PhD Studentships in Optimising Compilation at
University of Edinburgh
There are currently vacancies for EU funded PhD Studentships
within the
CArD group at the Institute for
Computer Systems Architecture within the School of Informatics
University of Edinburgh, Scotland, UK.
The studentships are for 3 years and in the areas of
Auto-Parallelisation and Compilers that Learn to Optimise.
Candidates should hold a good first degree, be self-motivated and
possess strong mathematical and computational skills. A relevant MSc or
knowledge of compiler optimisation would be
useful but not obligatory.
The compiler group investigates a diverse range of
problems from machine learning based compilation to auto-parallelising for
embedded DSP systems. There are close links with other UK, European
and US groups and there is ample opportunity for creative study and
interaction.
The start date for the studentship is flexible. To apply, complete
the application form available from
http://www.ed.ac.uk/studying/postgraduate/applications/forms.html,
specifying that you wish to be considered for an EU studentship in
Auto-Parallelisation or Learning
Compilation. Applications should be made as soon
as possible and by the end of March 2006 at the latest. Informal
enquiries may be made to Dr Michael O'Boyle, mob@inf.ed.ac.uk
using the subject line "EU studentships".
PhD in Auto-Parallelisation
The aim of this project is to develop advanced compiler techniology
that can take emerging applications and automatically map them on to
the next generation multi-core processors such as the IBM Cell. This
PhD will involve new research into discovering parallelism within
multimedia and streaming applications going beyond standard data
parallel analysis. The project will also investigate cost-effective
mapping of parallelism to processors which may include dynamic or
adaptive compilation
PhD in Compilers that Learn to Optimise
The overall objective of this project is to develop a compiler
framework that can automatically learn how to optimise
programs. Rather than hard-coding a compiler strategy for each
platform, we aim to develop a novel portable compiler approach that
can automatically tune itself to any fixed hardware and can improve
its performance over time. This is achieved by employing machine
learning approaches to optimisation, where the machine learning
algorithm first learns the optimisation space and then automatically
derives a compilation strategy that attempts to generate the ``best''
optimised version of any user program. Such an approach, if
successful, will have a wide range of applications. It will allow
portability and performance of compilers across platforms, eliminating
the human compiler-development bottleneck.
Feb 2006