Modern compilers have limited ability to exploit the performance improvement potential of complex transformation compositions. This is due to the ad-hoc nature of different transformations. Various frameworks have been proposed to provide a unified representation of different transformations, among them is Pugh's Unified Transformation Framework (UTF)[10]. It presents a unified and systematic representation of iteration reordering transformations and their arbitrary combination, which results in a large and complex optimisation space for a compiler to explore. This paper presents a heuristic search algorithm capable of efficiently locating good program optimisations within such a space. Preliminary experimental results on Java show that it can achieve an average speedup of 1.14 on Linux+Celeron and 1.10 on Windows+PentiumPro, and more than 75% of the maximum performance available can be obtained within 20 evaluations or less.

This paper describes a portable, machine learning-based approach to Java optimisation. This approach uses an instance-based learning scheme to select good transformations drawn from Pugh's Unified Transformation Framework[11]. This approach was implemented and applied to a number of numerical Java benchmarks on two platforms. Using this scheme, we are able to gain over 70% of the performance improvement found when using an exhaustive iterative search of the best compiler optimisations. Thus we have a scheme that gives a high level of portable performance without any excessive compilations.

Modern compilers have limited ability to exploit the performance improvement potential of complex transformation compositions. This is due to the ad-hoc nature of di#erent transformations. Various frameworks have been proposed to provide a unified representation of di#erent transformations, among them is Pugh's Unified Transformation Framework (UTF) (Kelly and Pugh (1993)). It presents a unified and systematic representation of iteration reordering transformations and their arbitrary combination, which results in a large and complex optimisation space for a compiler to explore. This paper presents a heuristic search algorithm capable of e#ciently locating good program optimisations within such a space. Preliminary experimental results on Java show that it can achieve an average speedup of 1.14 on Linux+Celeron and 1.10 on Windows+PentiumPro, and more than 75% of the maximum performance available can be obtained within 20 evaluations or less.