[pypy-commit] extradoc extradoc: rework the abstract

[fijal]

Author: Maciej Fijalkowski <fijall at gmail.com>
Branch: extradoc
Changeset: r5527:1e9cb1d73547
Date: 2015-04-14 13:31 +0200
http://bitbucket.org/pypy/extradoc/changeset/1e9cb1d73547/

Log:	rework the abstract

diff --git a/talk/ep2015/performance-abstract.txt b/talk/ep2015/performance-abstract.txt
--- a/talk/ep2015/performance-abstract.txt
+++ b/talk/ep2015/performance-abstract.txt
@@ -1,20 +1,21 @@
-==================================
-PyPy performance (not) for dummies
-==================================
+====================================
+Python performance (not) for dummies
+====================================
 
+In this talk we would like to have a short introduction on how Python
+programs are compiled and executed, with a special attention towards
+just in time compilation done by PyPy. PyPy is the most advanced Python
+interpreter around and while it should generally just speed up your programs
+there is a wide range of performance that you can get out of PyPy, ranging from
+slightly faster than CPython to C speeds, depending on how you write your
+programs.
 
-Abstract
----------
+We will split the talk in two parts. In the first part we will explain
+how things work and what can and what cannot be optimized as well as describe
+the basic heuristics of JIT compiler and optimizer. In the next part we will
+do a survey of existing tools for looking at performance of Python programs
+with specific focus on PyPy.
 
-PyPy is the fastest Python interpreter around, and its JIT can optimize most
-of your Python programs without problems. However, there are techniques to
-improve the performances even further and squeeze the most out of PyPy. In
-this talk we will see:
-
-- the general principles behind the PyPy JIT
-
-- how to profile programs to find the bottlenecks
-
-- how to examine the code generated by the JIT
-
-- how to write JIT-friendly programs
+As a result of this talk, an audience member should be better equipped with
+tools how to write new software and improve existing software with performance
+in mind.