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PyPy is your Past, Present, and Future
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Author
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  * Alex Gaynor (http://twitter.com/alex_gaynor)
  * Still in school, core Django, CPython, and Pypy committer

Intro
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  * There are 2 things faster than C
     * Neutrinos
     * Pypy

Story
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  * Armin wanted to write a JIT for Python (Psyco)        
  * Psyco was the written by Armin. 
  * Kind of messing.
  * Generators came along, and not supported
  * 64-bit computers weren't supported either
  * Started writing Python in Python
  * About 2000x slower than CPython  
  * Somethings in the standard library were in python
  * Copied some optimizations over (TimSort)
  * Writing JITs sucked.
  * Writing a JIT generator for arbitrary languages is much simpler than writing a JIT for Python
  * ~2-3 years ago Alex got into Pypy
  * Beat C in str_cmp ~1 month ago
  * http://speed.pypy.org
  * Tries to show example of real time video analysis, mplayer broke.


Numpy
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  * Science likes big datasets, use Numpy
  * Numpy is in C
  * Numpy likes speed, so does pypy
  * Started reimplementing Numpy in Pypy
  
Hotspot Detection
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  * Humans are bad at detecting slow downs
  * Pypy has a JITViewer
     * http://morepypy.blogspot.com/2011/08/visualization-of-jitted-code.html
     * Allows you to view code in levels
     * Python, Assembler, etc.
  * Shows demo fo JITViewer
  * Look into code
     * "I think that's too many instructions"
     * Optimize code!
  * Shows example of `sum(x**3 for x in xrange(10000))`
  * JVM Community has good tooling
  * Python could use that too.


Current
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  * Usually benchmark against C
  * Experimenting with using C extensions.

Where we're going
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  * Many projects are being migrated
     * Django
  * Porting to Python3

Architecture
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  * Because they use a JIT Generator, can improve constantly
  * Speedups in Python3 will improve Python2

GIL
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  * Wrote a blogpost on STM (Software Transactional Memory).
     * http://morepypy.blogspot.com/2011/08/we-need-software-transactional-memory.html

  * Think STM would be slower for single core
  * STM for multicore workloads
  * GIL for single core.


What People Are Doing with Pypy
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  * Researchers getting results over lunch, instead of over night. 
  * Financial company for market analysis
  * Engineers at CERN

What Pypy Needs from the Community
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  * Encourages use of pypy if you are CPU bound
  * Requests for slow code, and they'll use it in benchmarks
  * Want to make Python the right tool for the job in more places
  * Work on the ecosystem and tools