[pypy-dev] Idea for speed.pypy.org

Miquel Torres tobami at googlemail.com
Mon Dec 13 09:31:04 CET 2010

Oh, btw., the "normalized" stacked bars now display a warning note
about its correctness, and how it must be viewed as giving results a
weighting instead of them being normalized. It even includes a link to
the proper paper. I hope that is enough for the strict statisticians
among us ;-)


PS: there is a bug in the jqPlot plotting library when null values are
present. Trying to display PyPy 1.3 results for the newer go, pyflake
or  raytrace will create some nasty js loops. It also has problems
with autoscaling the axis sometimes.

2010/12/13 Miquel Torres <tobami at googlemail.com>:
> Thanks all for the input.
> I've compiled a list based on your mails, the Unladen benchmarks page
> (http://code.google.com/p/unladen-swallow/wiki/Benchmarks), and the
> alioth descriptions. Here is an extract of the current speed.pypy.org
> admin:
> ai
> chaos   Creates chaosgame-like fractals
> crypto_pyaes    A pure python implementation of AES
> django          Uses the Django template system to build a 150x150-cell HTML table
> fannkuch                Indexed-access to tiny integer-sequence. The fannkuch
> benchmark is defined by programs in Performing Lisp Analysis of the
> FANNKUCH Benchmark, Kenneth R. Anderson and Duane Rettig.
> float           Creates an array of points using circular projection and then
> normalizes and maximizes them. Floating-point heavy.
> go              A go (chess like game) computer player AI.
> html5lib        Parses the HTML 5 spec using html5lib
> meteor-contest  Searchs for solutions to shape packing puzzle.
> nbody_modified          Double-precision N-body simulation. It models the
> orbits of Jovian planets, using a simple symplectic-integrator.
> pyflate-fast            Stand-alone pure-Python DEFLATE (gzip) and bzip2
> decoder/decompressor.
> raytrace-simple A raytracer renderer
> richards                Medium-sized language benchmark that simulates the task
> dispatcher in the kernel of an operating system.
> rietveld        A Django application benchmark.
> slowspitfire
> spambayes       Runs a canned mailbox through a SpamBayes ham/spam classifier
> spectral-norm
> spitfire        Uses the Spitfire template system to build a 1000x1000-cell HTML table.
> spitfire_cstringio      Uses the Spitfire template system to build a
> 1000x1000-cell HTML table, using the cstringio module.
> telco
> twisted_iteration
> twisted_names
> twisted_pb
> twisted_tcp     Connects one Twised client to one Twisted server over TCP
> (on the loopback interface) and then writes bytes as fast as it can.
> waf     Python-based framework for configuring, compiling and installing
> applications. It derives from the concepts of other build tools such
> as Scons, Autotools, CMake or Ant.
> So the remaining descriptions are
> ai
> slowspitfire (what is the exact difference between the three spitfire benches?)
> spectral-norm
> telco
> twisted (most of them)
> Are the descriptions all right so far?. They can be made much longer
> if you deem it desirable.
> on speed.pypy.org you will currently see the descriptions in 3 places:
> - Changes view: A tooltip on hover over each benchmark
> - Timeline: a description box beneath each plot
> - Comparison: A tooltip over each benchmark when hovering the
> selection menu on the left side.
> Any suggestions on how to improve it further are welcome ;-)
> Miquel
> 2010/12/9 Paolo Giarrusso <p.giarrusso at gmail.com>:
>> On Thu, Dec 9, 2010 at 14:14, Leonardo Santagada <santagada at gmail.com> wrote:
>>> Here is a incomplete draft list:
>>> [slow]spitfire[cstringio]: Spitfire is a template language, the
>>> cstringio version uses a modified engine (that uses cstringio)
>>> spambayes: Spambayes is a bayesian spam filter
>> Why is [slow]spitfire slower with PyPy? Is it regex-related? I
>> remember when, because of this, spambayes was slower (including
>> release 1.3, now solved). But for spitfire, 1.3 was faster than 1.4
>> and the head (for slowspitfire it's the opposite).
>> For the rest, I see no significant case of slowdown of PyPy over time.
>> http://speed.pypy.org/comparison/?exe=2%2B35,1%2B41,1%2B172,1%2BL&ben=1,2,25,3,4,5,22,6,7,8,23,24,9,10,11,12,13,14,15,16,17,18,19,20,26&env=1&hor=true&bas=2%2B35&chart=normal+bars
>> --
>> Paolo Giarrusso - Ph.D. Student
>> http://www.informatik.uni-marburg.de/~pgiarrusso/

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