A Narrowing-based Instantiation Rule for Rewriting-based Fold/Unfold Transformations

AbstractIn this paper we show how to transfer some developments done in the field of functionallogic programming (FLP) to a pure functional setting (FP). More exactly, we propose a complete fold/unfold based transformation system for optimizing lazy functional programs. Our main contribution is the definition of a safe instantiation rule which is used to enable effective unfolding steps based on rewriting. Since instantiation has been traditionally considered problematic in FP, we take advantage of previous experiences in the more general setting of FLP where instantiation is naturally embedded into an unfolding rule based on narrowing. Inspired by the so called needed narrowing strategy, our instantiation rule inherits the best properties of this refinement of narrowing. Our proposal optimizes previous approaches (that require more transformation effort) defined in the specialized literature of pure FP by anticipating bindings on unifiers used to instantiate a given program rule and by generating redexes at different positions on instantiated rules in order to enable subsequent unfolding steps. As a consequence, our correct/complete technique avoids redundant rules and preserves the natural structure of programs

Bidirectional Programming and its Applications

Many problems in programming involve pairs of computations that cancel out each other’s effects; some examples include parsing/printing, embed- ding/projection, marshalling/unmarshalling, compressing/de-compressing etc. To avoid duplication of effort, the paradigm of bidirectional programming aims at to allow the programmer to write a single program that expresses both computations. Despite being a promising idea, existing studies mainly focus on the view-update problem in databases and its variants; and the impact of bidirectional programming has not reached the wider community. The goal of this thesis is to demonstrate, through concrete language designs and case studies, the relevance of bidirectional programming, in areas of computer science that have not been previously explored. In this thesis, we will argue for the importance of bidirectional programming in programming language design and compiler implementation. As evidence for this, we will propose a technique for incremental refactoring, which relies for its correctness on a bidirectional language and its properties, and devise a framework for implementing program transformations, with bidirectional properties that allow program analyses to be carried out in the transformed program, and have the results reported in the source program. Our applications of bidirectional programming to new areas bring up fresh challenges. This thesis also reflects on the challenges, and studies their impact to the design of bidirectional systems. We will review various design goals, including expressiveness, robustness, updatability, efficiency and easy of use, and show how certain choices, especially regarding updatability, can have significant influence on the effectiveness of bidirectional systems

Scalable String and Suffix Sorting: Algorithms, Techniques, and Tools

This dissertation focuses on two fundamental sorting problems: string sorting and suffix sorting. The first part considers parallel string sorting on shared-memory multi-core machines, the second part external memory suffix sorting using the induced sorting principle, and the third part distributed external memory suffix sorting with a new distributed algorithmic big data framework named Thrill.Comment: 396 pages, dissertation, Karlsruher Instituts f\"ur Technologie (2018). arXiv admin note: text overlap with arXiv:1101.3448 by other author

Proceedings of the Workshop on Change of Representation and Problem Reformulation

The proceedings of the third Workshop on Change of representation and Problem Reformulation is presented. In contrast to the first two workshops, this workshop was focused on analytic or knowledge-based approaches, as opposed to statistical or empirical approaches called 'constructive induction'. The organizing committee believes that there is a potential for combining analytic and inductive approaches at a future date. However, it became apparent at the previous two workshops that the communities pursuing these different approaches are currently interested in largely non-overlapping issues. The constructive induction community has been holding its own workshops, principally in conjunction with the machine learning conference. While this workshop is more focused on analytic approaches, the organizing committee has made an effort to include more application domains. We have greatly expanded from the origins in the machine learning community. Participants in this workshop come from the full spectrum of AI application domains including planning, qualitative physics, software engineering, knowledge representation, and machine learning

Dynamic Compilation for Functional Programs

Diese Arbeit behandelt die dynamische, zur Laufzeit stattfindende Übersetzung und Optimierung funktionaler Programme. Ziel der Optimierung ist die erhöhte Laufzeiteffizient der Programme, die durch die compilergesteuerte Eliminierung von Abstraktionen der Programmiersprache erreicht wird. Bei der Implementierung objekt-orientierter Programmiersprachen werden bereits seit mehreren Jahrzehnten Compiler-Techniken zur Laufzeit eingesetzt, um objekt-orientierte Programme effizient ausführen zu können. Spätestens seit der Einführung der Programmiersprache Java und ihres auf einer abstrakten Maschine basierenden Ausführungsmodells hat sich die Praktikabilität dieser Implementierungstechnik gezeigt. Viele Eigenschaften moderner Programmiersprachen konnten erst durch den Einsatz dynamischer Transformationstechniken effizient realisiert werden, wie zum Beispiel das dynamische Nachladen von Programmteilen (auch über Netzwerke), Reflection sowie verschiedene Sicherheitslösungen (z.B. Sandboxing). Ziel dieser Arbeit ist zu zeigen, dass rein funktionale Programmiersprachen auf ähnliche Weise effizient implementiert werden können, und sogar Vorteile gegenüber den allgemein eingesetzten objekt-orientierten Sprachen bieten, was die Effizienz, Sicherheit und Korrektheit von Programmen angeht. Um dieses Ziel zu erreichen, werden in dieser Arbeit Implementierungstechniken entworfen bzw. aus bestehenden Lösungen weiterentwickelt, welche die dynamische Kompilierung und Optimierung funktionaler Programme erlauben: zum einen präsentieren wir eine Programmzwischendarstellung (getypte dynamische Continuation-Passing-Style-Darstellung), welche sich zur dynamischen Kompilierung und Optimierung eignet. Basierend auf dieser Darstellung haben wir eine Erweiterung zur verzögerten und selektiven Codeerzeugung von Programmteilen entwickelt. Der wichtigste Beitrag dieser Arbeit ist die dynamische Spezialisierung zur Eliminierung polymorpher Funktionen und Datenstrukturen, welche die Effizienz funktionaler Programme deutlich steigern kann. Die präsentierten Ergebnisse experimenteller Messungen eines prototypischen Ausführungssystems belegen, dass funktionale Programme effizient dynamisch kompiliert werden können.This thesis is about dynamic translation and optimization of functional programs. The goal of the optimization is increased run-time efficiency, which is obtained by compiler-directed elimination of programming language abstractions. Object-oriented programming languages have been implemented for several decades using run-time compilation techniques. With the introduction of the Java programming language and its virtual machine-based execution model, the practicability of this implementation method for real-world applications has been proved. Many aspects of modern programming languages, such as dynamic loading and linking of code (even across networks), reflection and security solutions (e.g., sandboxing) can be realized efficiently only by using dynamic transformation techniques. The goal of this work is to show that functional programming languages can be efficiently implemented in a similar way, and that these languages even offer advantages when compared to more common object-oriented languages. Efficiency, security and correctness of programs is easier to ensure in the functional setting. Towards this goal, we design and develop implementation techniques to enable dynamic compilation and optimization of functional programming languages: we describe an intermediate representation for functional programs (typed dynamic continuation-passing style), which is well suited for dynamic compilation. Based on this representation, we have developed an extension for incremental and selective code generation. The main contribution of this work shows how dynamic specialization of polymorphic functions and data structures can increase the run-time efficiency of functional programs considerably. We present the results of experimental measurements for a prototypical implementation, which prove that functional programs can efficiently be dynamically compiled

Workshop on Database Programming Languages

These are the revised proceedings of the Workshop on Database Programming Languages held at Roscoff, Finistère, France in September of 1987. The last few years have seen an enormous activity in the development of new programming languages and new programming environments for databases. The purpose of the workshop was to bring together researchers from both databases and programming languages to discuss recent developments in the two areas in the hope of overcoming some of the obstacles that appear to prevent the construction of a uniform database programming environment. The workshop, which follows a previous workshop held in Appin, Scotland in 1985, was extremely successful. The organizers were delighted with both the quality and volume of the submissions for this meeting, and it was regrettable that more papers could not be accepted. Both the stimulating discussions and the excellent food and scenery of the Brittany coast made the meeting thoroughly enjoyable. There were three main foci for this workshop: the type systems suitable for databases (especially object-oriented and complex-object databases,) the representation and manipulation of persistent structures, and extensions to deductive databases that allow for more general and flexible programming. Many of the papers describe recent results, or work in progress, and are indicative of the latest research trends in database programming languages. The organizers are extremely grateful for the financial support given by CRAI (Italy), Altaïr (France) and AT&T (USA). We would also like to acknowledge the organizational help provided by Florence Deshors, Hélène Gans and Pauline Turcaud of Altaïr, and by Karen Carter of the University of Pennsylvania

Structural foundations for differentiable programming

This dissertation supports the broader thesis that categorical semantics is a powerful tool to study and design programming languages. It focuses on the foundational aspects of differentiable programming in a simply typed functional setting. Although most of the category theory used can be boiled down to a more elementary presentation, its influence was certainly key in obtaining the results presented in this dissertation. The conciseness of certain proofs and the compactness of certain definitions and insights were made easier thanks to my background in category theory. Backpropagation is the key algorithm that allows fast learning on neural networks. It enabled some of the impressive recent advancements in machine learning. With models of increasing complexity, data structures equally complex are required, which calls for the ability to go beyond standard differentiability. This emerging generalization was coined as differentiable programming. The idea is to allow users to write expressive programs representing (a generalization of) differentiable functions, whose gradient computation can be automated using automatic differentiation. In this dissertation, I lay some foundations for differentiable programming. This is done in three ways. Firstly, I present a simple higher-order functional language and define automatic differentiation as a structure-preserving program transformation. The language is given a denotational semantics using diffeological spaces, and it is shown that the transformation is correct, i.e. that AD produces programs that do compute gradients of the original programs, using a logical relations argument. Secondly, I extend the language from the previously described chapter to introduce new expressive program constructs such as conditionals and recursion. In such a setting, even first-order programs may represent functions that need not be differentiable. I introduce better-fitted denotational semantics for such a language and show how to extend AD to such a setting and what guarantees about AD now hold. This extended language models the more realistic needs in expressiveness that can be found in the literature, e.g. in modern probabilistic programming languages. Thirdly, I present detailed applications of the developed theory. I first show a general recipe for extending AD to non-trivial new types and new primitives. I then show how the guarantees about AD are sufficient for usage in certain applications, such as the change of variable formula of stochastic gradient descent, but how it may not be sufficient, for instance, in simple gradient descent. Finally, more applications in the specific context of probabilistic programming are explored. First, a denotational proof that the trace semantics of a probabilistic program is almost everywhere differentiable is given. Second, a characterization of posterior distributions of probabilistic programs valued in Euclidean spaces is obtained: they have densities with respect to (w.r.t.) some sum-of-Hausdorff measure on a countable union of smooth manifolds. Overall, these contributions give us better insights into differentiable programming. They form a foundational setting to study the differentiability-like properties of realistic complex programs, beyond usual settings such as differentiability or convexity. They give general recipes to prove some properties of such programs and modularly extend automatic differentiation to richer contexts with new types and primitives