I founded UNIDAPT Group to pioneers and promotes radically new practical techniques to overcome the complexity of current and future architectures, compilers, operating systems and programming environments. We are working on creating self-tuning intelligent adaptive systems, automating and improving architecture and compiler design (particularly for future heterogeneous multi-core systems), optimizing and parallelizing applications to improve performance, power consumption, size and fault-tolerance, reduce cost and time to market based on machine learning, artificial intelligence, statistical methods and biologically inspired techniques. This is critical to be able to continue innovation in science and industry (bioinformatics, medicine, physics, chemistry, finances, gaming, etc) that demands ever-increasing computing resources while placing strict requirements on systems.

Together with the MILEPOST colleagues we are currently developing MILEPOST GCC - the first machine learning based self-tuning research compiler described more in our GCC Summit'08 paper (many thanks to Cupertino Miranda for extending Interactive Compilation Interface, Edwin Bonilla for extending machine learning techniques, Mircea Namolaru for integrating static program feature extractor to MILEPOST GCC and John Thomson for performance evaluation). We plan to release MILEPOST GCC v1.0 by the end of 2008. In the mean time, you can register your interest here to receive information about (pre)releases of the MILEPOST GCC. You can also subscribe for UNIDAPT announcements or contact me if you would like to use it for your research and collaborate on some projects.

We are currently seeking a Compiler and Performance Engineer to work with us in the MILEPOST project until at least July 2009 and collaborators to extend MILEPOST GCC.

Tuning hardwired compiler optimizations for rapidly evolving hardware makes porting an optimizing compiler for each new platform extremely challenging. Our radical approach is to develop a modular, extensible, self-tuning intelligent compiler that automatically learns the best optimization heuristics based on combining feedback-directed iterative compilation and machine learning. MILEPOST GCC is a machine learning based compiler that automatically adjusts its optimization heuristics to improve execution time, code size, or compilation time of specific programs on different architectures. Currently, we use several iterative search strategies within CCC framework to find combinations of good optimization flags to substitute GCC default optimization levels for a particular architecture (such as -O0,-O1,-O2,-O3,-Os which we will not need in the future adaptive compilers) or tune optimization passes on a function-level for a particular program. Our preliminary experimental results show that it is possible to considerably reduce execution time of various benchmarks (MiBench, MediaBench, EEMBC, SPEC) on a range of platforms (x86, x8664, IA64, ARC, Longson/Godson, etc) entirely automatically. MILEPOST GCC can be used interactively in research on adaptive computing through the Interactive Compilation Interface (we currently support optimization pass selection and reordering, plugins and event mechanism to invoke any passes on demand (such as program feature extraction pass) or tune cost-models of specific transformations within passes).

Some history: Soon after starting research on iterative optimizations in 1999, I realized that current search methods are too slow in large non-linear optimization space and new techniques are needed to prune it. Since I had some experience with neural networks and machine learning during my undergraduate and M.S. studies, I wanted to apply some of these techniques to compiler optimizations to quickly capture main features and behavior of optimization spaces, speed up the search process and enable optimization knowledge reuse. However, lacking manpower and support ;) we could only proceed with this research in 2003 after teaming up with our colleagues from Machine Learning Group from the Institute for Adaptive and Neural Computation (University of Edinburgh).

We currently evaluate different statistical search and machine learning techniques to analyze and narrow down large optimizations spaces considerably and automatically learn best optimization settings among different programs and architectures using static and dynamic features. I am developing Continuous Collective Compilation framework to enable continuous program and architecture optimization knowledge reuse using ICI, UNIDAPT, MiDataSets coupled with machine learning and statistical search techniques. This framework is currently used in several research projects and will be publicly available at the end of 2008.

We also use ML and statistical search techniques to automatically build a performance model for a new architecture to predict the impact of different program transformations on this architecture using statistical and machine learning techniques, and a limited number of training runs. We use either statical or run-time features of the program to quickly predict the performance of all transformations for a given program on a new architecture thus speeding up the evaluation of compiler/architecture performance by several orders of magnitude. We developed a novel technique to characterize programs or architectures using program reaction to optimizations (transformations).