Author: Carl Friedrich Bolz <[email protected]>
Branch: extradoc
Changeset: r4630:131b456ebcb5
Date: 2012-08-16 17:37 +0200
http://bitbucket.org/pypy/extradoc/changeset/131b456ebcb5/
Log: some details
diff --git a/talk/dls2012/paper.tex b/talk/dls2012/paper.tex
--- a/talk/dls2012/paper.tex
+++ b/talk/dls2012/paper.tex
@@ -897,23 +897,11 @@
practice, and that might be worth noting.
}
-\revc{
-I would have liked to have benchmark results for some larger applications.
-When is this optimization effective on a large scale, if ever?
-}
-\cfbolz{I don't actually know. Does anybody?}
-
\revd{
It isn't clear from the paper, but a reader might conclude that the bulk of the
time savings are from removing boxing/unboxing operations.
}
-\revd{
-This paper is relatively short, and could be significantly improved with a
-couple of pages of additional information about the details of the benchmarks
--- both on the Python and on the C side.
-}
-
The loop peeling optimization was implemented in the PyPy
framework in about 450 lines of RPython code. That means that the
JIT-compilers generated for all
interpreters implemented with RPython now can take advantage of
@@ -1039,6 +1027,8 @@
The benchmarks and the scripts to run them can be found in the repository
for this paper:
\texttt{https://bitbucket.org/pypy/extradoc/src/
tip/talk/dls2012/benchmarks}
}
+For benchmarks using larger Python applications the times are unaffected or
+slightly improved by the loop optimization of this paper.
The benchmarks are
\begin{itemize}
@@ -1117,11 +1107,12 @@
point numbers, both in the Python, C and Lua code.
In addition we also ported the
-SciMark\footnote{\texttt{http://math.nist.gov/scimark2/}} benchmakts to
python, and compared
-their runtimes with the already existing Lua and C implementations.
-This port was performed after the release of the pypy used to run the
benchmarks which means that
-these benchmarks have not influenced the pypy implementation.
-SciMark consists of
+SciMark\footnote{\texttt{http://math.nist.gov/scimark2/}} benchmarts to
Python, and compared
+their runtimes with the already existing
+Lua\footnote{\texttt{http://luajit.org/download/scimark.lua}} and C
+implementations.
+
+SciMark consists of:
\begin{itemize}
\item {\bf SOR}$\left(n, c\right)$: Jacobi successive over-relaxation on a
$n\times n$ grid repreated $c$ times.
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