PyPy 1.4: Ouroboros in practice(morepypy.blogspot.com)
morepypy.blogspot.com
PyPy 1.4: Ouroboros in practice
http://morepypy.blogspot.com/2010/11/pypy-14-ouroboros-in-practice.html
7 comments
Another Implementation
~/stuff/Programming/sleepytree/
hendersont@glycineportable sleepytree $ time python test_metricspace.py -v
test_distance (__main__.TestCompare) ... ok
test_nondegenercy (__main__.TestCompare) ... ok
test_symmetry (__main__.TestCompare) ... ok
test_triangle_inequality (__main__.TestCompare) ... ok
test_contains (__main__.TestTestNode) ... ok
test_get (__main__.TestTestNode) ... ok
test_iter (__main__.TestTestNode) ... ok
----------------------------------------------------------------------
Ran 7 tests in 570.385s
OK
real 9m30.451s
user 9m23.920s
sys 0m1.730s
~/stuff/Programming/sleepytree/
hendersont@glycineportable sleepytree $ time pypy test_metricspace.py -v
test_distance (__main__.TestCompare) ... ok
test_nondegenercy (__main__.TestCompare) ... ok
test_symmetry (__main__.TestCompare) ... ok
test_triangle_inequality (__main__.TestCompare) ... ok
test_contains (__main__.TestTestNode) ... ok
test_get (__main__.TestTestNode) ... ok
test_iter (__main__.TestTestNode) ... ok
----------------------------------------------------------------------
Ran 7 tests in 255.339s
OK
real 4m15.396s
user 4m12.990s
sys 0m0.310sInteresting and promising. PyPy 1.4:
>>>> t1 = time.time(); a=[x*x for x in xrange(1000000)]; time.time()-t1
0.38609600067138672
>>>> t1 = time.time(); a=[x*x+math.sin(x/1000000.) for x in xrange(1000000)]; time.time()-t1
0.42182803153991699
>>> t1 = time.time(); a=[x*x for x in xrange(1000000)]; time.time()-t1
0.25005197525024414
>>> t1 = time.time(); a=[x*x+math.sin(x/1000000.) for x in xrange(1000000)]; time.time()-t1
0.6075689792633057
There is something strange with the example.
$ pypy -mtimeit -s'import math; sin=math.sin' \
'[x*x+sin(x/1e6) for x in xrange(1000000)]'
10 loops, best of 3: 156 msec per loop
$ pypy -mtimeit -s'import math; sin=math.sin' \
'[x*x+sin(x) for x in xrange(1000000)]'
10 loops, best of 3: 188 msec per loop
$ python2.7 -mtimeit -s'import math; sin=math.sin' \
'[x*x+sin(x) for x in xrange(1000000)]'
10 loops, best of 3: 231 msec per loop
$ python2.7 -mtimeit -s'import math; sin=math.sin' \
'[x*x+sin(x/1e6) for x in xrange(1000000)]'
10 loops, best of 3: 253 msec per loop
$ pypy -mtimeit '[x*x for x in xrange(1000000)]'
10 loops, best of 3: 126 msec per loop
$ python2.7 -mtimeit '[x*x for x in xrange(1000000)]'
10 loops, best of 3: 67.3 msec per loop
$ pypy -mtimeit '[x*x*x for x in xrange(1000000)]'
10 loops, best of 3: 123 msec per loop
$ python2.7 -mtimeit '[x*x*x for x in xrange(1000000)]'
10 loops, best of 3: 118 msec per loopWith no division it is slower (?)
Very interesting. Looks like sin is faster for arguments less than pi/4 (~=0.7853981633974483):
Edit: "Intel's sin/cos implementation sucks golfballs through gardenhoses for arguments outside of [-pi/4,pi/4]": http://stackoverflow.com/questions/523531/fast-transcendent-...
Very interesting. Looks like sin is faster for arguments less than pi/4 (~=0.7853981633974483):
Edit: "Intel's sin/cos implementation sucks golfballs through gardenhoses for arguments outside of [-pi/4,pi/4]": http://stackoverflow.com/questions/523531/fast-transcendent-...
$ pypy -mtimeit -s'import math; sin=math.sin; a=0.786' '[sin(a) for x in xrange(1000000)]'
10 loops, best of 3: 395 msec per loop
$ pypy -mtimeit -s'import math; sin=math.sin; a=0.785' '[sin(a) for x in xrange(1000000)]'
10 loops, best of 3: 375 msec per loop
$ python -mtimeit -s'import math; sin=math.sin; a=0.786' '[sin(a) for x in xrange(1000000)]'
10 loops, best of 3: 177 msec per loop
$ python -mtimeit -s'import math; sin=math.sin; a=0.785' '[sin(a) for x in xrange(1000000)]'
10 loops, best of 3: 155 msec per loopI think this is the first PyPy release that's actually viable to use for me (because of x86-64). Very exciting!
(Hope they catch up with 2.6 - or 2.7 - soon, though.)
(Hope they catch up with 2.6 - or 2.7 - soon, though.)
>PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython.
What still works in CPython but not PyPy?
What still works in CPython but not PyPy?
pycrypto, gmpy, pyOpenSSL, psycopg2 (fixes in progress, I hear), PyV8, Pyflakes
pyOpenSSL, which means you can't use SSL in twisted :(
Anything that relies on C extensions. Numpy is the biggie for me.
32-bit pypy 1.4 can compile and run some C extensions; they just have to really well-written (not relying on CPython behavior). It can't find documentation for this, so freenode/#pypy is a good place to get details.
wxPython is something I personally miss.
wxPython does work on pypy http://morepypy.blogspot.com/2010/05/running-wxpython-on-top...
That blog post just shows a proof of concept, not official support.
Sorry to bother people with this question, but I've spent ages searching my internet history for an answer, to no avail:
A few weeks ago someone posted a Python related link on HN. It was some sort of guide or in-depth analysis, with code snippets. The code snippets did not have any syntax colouring. The background of the site was a nice dark/deep green texture (slightly bluish maybe). The top of the page had a sort of golden bookmark icon in the corner.
If anyone remembers that site please let me know the url or the title. I need to find it again.
A few weeks ago someone posted a Python related link on HN. It was some sort of guide or in-depth analysis, with code snippets. The code snippets did not have any syntax colouring. The background of the site was a nice dark/deep green texture (slightly bluish maybe). The top of the page had a sort of golden bookmark icon in the corner.
If anyone remembers that site please let me know the url or the title. I need to find it again.
I hope they add support for JIT with the Stackless features.
[Edit:]
Pretty good so far!