Do you want to publish a course? Click here

Automating the Functional Correspondence between Higher-Order Evaluators and Abstract Machines

122   0   0.0 ( 0 )
 Added by Maciej Buszka
 Publication date 2021
and research's language is English




Ask ChatGPT about the research

The functional correspondence is a manual derivation technique transforming higher-order evaluators into the semantically equivalent abstract machines. The transformation consists of two well-known program transformations: translation to continuation-passing style that uncovers the control flow of the evaluator and Reynoldss defunctionalization that generates a first-order transition function. Ever since the transformation was first described by Danvy et al. it has found numerous applications in connecting known evaluators and abstract machines, but also in discovering new abstract machines for a variety of $lambda$-calculi as well as for logic-programming, imperative and object-oriented languages. We present an algorithm that automates the functional correspondence. The algorithm accepts an evaluator written in a dedicated minimal functional meta-language and it first transforms it to administrative normal form, which facilitates program analysis, before performing selective translation to continuation-passing style, and selective defunctionalization. The two selective transformations are driven by a control-flow analysis that is computed by an abstract interpreter obtained using the abstracting abstract machines methodology, which makes it possible to transform only the desired parts of the evaluator. The article is accompanied by an implementation of the algorithm in the form of a command-line tool that allows for automatic transformation of an evaluator embedded in a Racket source file and gives fine-grained control over the resulting machine.



rate research

Read More

232 - Yuting Wang 2017
We argue that the implementation and verification of compilers for functional programming languages are greatly simplified by employing a higher-order representation of syntax known as Higher-Order Abstract Syntax or HOAS. The underlying idea of HOAS is to use a meta-language that provides a built-in and logical treatment of binding related notions. By embedding the meta-language within a larger programming or reasoning framework, it is possible to absorb the treatment of binding structure in the object language into the meta-theory of the system, thereby greatly simplifying the overall implementation and reasoning processes. We develop the above argument in this thesis by presenting and demonstrating the effectiveness of an approach to the verified implementation of compiler transformations for functional programs that exploits HOAS. In this approach, transformations on functional programs are first articulated in the form of rule-based relational specifications. These specifications are rendered into programs in the language lambda Prolog. On the one hand, these programs serve directly as implementations. On the other hand, they can be used as input to the Abella system which allows us to prove properties about them and thereby about the implementations. Both lambda Prolog and Abella support the use of the HOAS approach. Thus, they constitute a framework that can be used to test out the benefits of the HOAS approach in verified compilation. We use them to implement and verify a compiler for a representative functional programming language that embodies the transformations that form the core of many compilers for such languages. In both the programming and the reasoning phases, we show how the use of the HOAS approach significantly simplifies the representation, manipulation, analysis and reasoning of binding structure.
Language-integrated query is a popular and powerful programming construct allowing database queries and ordinary program code to interoperate seamlessly and safely. Language-integrated query techniques rely on classical results about the nested relational calculus stating that its queries can be algorithmically translated to SQL, as long as their result type is a flat relation. Cooper and others advocated higher-order nested relational calculi as a basis for language-integrated queries in functional languages such as Links and F#. However, the translation of higher-order relational queries to SQL relies on a rewrite system for which no strong normalization proof has been published: a previous proof attempt does not deal correctly with rewrite rules that duplicate subterms. This paper fills the gap in the literature, explaining the difficulty with the previous attempt, and showing how to extend the $toptop$-lifting approach of Lindley and Stark to accommodate duplicating rewrites. We also show how to extend the proof to a recently-introduced calculus for heterogeneous queries mixing set and multiset semantics.
We present a unification of refinement and Hoare-style reasoning in a foundational mechanized higher-order distributed separation logic. This unification enables us to prove formally in the Coq proof assistant that concrete implementations of challenging distributed systems refine more abstract models and to combine refinement-style reasoning with Hoare-style program verification. We use our logic to prove correctness of concrete implementations of two-phase commit and single-decree Paxos by showing that they refine their abstract TLA+ specifications. We further use our notion of refinement to transfer fairness assumptions on program executions to model traces and then transfer liveness properties of fair model traces back to program executions, which enables us to prove liveness properties such as strong eventual consistency of a concrete implementation of a Conflict-Free Replicated Data Type and fair termination of a concurrent program.
We present a modular semantic account of Bayesian inference algorithms for probabilistic programming languages, as used in data science and machine learning. Sophisticated inference algorithms are often explained in terms of composition of smaller parts. However, neither their theoretical justification nor their implementation reflects this modularity. We show how to conceptualise and analyse such inference algorithms as manipulating intermediate representations of probabilistic programs using higher-order functions and inductive types, and their denotational semantics. Semantic accounts of continuous distributions use measurable spaces. However, our use of higher-order functions presents a substantial technical difficulty: it is impossible to define a measurable space structure over the collection of measurable functions between arbitrary measurable spaces that is compatible with standard operations on those functions, such as function application. We overcome this difficulty using quasi-Borel spaces, a recently proposed mathematical structure that supports both function spaces and continuous distributions. We define a class of semantic structures for representing probabilistic programs, and semantic validity criteria for transformations of these representations in terms of distribution preservation. We develop a collection of building blocks for composing representations. We use these building blocks to validate common inference algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo. To emphasize the connection between the semantic manipulation and its traditional measure theoretic origins, we use Kocks synthetic measure theory. We demonstrate its usefulness by proving a quasi-Borel counterpart to the Metropolis-Hastings-Green theorem.
121 - Masahiro Hamano 2012
RNA interference (RNAi) is a mechanism whereby small RNAs (siRNAs) directly control gene expression without assistance from proteins. This mechanism consists of interactions between RNAs and small RNAs both of which may be single or double stranded. The target of the mechanism is mRNA to be degraded or aberrated, while the initiator is double stranded RNA (dsRNA) to be cleaved into siRNAs. Observing the digital nature of RNAi, we represent RNAi as a Minsky register machine such that (i) The two registers hold single and double stranded RNAs respectively, and (ii) Machines instructions are interpreted by interactions of enzyme (Dicer), siRNA (with RISC com- plex) and polymerization (RdRp) to the appropriate registers. Interpreting RNAi as a computational structure, we can investigate the computational meaning of RNAi, especially its complexity. Initially, the machine is configured as a Chemical Ground Form (CGF), which generates incorrect jumps. To remedy this problem, the system is remodeled as recursive RNAi, in which siRNA targets not only mRNA but also the machine instructional analogues of Dicer and RISC. Finally, probabilistic termination is investigated in the recursive RNAi system.
comments
Fetching comments Fetching comments
Sign in to be able to follow your search criteria
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا