Model-guided Automatic Performance Tuning

Speaker: Dr Apan Qasem

Time: 11:30am-12:30pm, February 29th, 2008

Location: Nueces 201 Conference Room

Abstract:

Over the last several decades we have witnessed tremendous change in  the 
landscape of computer architecture. New architectures have emerged at a 
rapid pace with greater computing capabilities, that have often exceeded 
our expectations. However, the rapid rate of architectural innovations has 
also been a source of major concern for the high-performance community. 
Each new architecture or even a new model of a given architecture has 
brought with it  new features that have added to the complexity of the 
target platform. As a result, it has become increasingly difficult to 
exploit the full potential of modern architectures for complex scientific 
applications. The gap between the theoretical peak and the actual achievable 
performance has increased with every step of architectural innovation. As we 
head towards the boundaries of  Moore's Law and multi-core platforms become 
more pervasive, this performance gap is likely to increase. The current 
practice in dealing with the changing nature of computer architecture and 
its ever increasing complexity is the laborious process of manual retargeting 
of code which often costs many person-months for just a single application. 
My talk will describe an automatic tuning strategy that aims to eliminate 
the need for manual retargeting and retuning of scientific applications. I 
will give a brief overview of our tuning framework and explain the core ideas 
behind our strategy of pruning the large and complex search space of 
optimization parameters.