Code generation using a backtracking LR parser

Although the parsing phase of the modern compiler has been automated in a machine independent fashion, the diversity of computer architectures inhibits automating the code generation phase. During code generation, some intermediate representation of a source program is transformed into actual machine instructions. The need for portable compilers has driven research towards the automatic generation of code generators.;This research investigates the use of a backtracking LR parser that treats code generation as a series of tree transformations

Survey on Instruction Selection: An Extensive and Modern Literature Review

Instruction selection is one of three optimisation problems involved in the code generator backend of a compiler. The instruction selector is responsible of transforming an input program from its target-independent representation into a target-specific form by making best use of the available machine instructions. Hence instruction selection is a crucial part of efficient code generation. Despite on-going research since the late 1960s, the last, comprehensive survey on the field was written more than 30 years ago. As new approaches and techniques have appeared since its publication, this brings forth a need for a new, up-to-date review of the current body of literature. This report addresses that need by performing an extensive review and categorisation of existing research. The report therefore supersedes and extends the previous surveys, and also attempts to identify where future research should be directed.Comment: Major changes: - Merged simulation chapter with macro expansion chapter - Addressed misunderstandings of several approaches - Completely rewrote many parts of the chapters; strengthened the discussion of many approaches - Revised the drawing of all trees and graphs to put the root at the top instead of at the bottom - Added appendix for listing the approaches in a table See doc for more inf

The Design and Implementation of SPARK, a Toolkit for Implementing Domain-Specific Languages

SPARK is a toolkit for implementing domain-specific languages in Python. It is somewhat unusual in that its intended audience includes users who do not necessarily have a background in compilation; this choice impacts both the design and implementation of SPARK. We introduce SPARK in this paper and discuss major issues that have arisen in its design and ongoing development

Lily: A parser generator for LL(1) languages

This paper discusses the design and implementation of Lily, a language for generating LL(1) language parsers, originally designed by Dr. Thomas J. Sager of the University of Missouri--Rolla. A method for the automatic generation of parser tables is described which creates small, highly optimized tables, suitable for conversion to minimal perfect hash functions. An implementation of Lily is discussed with attention to design goals, implementation of parser table generation, and table optimization techniques. Proposals are made detailing possibilities for further augmentation of the system. Examples of Lily programs are given as well as a manual for the system

Supervised Machine Learning Models for Fake News Detection

Fake news or the distribution of disinformation has become one of the most challenging issues in society. News and information are churned out across online websites and platforms in real-time, with little or no way for the viewing public to determine what is real or manufactured. But an awareness of what we are consuming online is becoming apparent and efforts are underway to explore how we separate fake content from genuine and truthful information. The most challenging part of fake news is determining how to spot it. In technology, there are ways to help us do this. Supervised machine learning helps us to identify in a labelled dataset if a piece of information is fake or not. However, machine learning can be a black-box tool - a device, system or object which can be viewed in terms of its inputs and outputs – that focuses on one aspect of the problem and in doing so, isn’t addressing the bigger picture. To solve this issue, it is very important to understand how it works. The process of data pre-processing and the dataset labelling is part of this understanding. It is also worth knowing the algorithms mechanisms in order to choose the best one for the proposed project. Evaluating machine learning algorithms model is one way to get better results. Changing paths within algorithms is not a bad thing if it is addressing the limitations within. With this project, we have done just this, changing from Sports news detection using Twitter API to labelled datasets and as a result we have an original Gofaas dataset, Gofaas library R package and Gofaas WebApp. Machine Learning is a demanding subject but fascinating at the same time. We hope this modest project helps people to face these challenges and learn from our findings accordingly

Supervised Machine Learning Models for Fake News Detection

Fake news or the distribution of disinformation has become one of the most challenging issues in society. News and information are churned out across online websites and platforms in real-time, with little or no way for the viewing public to determine what is real or manufactured. But an awareness of what we are consuming online is becoming apparent and efforts are underway to explore how we separate fake content from genuine and truthful information. The most challenging part of fake news is determining how to spot it. In technology, there are ways to help us do this. Supervised machine learning helps us to identify in a labelled dataset if a piece of information is fake or not. However, machine learning can be a black-box tool - a device, system or object which can be viewed in terms of its inputs and outputs – that focuses on one aspect of the problem and in doing so, isn’t addressing the bigger picture. To solve this issue, it is very important to understand how it works. The process of data pre-processing and the dataset labelling is part of this understanding. It is also worth knowing the algorithms mechanisms in order to choose the best one for the proposed project. Evaluating machine learning algorithms model is one way to get better results. Changing paths within algorithms is not a bad thing if it is addressing the limitations within. With this project, we have done just this, changing from Sports news detection using Twitter API to labelled datasets and as a result we have an original Gofaas dataset, Gofaas library R package and Gofaas WebApp. Machine Learning is a demanding subject but fascinating at the same time. We hope this modest project helps people to face these challenges and learn from our findings accordingly

Estudo e desenvolvimento de sistemas de geração de back-ends do processo de compilação

O back-end de um compilador agrupa todo um conjunto de tarefas cuja implementação é intrinsecamente dependente das características do processador para o qual se pretende gerar código. A rápida evolução da industria dos processadores e microcontroladores levou esta área de desenvolvimento de software a realizar fortes investimentos na pesquisa de meios que permitissem dar uma resposta rápida e de qualidade à procura verificada. É dentro deste contexto que surge o tema e o trabalho desenvolvido ao longo desta tese de mestrado, que pretende de alguma forma sintetizar o que já se encontra feito e propor algumas soluções, que apesar de individualmente não serem originais permitem, quando em conjunto, vislumbrar alternativas aos sistemas já concebidos e avançar um pouco mais na área de investigação dos geradores de código final e optimizadores. O trabalho aqui descrito é extremamente abrangente para uma qualquer tese, cobrindo todas as áreas do processo de compilação a partir da análise semântica até à geração do código máquina, passando pela apresentação de modelos de compiladores, representação da informação, sistemas de análise de fluxo de controlo e de dados, alocação de registos local e global, selecção de instruções e geração de selectores, optimização de código a vários níveis, etc. É ainda de referir que do trabalho desenvolvido resultou o Back-End Development System, que como o nome indica é um sistema de apoio ao desenvolvimento das tarefas de back-end de um compilador. The back-end of a compiler gathers a group of tasks, whose implementation is directly dependent on the features of the processor for which machine code is intended to be generated. The fast evolution of processors and micro-controllers industry lead this area of software development to perform strong investments in the research of means, which would give a fast and proper answer to the demand. It is within this context that the theme and the work carried on through this thesis emerges. The aim of this work is to synthesise what has already been done and to give some solutions which, although individually not original, when put together, they allow alternatives to the pre-established systems and move on a little further in the research of generators of final code and optimisers. This work is extremely wide-ranging, covering all areas of the compiling process, going from the semantic analyses till the generation of machine code. It also contains the presentation of models of compilers, representation of information, control and data flow analysis, local and global registers allocation, instructions selection and generation of selectors, code optimisation at several levels, etc. It is also important to refer that from the development work emerged the Back-End Development System, which, as the name itself indicates, is a software system to support development of back-end tasks of a compiler

Estudo e desenvolvimento de sistemas de geração de back-ends do processo de compilação

O back-end de um compilador agrupa todo um conjunto de tarefas cuja implementação é intrinsecamente dependente das características do processador para o qual se pretende gerar código. A rápida evolução da industria dos processadores e microcontroladores levou esta área de desenvolvimento de software a realizar fortes investimentos na pesquisa de meios que permitissem dar uma resposta rápida e de qualidade à procura verificada. É dentro deste contexto que surge o tema e o trabalho desenvolvido ao longo desta tese de mestrado, que pretende de alguma forma sintetizar o que já se encontra feito e propor algumas soluções, que apesar de individualmente não serem originais permitem, quando em conjunto, vislumbrar alternativas aos sistemas já concebidos e avançar um pouco mais na área de investigação dos geradores de código final e optimizadores. O trabalho aqui descrito é extremamente abrangente para uma qualquer tese, cobrindo todas as áreas do processo de compilação a partir da análise semântica até à geração do código máquina, passando pela apresentação de modelos de compiladores, representação da informação, sistemas de análise de fluxo de controlo e de dados, alocação de registos local e global, selecção de instruções e geração de selectores, optimização de código a vários níveis, etc. É ainda de referir que do trabalho desenvolvido resultou o Back-End Development System, que como o nome indica é um sistema de apoio ao desenvolvimento das tarefas de back-end de um compilador. The back-end of a compiler gathers a group of tasks, whose implementation is directly dependent on the features of the processor for which machine code is intended to be generated. The fast evolution of processors and micro-controllers industry lead this area of software development to perform strong investments in the research of means, which would give a fast and proper answer to the demand. It is within this context that the theme and the work carried on through this thesis emerges. The aim of this work is to synthesise what has already been done and to give some solutions which, although individually not original, when put together, they allow alternatives to the pre-established systems and move on a little further in the research of generators of final code and optimisers. This work is extremely wide-ranging, covering all areas of the compiling process, going from the semantic analyses till the generation of machine code. It also contains the presentation of models of compilers, representation of information, control and data flow analysis, local and global registers allocation, instructions selection and generation of selectors, code optimisation at several levels, etc. It is also important to refer that from the development work emerged the Back-End Development System, which, as the name itself indicates, is a software system to support development of back-end tasks of a compiler

Implementation of a Modula 2 subset compiler supporting a \u27C\u27 language interface using commonly available UNIX tools

Modula 2 has been proposed as an appropriate language for systems programming. Smaller than PASCAL but more structured than \u27C\ Modula 2 is intended to be relatively easy to implement. A realization of a subset of Modula 2 for the MC68010 microprocessor is presented. Widely available UNIX tools and the \u27C language are used for the implementation. A mechanism for calling \u27C language functions from Modula 2 (and vice versa) is suggested. Critical source code, grammar, and an extensive bibliography pertinent to the implementation are included as appendices