[pypy-svn] r77678 - pypy/extradoc/talk/pepm2011

cfbolz at codespeak.net cfbolz at codespeak.net
Thu Oct 7 14:49:49 CEST 2010


Author: cfbolz
Date: Thu Oct  7 14:49:46 2010
New Revision: 77678

Modified:
   pypy/extradoc/talk/pepm2011/escape-tracing.pdf
   pypy/extradoc/talk/pepm2011/paper.tex
Log:
rewrite the introduction to mention partial evaluation more


Modified: pypy/extradoc/talk/pepm2011/escape-tracing.pdf
==============================================================================
Binary files. No diff available.

Modified: pypy/extradoc/talk/pepm2011/paper.tex
==============================================================================
--- pypy/extradoc/talk/pepm2011/paper.tex	(original)
+++ pypy/extradoc/talk/pepm2011/paper.tex	Thu Oct  7 14:49:46 2010
@@ -108,7 +108,7 @@
 dispatching. Those are problems that are usually not present or at least less
 severe in statically typed languages.
 
-Boxing of primitive types means that dynamic languages need to be able to handle
+Boxing of primitive types is necessary because dynamic languages need to be able to handle
 all objects, even integers, floats, booleans etc. in the same way as user-defined
 instances. Thus those primitive types are usually \emph{boxed}, i.e. a small
 heap-structure is allocated for them, that contains the actual value. Boxing
@@ -126,31 +126,34 @@
 need it.
 
 A recently popular approach to implementing just-in-time compilers for dynamic
-languages is that of a tracing JIT. A tracing JIT often takes the form of an
-extension to an existing interpreter, which can be sped up that way. This
-approach is also the one taken by the PyPy project, which is an environment for
-implementing dynamic programming languages. PyPy's
-approach to doing so is to straightforwardly implement an interpreter for the
-to-be-implemented language, and then use powerful tools to turn the interpreter
-into an efficient virtual machine (VM) that also contains a just-in-time compiler.
-This compiler
-is automatically generated from the interpreter using partial-evaluation-like
-techniques \cite{bolz_tracing_2009}. The PyPy project and its approach to
-tracing JIT compilers is described in Section~\ref{sec:Background}.
-
-The tracing JIT approach that the PyPy project is taking removes the overhead
-of bytecode dispatch. In this paper we want to explain how the traces that are
-produced by PyPy's tracing JIT can be optimized to also remove some of the
+languages is that of a tracing JIT. A tracing JIT works by observing the running
+program and recording linear execution traces, which are then turned into
+machine code. One reason for the popularity of tracing JITs is their relative
+simplicity. They can often be added to an interpreter and a lot of the
+infrastructure of an interpreter can be reused. They give some important
+optimizations like inlining and constant-folding for free. A tracing JIT always
+produces linear pieces of code, which makes many optimizations that are usually
+hard in a compiler simpler, such as register allocation.
+
+The usage of a tracing JIT can remove the overhead of bytecode dispatch and that
+of the interpreter data structures. In this paper we want to present an approach
+how an optimization can be added to a tracing JIT that also removes some of the
 overhead more closely associated to dynamic languages, such as boxing overhead
-and type dispatching. To understand the problem more closely, we analyze the
-occurring object lifetimes in Section~\ref{sec:lifetimes}. The most important
-technique to achieve this is a form of escape analysis \cite{XXX} that we call
-\emph{virtual objects}\footnote{The terminology comes from \cite{rigo_representation-based_2004}},
-which is described in Section~\ref{sec:virtuals}. The
-goal of virtual objects is to remove allocations of temporary objects that have
-a predictable lifetime and to optimize type dispatching in the process.
+and type dispatching. Our experimental platform is the PyPy project, which is an
+environment for implementing dynamic programming languages. PyPy and tracing
+JITs are described in more detail in Section~\ref{sec:Background}.
+Section~\ref{sec:lifetimes} analyzes the problem to be solved more closely.
+
+The most important technique we use to achieve to optimize traces is a
+straightforward application of partial evaluation. The partial evaluation
+performs a form of escape analysis \cite{XXX} on the traces and make some
+objects that are allocated in the trace \emph{static}\footnote{These objects are
+called \emph{virtual} in Psyco \cite{rigo_representation-based_2004}.} which
+means that they do not occur any more in the optimized trace. This technique is
+informally described in Section~\ref{sec:virtuals}, a more formal description is
+given in Section~\ref{sec:formal}.
 
-The basic approach of virtual objects can then be extended to also be used for
+The basic approach of static objects can then be extended to also be used for
 type-specializing the traces that are produced by the tracing JIT
 (Section~\ref{sec:crossloop}). In Section~\ref{sec:support} we describe some
 supporting techniques that are not central to the approach, but are needed to
@@ -161,9 +164,11 @@
 
 \begin{enumerate}
     \item An efficient and effective algorithm for removing object allocations in a tracing JIT.
-    \item XXX
+    \item A characterization of this algorithm as partial evaluation.
+    \item A rigorous evaluation of this algorithm.
 \end{enumerate}
 
+
 \section{Background}
 \label{sec:Background}
 
@@ -610,7 +615,7 @@
 and only three \texttt{guard\_class} operations, from the original seven.
 
 \section{Formal Description of the Algorithm}
-\label{sec:Algorithm}
+\label{sec:formal}
 
 
 \begin{figure*}



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