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UCPy: Reverse-Engineering Python John Aycock David Pereira Georges Jodoin
 

Summary: UCPy: Reverse-Engineering Python
John Aycock David Pereira Georges Jodoin
Department of Computer Science
University of Calgary
Calgary, Alberta, Canada T2N 1N4
{aycock|pereira|jodoin}@cpsc.ucalgary.ca
1 Introduction
One of the recurring topics in the Python community is how to make Python pro-
grams run faster. Typically, a set of solutions is proposed which include: adding
static type inference; somehow compiling programs into native code; translat-
ing Python programs into Parrot/Lisp/.net code; applying research results from
dynamically-typed language implementation. Progress has been made on some
of these, such as Psyco [8], but many of these proposed solutions are qualified
by the caveat no one has the time/resources to work on it.
In the Programming Languages Lab at the University of Calgary we have a
research project underway, UCPy, whose short-term goal is to examine ways we
can make Python run faster. We have learned some lessons through our design
and implementation work to date, about both Python and the undertaking of
such a project, which we present in this paper.
2 Design of UCPy

  

Source: Aycock, John - Department of Computer Science, University of Calgary

 

Collections: Computer Technologies and Information Sciences