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Sunday, March 22, 2009

TweetThis! PhD studentship : Adaptive Parallel Optimising Compilation using Machine Learning

Multi-core processors are the most viable means to delivering sustainable performance. However, this potential cannot be realised unless the application has been well parallelised. Unfortunately, efficient parallelisation of a sequential program is a challenging and error-prone task. It is generally agreed that manual code parallelisation by expert programmers results in the most streamlined parallel implementation, but at the same time this is the most costly and time-consuming approach. Parallelising compiler technology, on the other hand, has the potential to greatly reduce cost and time-to-market while ensuring formal correctness of the resulting parallel code. Given that the underlying processor architecture will change many times throughout the lifetime of the code, we would like parallel programs that are performance future proof too.

The project student will investigate new compiler directed approaches to delivering performance in a multi-core environment where the data input, concurrent workload and underlying architecture are evolving. Probabilistic analysis can be used to determine program parallelism which can then be mapped to available resources. Central to this work will be the use of machine learning as a technique to learn and adapt the parallel code to this changing parallel landscape. The CaRD group at Edinburgh is internationally leading in the use of machine learning for compiler and architecture co-design and optimisation - this will form the backbone to this project.

Funding Notes
A studentship, sponsored by the Centre for Numerical Algorithms and Intelligent Software (NAIS), will be available in September 2009.

Suitable candidates will have a first degree in Computer Science and a strong interest in parallel programming and the interface between programs, compilers and computer architecture.

Applications should be made via EUCLID, the University's online application system, by the end of April 2009.

Please mention “NAIS Studentship” in the application. The successful candidate will be notified by the end of May 2009.

Further details of the project and studentship (http://homepages.inf.ed.ac.uk/mob/phdplace.html)
EUCLID online application system (http://www.ed.ac.uk/studying/postgraduate/finder/details.php?id=492)

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