o objetivo deste trabalho é apresentar a implementação em Java de uma máquina abstrata para execução distribuída de programas funcionais. Mostra-se como as facilidades da linguagem Java foram utilizadas para a implementação de uma linguagem funcional paralela que roda os programas funcionais de forma distribuída em uma rede de computadores. Linguagens Funcionais geralmente são implementadas usando uma máquina abstrata para a execução dos programas. Essas máquinas são usualmente máquinas de redução de grafos. Para se rodar os programas funcionais na máquina virtual Java implementou-se a máquina de redução de grafos G-Machine em Java. Nesta dissertação, apresenta-se inicialmente a implementação da G-Machine em Java, realizada como primeira etapa do trabalho e discute-se a abordagem utilizada para essa implementação. Mostra-se em seguida, como os programas funcionais podem ser compilados para rodar nessa G-Machine. Na segunda etapa do trabalho, modifica-se o sistema implementado para permitir a execução distribuída dos programas funcionais. Finalmente apresenta-se uma avaliação de desempenho e mostra-se possíveis trabalhos futuros. / The objective of this work is to present the implementation in the Java language of an abstract machine for distributed execution of functional programs. We show how the Java facilities were used to implement a parallel functional programming language with a distributed runtime system. Functional Languages are usually implemented using an abstract machine to execute programs. These abstract machines are usually graph reduction machines. To mn the functional programs on the Java Virtual Machine we have implemented the G-Machine graph reduction machine in Java. In this text, we first present the implementation of the G-Machine in J ava, and discuss its implementation. Then we show how this implementation was modified to allow distributed execution of functional programs. Finally some benchmarks, possible future works and conclusions are presented.

o objetivo deste trabalho é apresentar a implementação em Java de uma máquina abstrata para execução distribuída de programas funcionais. Mostra-se como as facilidades da linguagem Java foram utilizadas para a implementação de uma linguagem funcional paralela que roda os programas funcionais de forma distribuída em uma rede de computadores. Linguagens Funcionais geralmente são implementadas usando uma máquina abstrata para a execução dos programas. Essas máquinas são usualmente máquinas de redução de grafos. Para se rodar os programas funcionais na máquina virtual Java implementou-se a máquina de redução de grafos G-Machine em Java. Nesta dissertação, apresenta-se inicialmente a implementação da G-Machine em Java, realizada como primeira etapa do trabalho e discute-se a abordagem utilizada para essa implementação. Mostra-se em seguida, como os programas funcionais podem ser compilados para rodar nessa G-Machine. Na segunda etapa do trabalho, modifica-se o sistema implementado para permitir a execução distribuída dos programas funcionais. Finalmente apresenta-se uma avaliação de desempenho e mostra-se possíveis trabalhos futuros. / The objective of this work is to present the implementation in the Java language of an abstract machine for distributed execution of functional programs. We show how the Java facilities were used to implement a parallel functional programming language with a distributed runtime system. Functional Languages are usually implemented using an abstract machine to execute programs. These abstract machines are usually graph reduction machines. To mn the functional programs on the Java Virtual Machine we have implemented the G-Machine graph reduction machine in Java. In this text, we first present the implementation of the G-Machine in J ava, and discuss its implementation. Then we show how this implementation was modified to allow distributed execution of functional programs. Finally some benchmarks, possible future works and conclusions are presented.

o objetivo deste trabalho é apresentar a implementação em Java de uma máquina abstrata para execução distribuída de programas funcionais. Mostra-se como as facilidades da linguagem Java foram utilizadas para a implementação de uma linguagem funcional paralela que roda os programas funcionais de forma distribuída em uma rede de computadores. Linguagens Funcionais geralmente são implementadas usando uma máquina abstrata para a execução dos programas. Essas máquinas são usualmente máquinas de redução de grafos. Para se rodar os programas funcionais na máquina virtual Java implementou-se a máquina de redução de grafos G-Machine em Java. Nesta dissertação, apresenta-se inicialmente a implementação da G-Machine em Java, realizada como primeira etapa do trabalho e discute-se a abordagem utilizada para essa implementação. Mostra-se em seguida, como os programas funcionais podem ser compilados para rodar nessa G-Machine. Na segunda etapa do trabalho, modifica-se o sistema implementado para permitir a execução distribuída dos programas funcionais. Finalmente apresenta-se uma avaliação de desempenho e mostra-se possíveis trabalhos futuros. / The objective of this work is to present the implementation in the Java language of an abstract machine for distributed execution of functional programs. We show how the Java facilities were used to implement a parallel functional programming language with a distributed runtime system. Functional Languages are usually implemented using an abstract machine to execute programs. These abstract machines are usually graph reduction machines. To mn the functional programs on the Java Virtual Machine we have implemented the G-Machine graph reduction machine in Java. In this text, we first present the implementation of the G-Machine in J ava, and discuss its implementation. Then we show how this implementation was modified to allow distributed execution of functional programs. Finally some benchmarks, possible future works and conclusions are presented.

Formal methods play a significant and increasing role in hardware verification, but their effectiveness can be impaired by the ac hoc nature of mainstream hardware languages such as VHDL, Verilog and SystemC, which have convoluted semantics that often necessitate contrived proof techniques. This dissertation investigates the application of formal reasoning to hardware architectures expressed in an alternative class of semantically elegant languages, which support efficient design, whilst also having been developed with proof techniques in mind. A network-on-chip architecture belonging to the SpiNNaker many-core processor is specified in Concurrent Haskell, and a hand proof is presented which verifies a novel routing mechanism by mathematical induction. A subset of Bluespec SystemVerilog (BSV) is embedded in the higher order logic of the PVS theorem prover. Owing to the clean semantics of BSV, application of monadic techniques leads to a surprisingly elegant embedding, in which hardware designs are translated into logic almost verbatim, preserving types and language constructs. Proof strategies are written in the PVS strategy language; these automatically verify temporal logic theorems concerning the resulting monadic expressions, by employing a combination of model checking and deductive reasoning. The subset of BSV which is embedded includes module definition and instantiation, methods, implicit conditions, scheduling attributes, and rule composition using methods from instantiated modules. The aforementioned subset of BSV is also embedded in the specification language of the SAL model checker, and a verification strategy is presented which combines the specialised model checking capabilities of SAL with the diverse proof strategies of PVS.

PrologPF is a parallelising compiler targeting a distributed system of general purpose workstations connected by a relatively low performance network. The source language extends standard Prolog with the integration of higher-order functions. The execution of a compiled PrologPF program proceeds in a similar manner to standard Prolog, but uses oracles in one of two modes. An oracle represents the sequence of clauses used to reach a given point in the problem search tree, and the same PrologPF executable can be used to build oracles, or follow oracles previously generated. The parallelisation strategy used by PrologPF proceeds in two phases, which this research shows can be interleaved. An initial phase searches the problem tree to a limited depth, recording the discovered incomplete paths. In the second phase these paths are allocated to the available processors in the network. Each processor follows its assigned paths and fully searches the referenced subtree, sending solutions back to a control processor. This research investigates the use of the technique with a one-time partitioning of the problem and no further scheduling communication, and with the recursive application of the partitioning technique to effect dynamic work reassignment. For a problem requiring all solutions to be found, execution completes when all the distributed processors have completed the search of their assigned subtrees. If one solution is required, the execution of all the path processors is terminated when the control processor receives the first solution. The presence of the extra-logical Prolog predicate cut in the user program conflicts with the use of oracles to represent valid open subtrees. PrologPF promotes the use of higher-order functional programming as an alternative to the use of cut. The combined language shows that functional support can be added as a consistent extension to standard Prolog.

Studies assessing novice programming proficiency have often found that many students coming out of introductory-level programming courses still struggle with programming. To address this, some researchers have attempted to find and develop ways to better help students succeed in learning to program. This dissertation research contributes to this area by studying the programming processes of students trained through a specific program design curriculum, How to Design Programs (HTDP). HTDP is an introductory-level curriculum for teaching program design that teaches a unique systematic process called the design recipe that leverages the structure of input data to design programs. The design recipe explicitly scaffolds learners through the program design process by asking students to produce intermediate artifacts that represent a given problem in different ways up to a program solution to the problem. Although HTDP is used in several higher-education institutions and some K-12 programs, how HTDP-trained students design programs towards problems, particularly ones with multiple task-components, has not been thoroughly studied. The overarching goal of this dissertation is to gain an understanding and insight into how students use the techniques put forth by the design recipe towards designing solutions for programming problems. I conducted a series of exploratory user studies with HTDP-trained student cohorts from HTDP course instances across two different universities to collect and analyze students’ programming process data in situ. I synthesized findings from each study towards an overall conceptual framework, which serves as a data-grounded theory that captures several facets of HTDP-trained students’ program design process. The main contribution of this work is this theory, which describes: (1) the program design-related skills that students used and the levels of complexity at which they applied these skills, (2) how students’ use of design skills evolve during a course, (3) the interactions between program design skills and course contexts that influenced how students applied their skills, and (4) the programming process patterns by which students approached the programming problems we gave and how these approaches relate towards students’ success with the problems. Using insights from the theory, I describe recommendations toward pedagogical practices for teaching HTDP-based courses, as well as broader reflections towards teaching introductory CS.

The main goal of this thesis was to implement a functional programming (FP) library named Funk that extends C# with support for concepts present in functional programming languages, such as F# and Scala. Funk utilizes many functional programming concepts, including immutability, pattern matching, and various types of monads, together with stronger typing. Introduction of these concepts into C# helps in avoiding many runtime errors and boilerplate code, and it also lets developers write C# code in a declarative rather than in an imperative way, making the day-to-day software development easier and less error-prone. Additionally, the thesis analyzes and compares Funk with existing functional programming libraries such as Language-ext and FuncSharp. Finally, it analyzes the new features of C# 8, which include nullable reference types and pattern matching and compares them with the functionalities of the Funk library.

Our work applies the dataflow algorithm to an area outside its traditional scope: functional languages. Our approach relies on a monadic intermediate language that provides low-level, imperative features like computed jumps and explicit allocations, while at the same time supporting high-level, functional-language features like case discrimination and partial application. We prototyped our work in Haskell using the HOOPL library and this dissertation shows numerous examples demonstrating its use. We prove the efficacy of our approach by giving a novel description of the uncurrying optimization in terms of the dataflow algorithm, as well as a complete implementation of the optimization using HOOPL.

Dependent type theory is a proven technology for verified functional programming in which programs and their correctness proofs may be developed using the same rules in a single formal system. In practice, large portions of programs developed in this way have no computational relevance to the ultimate result of the program and should therefore be removed prior to program execution. In previous work on identifying and removing irrelevant portions of programs, computational irrelevance is usually treated as an intrinsic property of program expressions. We find that such an approach forces programmers to maintain two copies of commonly used datatypes: a computationally relevant one and a computationally irrelevant one. We instead develop an extrinsic notion of computational irrelevance and find that it yields several benefits including (1) avoidance of the above mentioned code duplication problem; (2) an identification of computational irrelevance with a highly general form of parametric polymorphism; and (3) an elective (i.e., user-directed) notion of proof irrelevance. We also develop a program analysis for identifying irrelevant expressions and show how previously studied types embodying computational irrelevance (including subset types and squash types) are expressible in the extension of type theory developed herein.

Due to the complex nature of parallel programming, it is difficult to diagnose and solve performance related problems. Knowledge of program behavior is obtained experimentally, with repeated runs of a slightly modified version of the application or the same code in different environments. In these circumstances, comparative performance analysis can provide meaningful insights into the subtle effects of system and code changes on parallel program behavior by highlighting the difference in performance results across executions. I have designed and implemented modules which extend the PPerfDB performance tool to allow access to existing performance data generated by several commonly used tracing tools. Access occurs from within the experiment management framework provided by PPerfDB for the identification of system parameters, the representation of multiple sets of execution data, and the formulation of data queries. Furthermore, I have designed and implemented an additional module that will generate new data using dynamic instrumentation under the control of PPerfDB. This was done to enable the creation of novel experiments for performance hypothesis testing and to ultimately automate the diagnostic and tuning process. As data from such diverse sources has very different representations, various techniques to allow comparisons are presented. I have generalized the definition of the Performance Difference operator, which automatically detects divergence in multiple data sets, and I have defined an Overlay operation to provide uniform access to both dynamically generated and tracefile based data. The use and application of these new operations along with an indication of some of the issues involved in the creation of a fully automatic comparative profilier is presented via several case studies performed on an IBM SP2 using different versions of an MPI application.