Team Phoenix

Overall Objectives
Scientific Foundations
Application Domains
New Results
Contracts and Grants with Industry
Other Grants and Activities

Section: Scientific Foundations

Adaptation Tools and Techniques

To further the applicability of our approach, we have strengthened and extended adaptation tools and techniques. We have produced a detailed description of the key program analysis for imperative specialization, namely binding-time analysis [23] . This analysis is at the heart of our program specializer for C, named Tempo [23] . We have examined the importance of the accuracy of these analyses to successfully specialize existing programs. This study was conducted in the context of systems software [32] .

Tempo is the only specializer which enables programs to be specialized both at compile time and run time. Yet, specialization is always performed in one stage. As a consequence, this process cannot be factorized even if specialization values become available at multiple stages. We present a realistic and flexible approach to achieving efficient incremental run-time specialization [29] . Rather than developing new techniques, our strategy for incremental run-time specialization reuses existing technology by iterating a specialization process. Our approach has been implemented in Tempo.

While program specialization encodes the result of early computations into a new program, data specialization encodes the result of early computations into data structures. Although aiming at the same goal, namely processing early computations, these two forms of specialization have always been studied separately. The Compose group has proposed an extension of Tempo to perform both program and data specialization [21] . We showed how these two strategies can be integrated in a single specializer. Most notably, having both strategies enabled us to assess their benefits, limitations and their combination on a variety of programs.

Interpreters and run-time compilers are increasingly used to cope with heterogeneous architectures, evolving programming languages, and dynamically-loaded code. Although solving the same problem, these two strategies are very different. Interpreters are simple to implement but yield poor performance. Run-time compilation yields better performance, but is costly to implement. One approach to reconciling these two strategies is to develop interpreters for simplicity but to use specialization to achieve efficiency. Additionally, a specializer like Tempo can remove the interpretation overhead at compile time as well as at run time. We have conducted experiments to assess the benefits of applying specialization to interpreters [40] . These experiments have involved bytecode and structured-language interpreters. Our experimental data showed that specialization of structured-language interpreters can yield performance comparable to that of the compiled code of an optimizing compiler.

Besides targeting C, we developed the first program specializer for an object-oriented language. This specializer, named JSpec, processes Java programs [37] . JSpec is constructed from existing tools. Java programs are translated into C using our Java compiler, named Harissa. Then, the resulting C programs are specialized using Tempo. The specialized C program is executed in the Harissa environment. JSpec has been used for various applications and has shown to produce significant speedups [39] .


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