Design and implementation of a low-level language for interaction nets

Interaction nets are a graphical model of computation based on a restricted form of graph rewriting. A specific net can represent a program with a user-defined set of nodes and computation is modelled by a user-defined set of rewrite rules. This very simple model has had great success in modelling sharing in computation (specifically in the lambda calculus), and there is potential for generating a new theoretical foundation of parallel computation since all computation steps are local and thus can be implemented in parallel. This thesis is about the implementation of interaction nets. Specifically, for the first contributions we define a low-level language as an object language for the compilation of interaction nets. We study the efficiency and properties of different data structures, and focus on the management of the rewriting process which is usually hidden in the graph rewriting system. We provide experimental data comparing the different choices of data structures and select one for further development. For the compilation of nets and rules into this language, we show an optimisation such that allocated memory for agents is reused, and thus we obtain optimal efficiency for the rewriting process. The second part of this thesis describes extensions of interaction nets so that they can be used as a programming language. Interaction nets in their pure form are quite restrictive in expressive power. By extending the notions of agents and rules we can express computation more naturally, yet still preserve the good properties (such as strong confluence) of the rewriting system. We then implement a selection of algorithms using and extending the compilation techniques developed in the first part of the thesis. We also demonstrate experimental results on multi-core CPUs, using the Posix-thread library, thus realising some of the potential for parallel implementation mentioned above

An Analytical Approach to Programs as Data Objects

This essay accompanies a selection of 32 articles (referred to in bold face in the text and marginally marked in the bibliographic references) submitted to Aarhus University towards a Doctor Scientiarum degree in Computer Science.The author's previous academic degree, beyond a doctoral degree in June 1986, is an "Habilitation à diriger les recherches" from the Université Pierre et Marie Curie (Paris VI) in France; the corresponding material was submitted in September 1992 and the degree was obtained in January 1993.The present 32 articles have all been written since 1993 and while at DAIMI.Except for one other PhD student, all co-authors are or have been the author's students here in Aarhus

Programs as Polypeptides

We describe a visual programming language for defining behaviors manifested by reified actors in a 2D virtual world that can be compiled into programs comprised of sequences of combinators that are themselves reified as actors. This makes it possible to build programs that build programs from components of a few fixed types delivered by diffusion using processes that resemble chemistry as much as computation.Comment: in European Conference on Artificial Life (ECAL '15), York, UK, 201

Higher-Order, Data-Parallel Structured Deduction

State-of-the-art Datalog engines include expressive features such as ADTs (structured heap values), stratified aggregation and negation, various primitive operations, and the opportunity for further extension using FFIs. Current parallelization approaches for state-of-art Datalogs target shared-memory locking data-structures using conventional multi-threading, or use the map-reduce model for distributed computing. Furthermore, current state-of-art approaches cannot scale to formal systems which pervasively manipulate structured data due to their lack of indexing for structured data stored in the heap. In this paper, we describe a new approach to data-parallel structured deduction that involves a key semantic extension of Datalog to permit first-class facts and higher-order relations via defunctionalization, an implementation approach that enables parallelism uniformly both across sets of disjoint facts and over individual facts with nested structure. We detail a core language, $DL_s$, whose key invariant (subfact closure) ensures that each subfact is materialized as a top-class fact. We extend $DL_s$ to Slog, a fully-featured language whose forms facilitate leveraging subfact closure to rapidly implement expressive, high-performance formal systems. We demonstrate Slog by building a family of control-flow analyses from abstract machines, systematically, along with several implementations of classical type systems (such as STLC and LF). We performed experiments on EC2, Azure, and ALCF's Theta at up to 1000 threads, showing orders-of-magnitude scalability improvements versus competing state-of-art systems

A Rational Deconstruction of Landin's SECD Machine

Landin's SECD machine was the first abstract machine for the lambda-calculus viewed as a programming language. Both theoretically as a model of computation and practically as an idealized implementation, it has set the tone for the subsequent development of abstract machines for functional programming languages. However, and even though variants of the SECD machine have been presented, derived, and invented, the precise rationale for its architecture and modus operandi has remained elusive. In this article, we deconstruct the SECD machine into a lambda-interpreter, i.e., an evaluation function, and we reconstruct lambda-interpreters into a variety of SECD-like machines. The deconstruction and reconstructions are transformational: they are based on equational reasoning and on a combination of simple program transformations--mainly closure conversion, transformation into continuation-passing style, and defunctionalization. The evaluation function underlying the SECD machine provides a precise rationale for its architecture: it is an environment-based eval-apply evaluator with a callee-save strategy for the environment, a data stack of intermediate results, and a control delimiter. Each of the components of the SECD machine (stack, environment, control, and dump) is therefore rationalized and so are its transitions. The deconstruction and reconstruction method also applies to other abstract machines and other evaluation functions. It makes it possible to systematically extract the denotational content of an abstract machine in the form of a compositional evaluation function, and the (small-step) operational content of an evaluation function in the form of an abstract machine