<story><title>PyPy 1.4: Ouroboros in practice</title><url>http://morepypy.blogspot.com/2010/11/pypy-14-ouroboros-in-practice.html</url></story><parent_chain></parent_chain><comment><author>timtadh</author><text>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!&lt;p&gt;[Edit:]&lt;p&gt;Pretty good so far!&lt;p&gt;&lt;pre&gt;&lt;code&gt;    ~/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&lt;/code&gt;&lt;/pre&gt;</text></comment>
<story><title>PyPy 1.4: Ouroboros in practice</title><url>http://morepypy.blogspot.com/2010/11/pypy-14-ouroboros-in-practice.html</url></story><parent_chain></parent_chain><comment><author>orangecat</author><text>Interesting and promising. PyPy 1.4:&lt;p&gt;&lt;pre&gt;&lt;code&gt;    &amp;#62;&amp;#62;&amp;#62;&amp;#62; t1 = time.time(); a=[x*x for x in xrange(1000000)]; time.time()-t1
    0.38609600067138672
    &amp;#62;&amp;#62;&amp;#62;&amp;#62; t1 = time.time(); a=[x*x+math.sin(x/1000000.) for x in xrange(1000000)]; time.time()-t1
    0.42182803153991699
&lt;/code&gt;&lt;/pre&gt;
Python 2.7:&lt;p&gt;&lt;pre&gt;&lt;code&gt;    &amp;#62;&amp;#62;&amp;#62; t1 = time.time(); a=[x*x for x in xrange(1000000)]; time.time()-t1
    0.25005197525024414
    &amp;#62;&amp;#62;&amp;#62; t1 = time.time(); a=[x*x+math.sin(x/1000000.) for x in xrange(1000000)]; time.time()-t1
    0.6075689792633057
&lt;/code&gt;&lt;/pre&gt;
Both running in 64-bit on a 2.53GHz Core 2 Duo. It looks like PyPy&apos;s JIT has some fixed overhead, but can heavily optimize operations once it gets going.</text></comment>