Author: Carl Friedrich Bolz <[email protected]>
Branch: extradoc
Changeset: r4592:7653dca8a131
Date: 2012-08-15 13:00 +0200
http://bitbucket.org/pypy/extradoc/changeset/7653dca8a131/
Log: tune formulations
diff --git a/talk/dls2012/paper.tex b/talk/dls2012/paper.tex
--- a/talk/dls2012/paper.tex
+++ b/talk/dls2012/paper.tex
@@ -129,7 +129,7 @@
motion which is a very important optimization for code with tight kernels.
Especially for dynamic languages that typically perform quite a lot of loop
invariant
type checking, boxed value unwrapping and virtual method lookups.
-In this paper we explain a scheme invented within the context of the LuaJIT
project
+In this paper we explain a scheme pioneered within the context of the LuaJIT
project
for making simple optimizations loop-aware by
using a simple pre-processing step on the trace and not changing the
optimizations themselves.
@@ -1107,13 +1107,13 @@
explicit algorithms.
Loop invariant code motion has been part of early compilers in the 1960s and
-1970s~\cite{allen_catalogue_1971}. The approach for achieving loop invariant
-code motion is typically to perform partial redundancy elimination. The
+1970s~\cite{allen_catalogue_1971}. A common approach for achieving loop
invariant
+code motion is to perform partial redundancy elimination. The
approach was first proposed by Morel and Renvoise~\cite{morel_global_1979}. It
-involves solving data flow problems usually involding bidirection data flow
+involves solving data flow problems of bidirectional data flow
equations. After improvements~\cite{chow_portable_1984,
dhamdhere_practical_1991} this approach was followed by the work of Knoop
-et.al.~\cite{knoop_lazy_1992} who cleany separated the problem into a backward
+et.al.~\cite{knoop_lazy_1992} who cleanly separated the problem into a backward
and forward data flow analysis. Implementing partial redundancy elimination in
compilers that use SSA form \cite{chow_new_1997} simplified the algorithms
because no iterative data flow analysis is needed any more.
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