[pypy-svn] r77385 - pypy/extradoc/talk/pepm2011

cfbolz at codespeak.net cfbolz at codespeak.net
Sun Sep 26 18:07:01 CEST 2010

Author: cfbolz
Date: Sun Sep 26 18:07:00 2010
New Revision: 77385

   pypy/extradoc/talk/pepm2011/paper.bib   (contents, props changed)
whack at the introduction

Modified: pypy/extradoc/talk/pepm2011/paper.bib
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	Sun Sep 26 18:07:00 2010
@@ -100,28 +100,55 @@
 overhead of the interpreter's data structures, such as operand stack etc. The
 second important problem that any JIT for a dynamic language needs to solve is
 how to deal with the overhead of boxing of primitive types and of type
-dispatching. Those are problems that are usually not present in statically typed
+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
-all objects, even integers, floats, etc. in the same way as user-defined
+all objects, even integers, floats, bools 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.
+heap-structure is allocated for them, that contains the actual value. Boxing
+primitive types can be very costly, because XXX
 Type dispatching is the process of finding the concrete implementation that is
 applicable to the objects at hand when doing a generic operation on them. An
 example would be the addition of two objects: The addition needs to check what
 the concrete objects that should be added are, and choose the implementation
-that is fitting for them.
+that is fitting for them. Type dispatching is a very common operation in a
+dynamic language because no types are known at compile time, so all operations
+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. The PyPy
+project is an environment for implementing dynamic programming languages. It'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 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}
-Last year, we wrote a paper \cite{XXX} about how PyPy's meta-JIT
-approach works. These explain how the meta-tracing JIT can remove the overhead
+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 our meta-tracing JIT are then optimized to also remove some of the
+produced by our meta-tracing JIT can be optimized to also remove some of the
 overhead more closely associated to dynamic languages, such as boxing overhead
-and type dispatching. The most important technique to achieve this is a form of
-escape analysis \cite{XXX} that we call \emph{virtual objects}. This is best
-explained via an example.
+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}, which is described in Section~\ref{sec:virtuals}. The
+basic approach of virtual 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:XXX} we describe some
+supporting techniques that are not central to the approach, but are needed to
+improve the results. The introduced techniques are evaluated in
+The contributions of this paper are:
+    \item An efficient and effective escape analysis for a tracing JIT
+    \item XXX
@@ -184,7 +211,7 @@
 3) or escape (category 4).
 \section{Escape Analysis in a Tracing JIT}
-\label{sec:Escape Analysis in a Tracing JIT}
 \subsection{Running Example}

More information about the Pypy-commit mailing list