[pypy-svn] r44647 - pypy/extradoc/talk/badhonnef2007

cfbolz at codespeak.net cfbolz at codespeak.net
Sun Jul 1 13:33:00 CEST 2007

Author: cfbolz
Date: Sun Jul  1 13:32:58 2007
New Revision: 44647

      - copied unchanged from r44646, pypy/extradoc/talk/dyla2007/dyla.bib
      - copied unchanged from r44646, pypy/extradoc/talk/dyla2007/llncs.cls
beginnig of the bad honnef paper

Added: pypy/extradoc/talk/badhonnef2007/Makefile
--- (empty file)
+++ pypy/extradoc/talk/badhonnef2007/Makefile	Sun Jul  1 13:32:58 2007
@@ -0,0 +1,15 @@
+badhonnef.pdf: badhonnef.tex badhonnef.bib
+	pdflatex badhonnef
+	bibtex badhonnef
+	pdflatex badhonnef
+	pdflatex badhonnef
+%.pdf: %.eps
+	epstopdf $<
+view: badhonnef.pdf
+	xpdf badhonnef.pdf  &
+	rm badhonnef.pdf

Added: pypy/extradoc/talk/badhonnef2007/badhonnef.tex
--- (empty file)
+++ pypy/extradoc/talk/badhonnef2007/badhonnef.tex	Sun Jul  1 13:32:58 2007
@@ -0,0 +1,84 @@
+\title{A Flexible Prolog Interpreter in Python}
+%\titlerunning{XXX}     % abbreviated title (for running head)
+%                                     also used for the TOC unless
+%                                     \toctitle is used
+\author{Carl Friedrich Bolz}
+\authorrunning{Bolz}   % abbreviated author list (for running head)
+%%%% modified list of authors for the TOC (add the affiliations)
+\tocauthor{Bolz (D\"usseldorf)}
+\institute{ Lehrstuhl Softwaretechnik und Programmiersprachen\\
+Institut f\"{u}r Informatik, Universit\"at D\"usseldorf, Germany\\
+  \email{ cfbolz at gmx.de}}
+Efficient virtual machines for the Prolog programming language (and also for
+other dynamic highlevel languages) are usually large and intricate pieces of
+software implemented in a low-level language such as C that are extensively
+optimized over many years and are therefore difficult to change in many
+respects. Due to their inherent complexity it is hard to develop them further,
+to maintain them or to use them to experiment with language extensions or new
+Early implementation decisions, such as choice of garbage collector, are
+extremely hard to change afterwards. In addition, it is also much harder to
+perform advanced optimizations or transformations (such as partial evaluation)
+on the virtual machine itself due to the low level of the implementation
+language. Furthermore it is (by definition) impossible to port them to a non-C
+platform such as the Java Virtual Machine or the .NET framework Therefore the
+numerous implementations of a Prolog virtual machine integrated into one of
+these environments are all new implementations which have to be maintained
+themselves and therefore all have different levels of maturity and slightly
+different sets of built-ins available.
+Using a higher-level language than C addresses many of these problems. It
+allows the language implementor to work at a higher level of abstraction, which
+eases implementation, maintenance and extensions of the virtual machine and
+reduces the size of the code base.  Furthermore, advanced optimizations and
+transformations can be performed. To regain usable performance the
+implementation needs then to be translated to a low-level language again.
+The goal of the PyPy project is to support the implementation of interpreters
+in the high-level language Python. Python is an object oriented dynamic
+language which is becoming more and more popular for scripting use as well as
+for “real” applications. PyPy provides a translation toolchain that helps to
+translate interpreters written in Python to a low-level language such as C and
+the .NET Intermediate Language. To make this translation work, these
+interpreters have to be written in RPython, which is a proper subset of Python
+chosen in such a way that it is amenable to analysis.
+In this talk we give an quick overview of the PyPy project and present a Prolog
+interpreter written in RPython, exploring the possibilities of implementing a
+well-known declarative language in a high-level language (as opposed to C). One
+of the main goals of the implementation was to keep it as simple and as
+extensible as possible (even at the cost of performance). The PyPy tool suite
+is used to reach a reasonable level of performance and to ensure portability to
+various platforms.\footnote{This research was partially supported by the EU funded
+ project: IST 004779 PyPy (PyPy: Implementing Python in Python).} \\
+% ---- Bibliography ----

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