Design and implementation of a behaviorally typed programming system for web services

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática.

Speculative execution by using software transactional memory

Dissertação apresentada na Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa para a obtenção do Grau de Mestre em Engenharia Informática.

Type inference for conversation types

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Transactional filesystems

Dissertação de Mestrado em Engenharia Informática

Type-based protocol conformance and aliasing control in concurrent java programs

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

A modular distributed transactional memory framework

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

The dilution effect: The influence of expertise and abstraction on consumer's judgements of products

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Data-centric concurrency control on the java programming language

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Preventing atomicity violations with contracts

Concurrent programming is a difficult and error-prone task because the programmer must reason about multiple threads of execution and their possible interleavings. A concurrent program must synchronize the concurrent accesses to shared memory regions, but this is not enough to prevent all anomalies that can arise in a concurrent setting. The programmer can misidentify the scope of the regions of code that need to be atomic, resulting in atomicity violations and failing to ensure the correct behavior of the program. Executing a sequence of atomic operations may lead to incorrect results when these operations are co-related. In this case, the programmer may be required to enforce the sequential execution of those operations as a whole to avoid atomicity violations. This situation is specially common when the developer makes use of services from third-party packages or modules. This thesis proposes a methodology, based on the design by contract methodology, to specify which sequences of operations must be executed atomically. We developed an analysis that statically verifies that a client of a module is respecting its contract, allowing the programmer to identify the source of possible atomicity violations.

A system’s approach to cache hierarchy-aware decomposition of data-parallel computations

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Heterogeneous computing with an algorithmic skeleton framework

The Graphics Processing Unit (GPU) is present in almost every modern day personal computer. Despite its specific purpose design, they have been increasingly used for general computations with very good results. Hence, there is a growing effort from the community to seamlessly integrate this kind of devices in everyday computing. However, to fully exploit the potential of a system comprising GPUs and CPUs, these devices should be presented to the programmer as a single platform. The efficient combination of the power of CPU and GPU devices is highly dependent on each device’s characteristics, resulting in platform specific applications that cannot be ported to different systems. Also, the most efficient work balance among devices is highly dependable on the computations to be performed and respective data sizes. In this work, we propose a solution for heterogeneous environments based on the abstraction level provided by algorithmic skeletons. Our goal is to take full advantage of the power of all CPU and GPU devices present in a system, without the need for different kernel implementations nor explicit work-distribution.To that end, we extended Marrow, an algorithmic skeleton framework for multi-GPUs, to support CPU computations and efficiently balance the work-load between devices. Our approach is based on an offline training execution that identifies the ideal work balance and platform configurations for a given application and input data size. The evaluation of this work shows that the combination of CPU and GPU devices can significantly boost the performance of our benchmarks in the tested environments, when compared to GPU-only executions.