Verification of Timed Automata Using Rewrite Rules and Strategies

ELAN is a powerful language and environment for specifying and prototyping deduction systems in a language based on rewrite rules controlled by strategies. Timed automata is a class of continuous real-time models of reactive systems for which efficient model-checking algorithms have been devised. In this paper, we show that these algorithms can very easily be prototyped in the ELAN system. This paper argues through this example that rewriting based systems relying on rules and strategies are a good framework to prototype, study and test rather efficiently symbolic model-checking algorithms, i.e. algorithms which involve combination of graph exploration rules, deduction rules, constraint solving techniques and decision procedures

Two Decades of Maude

This paper is a tribute to José Meseguer, from the rest of us in the Maude team, reviewing the past, the present, and the future of the language and system with which we have been working for around two decades under his leadership. After reviewing the origins and the language's main features, we present the latest additions to the language and some features currently under development. This paper is not an introduction to Maude, and some familiarity with it and with rewriting logic are indeed assumed.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

DyCL: Dynamic Neural Network Compilation Via Program Rewriting and Graph Optimization

DL compiler's primary function is to translate DNN programs written in high-level DL frameworks such as PyTorch and TensorFlow into portable executables. These executables can then be flexibly executed by the deployed host programs. However, existing DL compilers rely on a tracing mechanism, which involves feeding a runtime input to a neural network program and tracing the program execution paths to generate the computational graph necessary for compilation. Unfortunately, this mechanism falls short when dealing with modern dynamic neural networks (DyNNs) that possess varying computational graphs depending on the inputs. Consequently, conventional DL compilers struggle to accurately compile DyNNs into executable code. To address this limitation, we propose \tool, a general approach that enables any existing DL compiler to successfully compile DyNNs. \tool tackles the dynamic nature of DyNNs by introducing a compilation mechanism that redistributes the control and data flow of the original DNN programs during the compilation process. Specifically, \tool develops program analysis and program transformation techniques to convert a dynamic neural network into multiple sub-neural networks. Each sub-neural network is devoid of conditional statements and is compiled independently. Furthermore, \tool synthesizes a host module that models the control flow of the DyNNs and facilitates the invocation of the sub-neural networks. Our evaluation demonstrates the effectiveness of \tool, achieving a 100\% success rate in compiling all dynamic neural networks. Moreover, the compiled executables generated by \tool exhibit significantly improved performance, running between $1.12\times$ and $20.21\times$ faster than the original DyNNs executed on general-purpose DL frameworks.Comment: This paper has been accepted to ISSTA 202

Compiling language definitions: the ASF+SDF compiler

The ASF+SDF Meta-Environment is an interactive language development environment whose main application areas are definition of domain-specific languages, generation of program analysis and transformation tools, production of software renovation tools, and general specification and prototyping. It uses conditional rewrite rules to define the dynamic semantics and other tool-oriented aspects of languages, so the effectiveness of the generated tools is critically dependent on the quality of the rewrite rule implementation. The ASF+SDF rewrite rule compiler generates C code, thus taking advantage of C's portability and the sophisticated optimization capabilities of current C compilers as well as avoiding potential abstract machine interface bottlenecks. It can handle large(10 000+ rule) language definitions and uses an efficient run-time storage scheme capable of handling large (1 000 000+ node) terms. Term storage uses maximal subterm sharing (hash-consing), which turns out to be more effective in the case of ASF+SDF than in Lisp or SML. Extensive benchmarking has shown the time and space performance of the generated code to be as good as or better than that of the best current rewrite rule and functional language compilers

Formal Compiler Implementation in a Logical Framework

The task of designing and implementing a compiler can be a difficult and error-prone process. In this paper, we present a new approach based on the use of higher-order abstract syntax and term rewriting in a logical framework. All program transformations, from parsing to code generation, are cleanly isolated and specified as term rewrites. This has several advantages. The correctness of the compiler depends solely on a small set of rewrite rules that are written in the language of formal mathematics. In addition, the logical framework guarantees the preservation of scoping, and it automates many frequently-occurring tasks including substitution and rewriting strategies. As we show, compiler development in a logical framework can be easier than in a general-purpose language like ML, in part because of automation, and also because the framework provides extensive support for examination, validation, and debugging of the compiler transformations. The paper is organized around a case study, using the MetaPRL logical framework to compile an ML-like language to Intel x86 assembly. We also present a scoped formalization of x86 assembly in which all registers are immutable