Towards Bridging the Gap Between Programming Languages and Partial Evaluation

International audiencePartial evaluation is a program-transformation technique that automatically specializes a program with respect to user-supplied invariants. Despite successful applications in areas such as graphics, operating systems, and software engineering, partial evaluators have yet to achieve widespread use. One reason is the difficulty of adequately describing specialization opportunities. Indeed, under-specialization or over-specialization often occurs, without any direct feedback to the user as to the source of the problem. We have developed a high-level, module-based language allowing the programmer to guide the choice of both the code to specialize and the invariants to exploit during the specialization process. To ease the use of partial evaluation, the syntax of this language is similar to the declaration syntax of the target language of the partial evaluator. To provide feedback to the programmer, declarations are checked throughout the analyses performed by partial evaluation. The language has been successfully used by a signal-processing expert in the design of a specializable Forward Error Correction component

Automatic Specialization of Protocol Stacks in OS kernels

Awarded best paperInternational audienceFast and optimized protocol stacks play a major role in the performance of network services. This role is especially important in embedded class systems, where performance metrics such as data throughput tend to be limited by the CPU. It is common on such systems, to have protocol stacks that are optimized by hand for better performance and smaller code footprint. In this paper, we propose a strategy to automate this process. Our approach uses program specialization, and enables applications using the network to request specialized code based on the current usage scenario. The specialized code is generated dynamically and loaded in the kernel to be used by the application. We have successfully applied our approach to the TCP/IP implementation in the Linux kernel and used the optimized protocol stack in existing applications. These applications were minimally modied to request the specialization of code based on the current usage context, and to use the specialized code generated instead of its generic version. Specialization can be performed locally, or deferred to a remote specialization server using a novel mechanism [1]. Experiments conducted on three platforms show that the specialized code runs about 25% faster and its size reduces by up to 20 times. The throughput of the protocol stack improves by up to 21%

AllenRV: an Extensible Monitor for Multiple Complex Specifications with High Reactivity

International audienceAllenRV is a tool for monitoring temporal specifications, designed for ensuring good scalability in terms of size and number of formulae, and high reactivity. Its features reflect this design goal. For ensuring scalability in the number of formulae, it can simultaneously monitor a set of formulae written in past and future, next-free LTL, with some metric extensions; their efficient simultaneous monitoring is supported by a let construct allowing to share computations between formulae. For ensuring scalability in the size of formulae, it allows defining new abstractions as user-defined operators, which take discrete time boolean signals as arguments, but also constant parameters such as delays. For ensuring high reactivity, its monitoring algorithm does not require clock tick events, unlike many other tools. This is achieved by recomputing output signals both upon input signals changes and upon internally generated timeout events relative to such changes. As a consequence, monitoring remains efficient on arbitrarily fine-grained time domains. AllenRV is implemented by extending the existing Allen language and compiler, initially targeting ubiquitous applications using binary sensors, with temporal logic operators and a comprehensive library of user-defined operators on top of them. The most complex of these operators, including a complete adaptation of Allen-logic relations as selection operators, are proven correct with respect to their defined semantics. Thus, AllenRV offers an open platform for cooperatively developing increasingly complex libraries of high level, general or domain-specific, temporal operators and abstractions, without compromising correctness

A Symmetric Approach to Compilation and Decompilation

Just as specializing a source interpreter can achieve compilation from a source language to a target language, we observe that specializing a target interpreter can achieve compilation from the target language to the source language. In both cases, the key issue is the choice of whether to perform an evaluation or to emit code that represents this evaluation. We substantiate this observation by specializing two source interpreters and two target interpreters. We first consider a source language of arithmetic expressions and a target language for a stack machine, and then the lambda-calculus and the SECD-machine language. In each case, we prove that the target-to-source compiler is a left inverse of the source-to-target compiler, i.e., it is a decompiler. In the context of partial evaluation, compilation by source-interpreter specialization is classically referred to as a Futamura projection. By symmetry, it seems logical to refer to decompilation by target-interpreter specialization as a Futamura embedding