Specializing Interpreters using Offline Partial Deduction

We present the latest version of the Logen partial evaluation system for logic programs. In particular we present new binding-types, and show how they can be used to effectively specialise a wide variety of interpreters.We show how to achieve Jones-optimality in a systematic way for several interpreters. Finally, we present and specialise a non-trivial interpreter for a small functional programming language. Experimental results are also presented, highlighting that the Logen system can be a good basis for generating compilers for high-level languages

Offline Specialisation in Prolog Using a Hand-Written Compiler Generator

The so called "cogen approach" to program specialisation, writing a compiler generator instead of a specialiser, has been used with considerable success in partial evaluation of both functional and imperative languages. This paper demonstrates that the "cogen" approach is also applicable to the specialisation of logic programs (called partial deduction when applied to pure logic programs) and leads to effective specialisers. Moreover, using good binding-time annotations, the speed-ups of the specialised programs are comparable to the speed-ups obtained with online specialisers. The paper first develops a generic approach to offline partial deduction and then a specific offline partial deduction method, leading to the offline system LIX for pure logic programs. While this is a usable specialiser by itself, its specialisation strategy is used to develop the "cogen" system LOGEN. Given a program, a specification of what inputs will be static, and an annotation specifying which calls should be unfolded, LOGEN generates a specialised specialiser for the program at hand. Running this specialiser with particular values for the static inputs results in the specialised program. While this requires two steps instead of one, the efficiency of the specialisation process is improved in situations where the same program is specialised multiple times. The paper also presents and evaluates an automatic binding-time analysis that is able to derive the annotations. While the derived annotations are still suboptimal compared to hand-crafted ones, they enable non-expert users to use the LOGEN system in a fully automated way Finally, LOGEN is extended so as to directly support a large part of Prolog's declarative and non-declarative features and so as to be able to perform so called mixline specialisations. In mixline specialisation some unfolding decisions depend on the outcome of tests performed at specialisation time instead of being hardwired into the specialiser

The Ecce and Logen Partial Evaluators and their Web Interfaces

We present Ecce and Logen, two partial evaluators for Prolog using the online and offline approach respectively. We briefly present the foundations of these tools and discuss various applications. We also present new implementations of these tools, carried out in Ciao Prolog. In addition to a command-line interface new user-friendly web interfaces were developed. These enable non-expert users to specialise logic programs using a web browser, without the need for a local installation

A principled approach to programming with nested types in Haskell

Initial algebra semantics is one of the cornerstones of the theory of modern functional programming languages. For each inductive data type, it provides a Church encoding for that type, a build combinator which constructs data of that type, a fold combinator which encapsulates structured recursion over data of that type, and a fold/build rule which optimises modular programs by eliminating from them data constructed using the buildcombinator, and immediately consumed using the foldcombinator, for that type. It has long been thought that initial algebra semantics is not expressive enough to provide a similar foundation for programming with nested types in Haskell. Specifically, the standard folds derived from initial algebra semantics have been considered too weak to capture commonly occurring patterns of recursion over data of nested types in Haskell, and no build combinators or fold/build rules have until now been defined for nested types. This paper shows that standard folds are, in fact, sufficiently expressive for programming with nested types in Haskell. It also defines buildcombinators and fold/build fusion rules for nested types. It thus shows how initial algebra semantics provides a principled, expressive, and elegant foundation for programming with nested types in Haskell

The construction of high-performance virtual machines for dynamic languages

Dynamic languages, such as Python and Ruby, have become more widely used over the past decade. Despite this, the standard virtual machines for these languages have disappointing performance. These virtual machines are slow, not because methods for achieving better performance are unknown, but because their implementation is hard. What makes the implementation of high-performance virtual machines difficult is not that they are large pieces of software, but that there are fundamental and complex interdependencies between their components. In order to work together correctly, the interpreter, just-in-time compiler, garbage collector and library must all conform to the same precise low-level protocols. In this dissertation I describe a method for constructing virtual machines for dynamic languages, and explain how to design a virtual machine toolkit by building it around an abstract machine. The design and implementation of such a toolkit, the Glasgow Virtual Machine Toolkit, is described. The Glasgow Virtual Machine Toolkit automatically generates a just-in-time compiler, integrates precise garbage collection into the virtual machine, and automatically manages the complex inter-dependencies between all the virtual machine components. Two different virtual machines have been constructed using the GVMT. One is a minimal implementation of Scheme; which was implemented in under three weeks to demonstrate that toolkits like the GVMT can enable the easy construction of virtual machines. The second, the HotPy VM for Python, is a high-performance virtual machine; it demonstrates that a virtual machine built with a toolkit can be fast and that the use of a toolkit does not overly constrain the high-level design. Evaluation shows that HotPy outperforms the standard Python interpreter, CPython, by a large margin, and has performance on a par with PyPy, the fastest Python VM currently available