Utilização de GPGPUs para a identificação de objectos em imagens tomográficas

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Reconstrução/processamento de imagem médica com GPU em tomossíntese

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

A framework for scientific computing with GPUs

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Optimization of breast tomosynthesis image reconstruction using parallel computing

Breast cancer is the most common cancer among women, being a major public health problem. Worldwide, X-ray mammography is the current gold-standard for medical imaging of breast cancer. However, it has associated some well-known limitations. The false-negative rates, up to 66% in symptomatic women, and the false-positive rates, up to 60%, are a continued source of concern and debate. These drawbacks prompt the development of other imaging techniques for breast cancer detection, in which Digital Breast Tomosynthesis (DBT) is included. DBT is a 3D radiographic technique that reduces the obscuring effect of tissue overlap and appears to address both issues of false-negative and false-positive rates. The 3D images in DBT are only achieved through image reconstruction methods. These methods play an important role in a clinical setting since there is a need to implement a reconstruction process that is both accurate and fast. This dissertation deals with the optimization of iterative algorithms, with parallel computing through an implementation on Graphics Processing Units (GPUs) to make the 3D reconstruction faster using Compute Unified Device Architecture (CUDA). Iterative algorithms have shown to produce the highest quality DBT images, but since they are computationally intensive, their clinical use is currently rejected. These algorithms have the potential to reduce patient dose in DBT scans. A method of integrating CUDA in Interactive Data Language (IDL) is proposed in order to accelerate the DBT image reconstructions. This method has never been attempted before for DBT. In this work the system matrix calculation, the most computationally expensive part of iterative algorithms, is accelerated. A speedup of 1.6 is achieved proving the fact that GPUs can accelerate the IDL implementation.

Towards an algorithmic skeleton framework for programming the Intel R Xeon PhiTM processor

The Intel R Xeon PhiTM is the first processor based on Intel’s MIC (Many Integrated Cores) architecture. It is a co-processor specially tailored for data-parallel computations, whose basic architectural design is similar to the ones of GPUs (Graphics Processing Units), leveraging the use of many integrated low computational cores to perform parallel computations. The main novelty of the MIC architecture, relatively to GPUs, is its compatibility with the Intel x86 architecture. This enables the use of many of the tools commonly available for the parallel programming of x86-based architectures, which may lead to a smaller learning curve. However, programming the Xeon Phi still entails aspects intrinsic to accelerator-based computing, in general, and to the MIC architecture, in particular. In this thesis we advocate the use of algorithmic skeletons for programming the Xeon Phi. Algorithmic skeletons abstract the complexity inherent to parallel programming, hiding details such as resource management, parallel decomposition, inter-execution flow communication, thus removing these concerns from the programmer’s mind. In this context, the goal of the thesis is to lay the foundations for the development of a simple but powerful and efficient skeleton framework for the programming of the Xeon Phi processor. For this purpose we build upon Marrow, an existing framework for the orchestration of OpenCLTM computations in multi-GPU and CPU environments. We extend Marrow to execute both OpenCL and C++ parallel computations on the Xeon Phi. We evaluate the newly developed framework, several well-known benchmarks, like Saxpy and N-Body, will be used to compare, not only its performance to the existing framework when executing on the co-processor, but also to assess the performance on the Xeon Phi versus a multi-GPU environment.

Parallel programming in biomedical signal processing

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Acceleration of physics simulation engine through OpenCL

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Reconstrução de imagem médica de mamografia por emissão de positrões (PEM) com GPU

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica

Algorithmic skeleton framework for the orchestration of GPU 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.

Multiresponsive supramolecular systems for information processing at the molecular level

Dissertação para obtenção do Grau de Doutor em Química Sustentável

Environmental data management in the Sado Estuary

Estuaries are perhaps the most threatened environments in the coastal fringe; the coincidence of high natural value and attractiveness for human use has led to conflicts between conservation and development. These conflicts occur in the Sado Estuary since its location is near the industrialised zone of Peninsula of Setúbal and at the same time, a great part of the Estuary is classified as a Natural Reserve due to its high biodiversity. These facts led us to the need of implementing a model of environmental management and quality assessment, based on methodologies that enable the assessment of the Sado Estuary quality and evaluation of the human pressures in the estuary. These methodologies are based on indicators that can better depict the state of the environment and not necessarily all that could be measured or analysed. Sediments have always been considered as an important temporary source of some compounds or a sink for other type of materials or an interface where a great diversity of biogeochemical transformations occur. For all this they are of great importance in the formulation of coastal management system. Many authors have been using sediments to monitor aquatic contamination, showing great advantages when compared to the sampling of the traditional water column. The main objective of this thesis was to develop an estuary environmental management framework applied to Sado Estuary using the DPSIR Model (EMMSado), including data collection, data processing and data analysis. The support infrastructure of EMMSado were a set of spatially contiguous and homogeneous regions of sediment structure (management units). The environmental quality of the estuary was assessed through the sediment quality assessment and integrated in a preliminary stage with the human pressure for development. Besides the earlier explained advantages, studying the quality of the estuary mainly based on the indicators and indexes of the sediment compartment also turns this methodology easier, faster and human and financial resource saving. These are essential factors to an efficient environmental management of coastal areas. Data management, visualization, processing and analysis was obtained through the combined use of indicators and indices, sampling optimization techniques, Geographical Information Systems, remote sensing, statistics for spatial data, Global Positioning Systems and best expert judgments. As a global conclusion, from the nineteen management units delineated and analyzed three showed no ecological risk (18.5 % of the study area). The areas of more concern (5.6 % of the study area) are located in the North Channel and are under strong human pressure mainly due to industrial activities. These areas have also low hydrodynamics and are, thus associated with high levels of deposition. In particular the areas near Lisnave and Eurominas industries can also accumulate the contamination coming from Águas de Moura Channel, since particles coming from that channel can settle down in that area due to residual flow. In these areas the contaminants of concern, from those analyzed, are the heavy metals and metalloids (Cd, Cu, Zn and As exceeded the PEL guidelines) and the pesticides BHC isomers, heptachlor, isodrin, DDT and metabolits, endosulfan and endrin. In the remain management units (76 % of the study area) there is a moderate impact potential of occurrence of adverse ecological effects and in some of these areas no stress agents could be identified. This emphasizes the need for further research, since unmeasured chemicals may be causing or contributing to these adverse effects. Special attention must be taken to the units with moderate impact potential of occurrence of adverse ecological effects, located inside the natural reserve. Non-point source pollution coming from agriculture and aquaculture activities also seem to contribute with important pollution load into the estuary entering from Águas de Moura Channel. This pressure is expressed in a moderate impact potential for ecological risk existent in the areas near the entrance of this Channel. Pressures may also came from Alcácer Channel although they were not quantified in this study. The management framework presented here, including all the methodological tools may be applied and tested in other estuarine ecosystems, which will also allow a comparison between estuarine ecosystems in other parts of the globe.

Extraction automatique d´associations lexicales à partir de corpora

The automatic acquisition of lexical associations from corpora is a crucial issue for Natural Language Processing. A lexical association is a recurrent combination of words that co-occur together more often than expected by chance in a given domain. In fact, lexical associations define linguistic phenomena such as idiomes, collocations or compound words. Due to the fact that the sense of a lexical association is not compositionnal, their identification is fundamental for the realization of analysis and synthesis that take into account all the subtilities of the language. In this report, we introduce a new statistically-based architecture that extracts from naturally occurring texts contiguous and non contiguous. For that purpose, three new concepts have been defined : the positional N-gram models, the Mutual Expectation and the GenLocalMaxs algorithm. Thus, the initial text is fisrtly transformed in a set of positionnal N-grams i.e ordered vectors of simple lexical units. Then, an association measure, the Mutual Expectation, evaluates the degree of cohesion of each positional N-grams based on the identification of local maximum values of Mutual Expectation. Great efforts have also been carried out to evaluate our metodology. For that purpose, we have proposed the normalisation of five well-known association measures and shown that both the Mutual Expectation and the GenLocalMaxs algorithm evidence significant improvements comparing to existent metodologies.

Fractional discrete-time signal processing: scale conversion and linear prediction

Nonlinear Dynamics, Vol. 29

Fractional signal processing: scale conversion

In Proceedings of the “ECCTD '01 - European Conference on Circuit Theory and Design, Espoo, Finland, August 2001