Static Computation and Reflection

Thesis (PhD) - Indiana University, Computer Sciences, 2008Most programming languages do not allow programs to inspect their static type information or perform computations on it. C++, however, lets programmers write template metaprograms, which enable programs to encode static information, perform compile-time computations, and make static decisions about run-time behavior. Many C++ libraries and applications use template metaprogramming to build specialized abstraction mechanisms, implement domain-specific safety checks, and improve run-time performance. Template metaprogramming is an emergent capability of the C++ type system, and the C++ language specification is informal and imprecise. As a result, template metaprogramming often involves heroic programming feats and often leads to code that is difficult to read and maintain. Furthermore, many template-based code generation and optimization techniques rely on particular compiler implementations, rather than language semantics, for performance gains. Motivated by the capabilities and techniques of C++ template metaprogramming, this thesis documents some common programming patterns, including static computation, type analysis, generative programming, and the encoding of domain-specific static checks. It also documents notable shortcomings to current practice, including limited support for reflection, semantic ambiguity, and other issues that arise from the pioneering nature of template metaprogramming. Finally, this thesis presents the design of a foundational programming language, motivated by the analysis of template metaprogramming, that allows programs to statically inspect type information, perform computations, and generate code. The language is specified as a core calculus and its capabilities are presented in an idealized setting

The Reflex Sandbox : an experimentation environment for an aspect-oriented Kernel

Reflex es un núcleo versátil para la programación orientada aspectos en Java. Provee de las abstracciones básicas, estructurales y de comportamiento, que permiten implementar una variedad de técnicas orientadas a aspectos. Esta tesis estudia dos tópicos fundamentales. En primer lugar, el desarrollo formal, utilizando el lenguaje Haskell, de las construcciones fundamentales del modelo Reflex para reflexión parcial de comportamiento. Este desarrollo abarca el diseño de un lenguaje, llamado Kernel, el cual es una extensión reflexiva de un lenguaje orientado a objetos simple. La semántica operacional del lenguaje Kernel es presentada mediante una máquina de ejecución abstracta. El otro tópico fundamental que estudia esta tesis es validar que el modelo de reflexión parcial de comportamiento es suficientemente expresivo para proveer de semántica a un subconjunto del lenguaje AspectJ. Con este fin, se desarrolló el Reflex Sandbox: un ambiente de experimentación en Haskell para el modelo Reflex. Tanto el desarrollo formal del modelo de reflexión parcial de comportamiento como la validación del soporte de AspectJ, son estudiados en el contexto del Reflex Sandbox. La validación abarca la definición de un lenguaje orientado a aspectos que caracteriza el enfoque de AspectJ a la programación orientada a aspectos, así como la definición de su máquina de ejecución abstracta. También se presenta un compilador que transforma programas escritos en este lenguaje al lenguaje Kernel. Este proceso de compilación provee los fundamentos para entender como dicha transformación puede ser realizada. El proceso de compilación también fue implementado en Java, pero transformando programas AspectJ a programas Reflex. También se presentan mediciones preliminares del desempeño de un programa compilado y ejecutado en Reflex y un programa compilado, y ejecutado con el compilador AspectJ

Achieving Efficient Work-Stealing for Data-Parallel Collections

In modern programming high-level data-structures are an important foundation for most applications. With the rise of the multi-core era, there is a growing trend of supporting data-parallel collection operations in general purpose programming languages and platforms. To facilitate object-oriented reuse these operations are highly parametric, incurring abstraction performance penalties. Furthermore, data-parallel operations must scale when used in problems with irregular workloads. Work-stealing is a proven load-balancing technique when it comes to irregular workloads, but general purpose work-stealing also suffers from abstraction penalties. In this paper we present a generic design of a data-parallel collections framework based on work-stealing for shared-memory architectures. We show how abstraction penalties can be overcome through callsite specialization of data-parallel operations instances. Moreover, we show how to make work-stealing fine-grained and efficient when specialized for particular data-structures. We experimentally validate the performance of different data-structures and data-parallel operations, achieving up to 60X better performance with abstraction penalties eliminated and 3X higher speedups by specializing work-stealing compared to existing approaches

Applications of reprogrammability in algorithm acceleration

This doctoral thesis consists of an introductory part and eight appended publications, which deal with hardware-based reprogrammability in algorithm acceleration with a specific emphasis on the possibilities offered by modern large-scale Field Programmable Gate Arrays (FPGAs) in computationally demanding applications. The historical evolution of both the theoretical and technological paths culminating in the introduction of reprogrammable logic devices is first outlined. This is followed by defining the commonly used terms in the thesis. The reprogrammable logic market is surveyed, and the architectural structures and the technological reasonings behind them are described in detail. As reprogrammable logic lies between Application Specific Integrated Circuits (ASICs) and general-purpose microprocessors in the implementation spectrum of electronics systems, special attention has been paid to differentiate these three implementation approaches. This has been done to emphasize, that reprogrammable logic offers much more than just a low-volume replacement for ASICs. Design systems for reprogrammable logic are investigated, as the learning curve associated with them is the main hurdle for software-oriented designers for using reprogrammable logic devices. The theoretically important topic of partial reprogrammability is described in detail, but it is concluded, that the practical problems in designing viable development platforms for partially reprogrammable systems will hinder its wide-spread adoption. The main technical, design-oriented, and economic applicability factors of reprogrammable logic are laid out. The main advantages of reprogrammable logic are their suitability for fine-grained bit-level parallelizable computing with a short time-to-market and low upfront costs. It is also concluded, that the main opportunities for reprogrammable logic lie in the potential of high-level design systems, and the ever-growing ASIC design gap. On the other hand, most power-conscious mass-market portable products do not seem to offer major new market potential for reprogrammable logic. The appended publications are examined and compared to contemporaneous research at other research institutions. The conclusion is that for relatively wide classes of well-defined computation problems, reprogrammable logic offers a more efficient solution than a software-centered approach, with a much shorter production cycle than is the case with ASICs.reviewe

System Synthesis from a Monadic Functional Language

Embedded systems typically combine a mixture of heterogeneous components, some that are software executing on general purpose CPUs, some that are off-the-shelf hardware components, and some that are application specific circuitry. A major challenge when designing and implementing such systems is the dissimilar models of computation exhibited by hardware and software targets. To successfully navigate this challenge, components must be implemented in a way that does not unnecessarily bias the implementation towards either computational model, allowing the components to be retargeted as application requirements change. This dissertation presents an approach to this problem using a functional programming language extended with monadic imperative and concurrency effects. We argue that these language features allow components to be implemented and compiled to either hardware or software targets. To demonstrate this claim, we detail the design of such a language, Oread. Moreover, we describe the compilation of Oread to both hardware, via VHDL, and software, via C. Using these compilation techniques, we describe the development of a digital processing component in Oread and the integration of that component into a larger system

On Lock-Free Work-stealing Iterators for Parallel Data Structures

With the rise of multicores, there is a trend of supporting data-parallel collection operations in general purpose programming languages. These operations are highly parametric, incurring abstraction performance penalties. Furthermore, data-parallel operations must scale when applied to irregular workloads. Work-stealing is a proven technique for load balancing irregular workloads, but general purpose work-stealing also suffers abstraction penalties. We present a generic data-parallel collections design based on work-stealing for shared-memory architectures that overcomes abstraction penalties through callsite specialization of data-parallel operation instances. Moreover, we introduce \textit{work-stealing iterators} that allow fine-grained and efficient work-stealing for particular data-structures. By eliminating abstraction penalties and making work-stealing data-structure-aware we achieve up to 60x better performance compared to JVM-based approaches and 3x speedups compared to tools such as Intel TBB

The modular compilation of effects

The introduction of new features to a programming language often requires that its compiler goes to the effort of ensuring they are introduced in a manner that does not interfere with the existing code base. Engineers frequently find themselves changing code that has already been designed, implemented and (ideally) proved correct, which is bad practice from a software engineering point of view. This thesis addresses the issue of constructing a compiler for a source language that is modular in the computational features that it supports. Utilising a minimal language that allows us to demonstrate the underlying techniques, we go on to introduce a significant range of effectful features in a modular manner, showing that their syntax can be compiled independently, and that source languages containing multiple features can be compiled by making use of a fold. In the event that new features necessitate changes in the underlying representation of either the source language or that of the compiler, we show that our framework is capable of incorporating these changes with minimal disruption. Finally, we show how the framework we have developed can be used to define both modular evaluators and modular virtual machines

The modular compilation of effects

The introduction of new features to a programming language often requires that its compiler goes to the effort of ensuring they are introduced in a manner that does not interfere with the existing code base. Engineers frequently find themselves changing code that has already been designed, implemented and (ideally) proved correct, which is bad practice from a software engineering point of view. This thesis addresses the issue of constructing a compiler for a source language that is modular in the computational features that it supports. Utilising a minimal language that allows us to demonstrate the underlying techniques, we go on to introduce a significant range of effectful features in a modular manner, showing that their syntax can be compiled independently, and that source languages containing multiple features can be compiled by making use of a fold. In the event that new features necessitate changes in the underlying representation of either the source language or that of the compiler, we show that our framework is capable of incorporating these changes with minimal disruption. Finally, we show how the framework we have developed can be used to define both modular evaluators and modular virtual machines

Modular Interpreters in Haskell

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Convergence of program transformers in the metric space of trees

AbstractIn recent years increasing consensus has emerged that program transformers, e.g. partial evaluation and unfold/fold transformations, should terminate; a compiler should stop even if it performs fancy optimizations! A number of techniques to ensure termination of program transformers have been invented, but their correctness proofs are sometimes long and involved. We present a framework for proving termination of program transformers, cast in the metric space of trees. We first introduce the notion of an abstract program transformer; a number of well-known program transformers can be viewed as instances of this notion. We then formalize what it means that an abstract program transformer terminates and give a general sufficient condition for an abstract program transformer to terminate. We also consider some specific techniques for satisfying the condition. As applications we show that termination of some well-known program transformers either follows directly from the specific techniques or is easy to establish using the general condition. Our framework facilitates simple termination proofs for program transformers. Also, since our framework is independent of the language being transformed, a single correctness proof can be given in our framework for program transformers that use essentially the same technique in the context of different languages. Moreover, it is easy to extend termination proofs for program transformers to accommodate changes to these transformers. Finally, the framework may prove useful for designing new termination techniques for program transformers