Author: Hakan Ardo <[email protected]>
Branch: extradoc
Changeset: r4587:ed267e483232
Date: 2012-08-15 20:09 +0200
http://bitbucket.org/pypy/extradoc/changeset/ed267e483232/
Log: correct some details
diff --git a/talk/dls2012/licm.pdf b/talk/dls2012/licm.pdf
index
4e41479628229f6b9c2635f91c7f58c4684ae264..53e9a461f7d0e384c8c7fba88a6002c1337aaeb1
GIT binary patch
[cut]
diff --git a/talk/dls2012/paper.tex b/talk/dls2012/paper.tex
--- a/talk/dls2012/paper.tex
+++ b/talk/dls2012/paper.tex
@@ -63,7 +63,7 @@
\newboolean{showcomments}
-\setboolean{showcomments}{false}
+\setboolean{showcomments}{true}
\ifthenelse{\boolean{showcomments}}
{\newcommand{\nb}[2]{
\fbox{\bfseries\sffamily\scriptsize#1}
@@ -1007,10 +1007,10 @@
\item {\bf conv5}$\left(n\right)$: one-dimensional convolution with fixed
kernel-size $5$. Similar to conv3, but with
${\bf k} = \left(k_1, k_2, k_3, k_4, k_5\right)$. The enumeration of the
elements in $\bf k$ is still
hardcoded into the implementation making the benchmark consist of a single
loop too.
-\item {\bf conv3x3}$\left(n\right)$: two-dimensional convolution with kernel
of fixed
+\item {\bf conv3x3}$\left(n,m\right)$: two-dimensional convolution with kernel
of fixed
size $3 \times 3$ using a custom class to represent two-dimensional
arrays. It is implemented as two nested loops that iterates over the
elements of the
-$n\times n$ output matrix ${\bf B} = \left(b_{i,j}\right)$ and calculates each
element from the input matrix
+$m\times n$ output matrix ${\bf B} = \left(b_{i,j}\right)$ and calculates each
element from the input matrix
${\bf A} = \left(a_{i,j}\right)$ and a kernel ${\bf K} = \left(k_{i,j}\right)$
using $b_{i,j} = $
\begin{equation}
\label{eq:convsum}
@@ -1020,8 +1020,9 @@
k_{1,3} a_{i+1,j-1} &+& k_{1,2} a_{i+1,j} &+& k_{1,1} a_{i+1,j+1} \\
\end{array}
\end{equation}
-for $1 \leq i \leq n$ and $1 \leq j \leq n$.
-The memory for storing the matrices are again allocated outside the benchmark
and $n=1000$ was used.
+for $1 \leq i \leq m$ and $1 \leq j \leq n$.
+The memory for storing the matrices are again allocated outside the benchmark
and $(n,m)=(1000,1000)$
+as well as $(n,m)=(1000000,3)$ was used.
\item {\bf dilate3x3}$\left(n\right)$: two-dimensional dilation with kernel of
fixed
size $3 \times 3$. This is similar to convolution but instead of
summing over the terms in Equation~\ref{eq:convsum}, the maximum over those
terms is taken. That places a
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