Author: Maciej Fijalkowski <fij...@gmail.com>
Branch: extradoc
Changeset: r4866:85b726d79ae2
Date: 2012-10-17 09:51 +0200
http://bitbucket.org/pypy/extradoc/changeset/85b726d79ae2/
Log: merge
diff --git a/talk/dls2012/benchmarks/image/sobel.py
b/talk/dls2012/benchmarks/image/sobel.py
--- a/talk/dls2012/benchmarks/image/sobel.py
+++ b/talk/dls2012/benchmarks/image/sobel.py
@@ -27,7 +27,7 @@
-1.0 * img[p + (-1, 1)] + 1.0 * img[p + (1, 1)]
dy = -1.0*img[p + (-1,-1)] -2.0*img[p + (0,-1)] -1.0*img[p + (1,-1)] +
\
1.0*img[p + (-1, 1)] +2.0*img[p + (0, 1)] +1.0*img[p + (1, 1)]
- res[p] = sqrt(dx*dx + dy*dy) / 4.0
+ res[p] = sqrt(dx*dx + dy*dy) / 1.5
return res
def uint8(img):
diff --git a/talk/rupy2012/abstract.rst b/talk/rupy2012/abstract.rst
--- a/talk/rupy2012/abstract.rst
+++ b/talk/rupy2012/abstract.rst
@@ -1,10 +1,10 @@
In this talk I would like to present the dominant implementation of Python
(CPython) performance characteristics and explain why, in case the performance
-is an issue for your application, it's characteristics are bad for
+is an issue for your application, its characteristics are bad for
abstractions.
In the next part I'll explain the mission statement of the PyPy Python
-implementation, brief description of it's performance characteristics and
+implementation, brief description of its performance characteristics and
where the project is going. I'll also explain the basics of Just in Time
compilation and what it changes on the observed performance.
In summary, the goal is to explain how
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