/pypy/pypy/issues/2401 Cheers Irmen de Jong -- https://mail.python.org/mailman/listinfo/python-list
