17 ms·

PyPy 1.4: Ouroboros in practice

127 points by aaw 16y ago

orangecat 16y ago
Interesting 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 Python 2.7: >>> 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 Both running in 64-bit on a 2.53GHz Core 2 Duo. It looks like PyPy's JIT has some fixed overhead, but can heavily optimize operations once it gets going.
- d0mine 16y ago
  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 With no division it is slower (?): $ 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 CPython shows expected behavior: $ 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 CPython is faster for tiny cases: $ 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 loop
  - orangecat 16y ago
    With 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-trigonometric-functions-for-java 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 loop
huadie 16y ago
Your satisfaction is our eternal pursuit. ( http://www.newgoin.com http://www.newgoin.com ) We specialized in the exportation of sport shoes and other products(clothing, bag,sunglasses,watches,belts,etc )which have great enjoyed popularty in the world market Many of our goods are on sales ,we can guarantee the crediblity by Pay-pal and delivery time .we would like to make a long termship. 1)Name : The perfect gift. 2)Grade : AAA+ 3)Package : in original boxes 4)Color : various 5)MOQ : 1 pc 6)Payment : Pay-pal/Western Union / Credit card/Moneygram 7)Ship-ping : 4-7 days with guarantee of customs clearance, drop shipp-ing is accepted to customers'demands. 8)Who-lesale: very low price, you can make a small order first to test quality and service. ( http://newgoin.com http://newgoin.com ) Online Contact . thank you.
dochtman 16y ago
I 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.)
- kingkilr 16y ago
  We have a fast-forward branch that is currently working on moving to Python 2.7.
  - dochtman 16y ago
    I know, I hope its merging is followed by a quick release. :)
carlosedp 16y ago
I hope they add support for JIT with the Stackless features.
RyanMcGreal 16y ago
>PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython. What still works in CPython but not PyPy?
- cool-RR 16y ago
  wxPython is something I personally miss.
  - santagada 16y ago
    wxPython does work on pypy http://morepypy.blogspot.com/2010/05/running-wxpython-on-top-of-pypy.html http://morepypy.blogspot.com/2010/05/running-wxpython-on-top...
    - cool-RR 16y ago
      That blog post just shows a proof of concept, not official support.
- pquerna 16y ago
  pyOpenSSL, which means you can't use SSL in twisted :(
- ivank 16y ago
  pycrypto, gmpy, pyOpenSSL, psycopg2 (fixes in progress, I hear), PyV8, Pyflakes
- hyperbovine 16y ago
  Anything that relies on C extensions. Numpy is the biggie for me.
  - ivank 16y ago
    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.
timtadh 16y ago
Currently testing a CPU intensive algorithm I need for my current research project. Hoping it will save me some time. When I am done I will post the results! [Edit:] Pretty good so far! ~/stuff/Programming/faultire/src/ hendersont@glycineportable src $ time pypy sleepytree/test_metricspace.py ....... ---------------------------------------------------------------------- Ran 7 tests in 39.621s OK real 0m39.675s user 0m39.150s sys 0m0.170s ~/stuff/Programming/faultire/src/ hendersont@glycineportable src $ time python sleepytree/test_metricspace.py ....... ---------------------------------------------------------------------- Ran 7 tests in 69.442s OK real 1m9.483s user 1m8.970s sys 0m0.110s
- timtadh 16y ago
  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 0m0.310s
- bad_user 16y ago
  Could you please also run this benchmark compiled with Cython? Thanks,
  - 16y ago
Tycho 16y ago
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.

127 points by aaw 16y ago

Interesting 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 Python 2.7: >>> 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 Both running in 64-bit on a 2.53GHz Core 2 Duo. It looks like PyPy's JIT has some fixed overhead, but can heavily optimize operations once it gets going.

d0mine 16y ago
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 With no division it is slower (?): $ 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 CPython shows expected behavior: $ 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 CPython is faster for tiny cases: $ 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 loop
- orangecat 16y ago
  With 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-trigonometric-functions-for-java 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 loop

huadie 16y ago

Your satisfaction is our eternal pursuit. ( http://www.newgoin.com http://www.newgoin.com ) We specialized in the exportation of sport shoes and other products(clothing, bag,sunglasses,watches,belts,etc )which have great enjoyed popularty in the world market Many of our goods are on sales ,we can guarantee the crediblity by Pay-pal and delivery time .we would like to make a long termship. 1)Name : The perfect gift. 2)Grade : AAA+ 3)Package : in original boxes 4)Color : various 5)MOQ : 1 pc 6)Payment : Pay-pal/Western Union / Credit card/Moneygram 7)Ship-ping : 4-7 days with guarantee of customs clearance, drop shipp-ing is accepted to customers'demands. 8)Who-lesale: very low price, you can make a small order first to test quality and service. ( http://newgoin.com http://newgoin.com ) Online Contact . thank you.

dochtman 16y ago

I 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.)

kingkilr 16y ago
We have a fast-forward branch that is currently working on moving to Python 2.7.
- dochtman 16y ago
  I know, I hope its merging is followed by a quick release. :)

carlosedp 16y ago

I hope they add support for JIT with the Stackless features.

RyanMcGreal 16y ago

>PyPy is a very compliant Python interpreter, almost a drop-in replacement for CPython. What still works in CPython but not PyPy?

cool-RR 16y ago
wxPython is something I personally miss.
- santagada 16y ago
  wxPython does work on pypy http://morepypy.blogspot.com/2010/05/running-wxpython-on-top-of-pypy.html http://morepypy.blogspot.com/2010/05/running-wxpython-on-top...
  - cool-RR 16y ago
    That blog post just shows a proof of concept, not official support.
pquerna 16y ago
pyOpenSSL, which means you can't use SSL in twisted :(
ivank 16y ago
pycrypto, gmpy, pyOpenSSL, psycopg2 (fixes in progress, I hear), PyV8, Pyflakes
hyperbovine 16y ago
Anything that relies on C extensions. Numpy is the biggie for me.
- ivank 16y ago
  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.

timtadh 16y ago

Currently testing a CPU intensive algorithm I need for my current research project. Hoping it will save me some time. When I am done I will post the results! [Edit:] Pretty good so far! ~/stuff/Programming/faultire/src/ hendersont@glycineportable src $ time pypy sleepytree/test_metricspace.py ....... ---------------------------------------------------------------------- Ran 7 tests in 39.621s OK real 0m39.675s user 0m39.150s sys 0m0.170s ~/stuff/Programming/faultire/src/ hendersont@glycineportable src $ time python sleepytree/test_metricspace.py ....... ---------------------------------------------------------------------- Ran 7 tests in 69.442s OK real 1m9.483s user 1m8.970s sys 0m0.110s

timtadh 16y ago
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 0m0.310s
bad_user 16y ago
Could you please also run this benchmark compiled with Cython? Thanks,
- 16y ago

Tycho 16y ago

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.