i am just testing a n_queen solver, yesterday with 2.6.1 vs Nodejs.
The code i tested is from https://github.com/chaddotson/puzzles
But pypy is much faster as nqueen grows . And it is 40% faster than nodejs.
In smaller numbers < 10 it is slower but it has to do with JIT Warmup right?
(pypy-current)~/g/nqueen-benchmark >>> python python_n_queens_solver.py 13
N-Queens Found 73712 Solutions in 94.664358s on a 13x13 board
(pypy-current)~/g/nqueen-benchmark >>> pypy python_n_queens_solver.py 13
N-Queens Found 73712 Solutions in 5.488652s on a 13x13 board
(pypy-current)~/g/nqueen-benchmark >>> node javascript_n_queens_solver.js
13
⏎
N-Queens Found 73712 solutions in 7.112s on a 13x13 board
On Tue, Sep 1, 2015 at 5:58 PM, Armin Rigo <ar...@tunes.org> wrote:
> Hi Dima,
>
> On Mon, Aug 4, 2014 at 5:04 PM, Dima Tisnek <dim...@gmail.com> wrote:
> > Attached is n-queens solver (pardon my naive algorithm), it runs:
> > python 2.7.6: 17s
> > pypy 2.4.0 alpha: 23s
> > same nojit: 32s
> >
> > I've tried similar-looking algorithm for another problem before, and has
> > similar results -- somehow pypy was slower.
> >
> > feel free to investigate / tweak or even use on speed.pypy.org
>
> So, it took us more than one year, but now I finally figured it out.
> The reason it is slower in PyPy is because sets recompute the items'
> hash much more often than in CPython. I fixed it in the branch
> 'keys_with_hash' (which is just too late for pypy 2.6.1). Some
> microbenchmarks are 2x or 3x faster now.
>
> In your code, it shows up as the _diff() function, which returns "a -
> b" where a and b are sets of complicated objects. Actually, there are
> always disjoint sets, so the original code might be a bit buggy :-)
> But the _diff() operation is now twice faster. I get the following
> times on your nq.py example:
>
> python 2.7.3: 15.5s
> pypy 2.6.1 in keys_with_hash: 10.9s
> same with '--jit off': 20.3s
>
>
> A bientôt,
>
> Armin.
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