Parallel hyperspectral compressive sensing method on GPU

Remote hyperspectral sensors collect large amounts of data per flight usually with low spatial resolution. It is known that the bandwidth connection between the satellite/airborne platform and the ground station is reduced, thus a compression onboard method is desirable to reduce the amount of data to be transmitted. This paper presents a parallel implementation of an compressive sensing method, called parallel hyperspectral coded aperture (P-HYCA), for graphics processing units (GPU) using the compute unified device architecture (CUDA). This method takes into account two main properties of hyperspectral dataset, namely the high correlation existing among the spectral bands and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. Experimental results conducted using synthetic and real hyperspectral datasets on two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN, reveal that the use of GPUs can provide real-time compressive sensing performance. The achieved speedup is up to 20 times when compared with the processing time of HYCA running on one core of the Intel i7-2600 CPU (3.4GHz), with 16 Gbyte memory.

Parallel hyperspectral unmixing method via split augmented lagrangian on GPU

One of the main problems of hyperspectral data analysis is the presence of mixed pixels due to the low spatial resolution of such images. Linear spectral unmixing aims at inferring pure spectral signatures and their fractions at each pixel of the scene. The huge data volumes acquired by hyperspectral sensors put stringent requirements on processing and unmixing methods. This letter proposes an efficient implementation of the method called simplex identification via split augmented Lagrangian (SISAL) which exploits the graphics processing unit (GPU) architecture at low level using Compute Unified Device Architecture. SISAL aims to identify the endmembers of a scene, i.e., is able to unmix hyperspectral data sets in which the pure pixel assumption is violated. The proposed implementation is performed in a pixel-by-pixel fashion using coalesced accesses to memory and exploiting shared memory to store temporary data. Furthermore, the kernels have been optimized to minimize the threads divergence, therefore achieving high GPU occupancy. The experimental results obtained for the simulated and real hyperspectral data sets reveal speedups up to 49 times, which demonstrates that the GPU implementation can significantly accelerate the method's execution over big data sets while maintaining the methods accuracy.

Parallel method for sparse semisupervised hyperspectral unmixing

Parallel hyperspectral unmixing problem is considered in this paper. A semisupervised approach is developed under the linear mixture model, where the abundance's physical constraints are taken into account. The proposed approach relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. Since Libraries are potentially very large and hyperspectral datasets are of high dimensionality a parallel implementation in a pixel-by-pixel fashion is derived to properly exploits the graphics processing units (GPU) architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Parallel hyperspectral unmixing method on GPU

Many Hyperspectral imagery applications require a response in real time or near-real time. To meet this requirement this paper proposes a parallel unmixing method developed for graphics processing units (GPU). This method is based on the vertex component analysis (VCA), which is a geometrical based method highly parallelizable. VCA is a very fast and accurate method that extracts endmember signatures from large hyperspectral datasets without the use of any a priori knowledge about the constituent spectra. Experimental results obtained for simulated and real hyperspectral datasets reveal considerable acceleration factors, up to 24 times.

Parallel sparse unmixing of hyperspectral data

In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 1 64 times, with regards to optimized serial implementation.

Multi-objective Parallel Particle Swarm Optimization for Day-ahead Vehicle-To-Grid Scheduling

This paper presents a methodology for multi-objective day-ahead energy resource scheduling for smart grids considering intensive use of distributed generation and Vehicle- To-Grid (V2G). The main focus is the application of weighted Pareto to a multi-objective parallel particle swarm approach aiming to solve the dual-objective V2G scheduling: minimizing total operation costs and maximizing V2G income. A realistic mathematical formulation, considering the network constraints and V2G charging and discharging efficiencies is presented and parallel computing is applied to the Pareto weights. AC power flow calculation is included in the metaheuristics approach to allow taking into account the network constraints. A case study with a 33-bus distribution network and 1800 V2G resources is used to illustrate the performance of the proposed method.

Algorithms for time series clustering applied to biomedical signals

Thesis submitted in the fulfillment of the requirements for the Degree of Master in Biomedical Engineering

Tolerância a falhas em sistemas de tempo-real distribuídos e embebidos

Este documento descreve um modelo de tolerância a falhas para sistemas de tempo-real distribuídos. A sugestão deste modelo tem como propósito a apresentação de uma solu-ção fiável, flexível e adaptável às necessidades dos sistemas de tempo-real distribuídos. A tolerância a falhas é um aspeto extremamente importante na construção de sistemas de tempo-real e a sua aplicação traz inúmeros benefícios. Um design orientado para a to-lerância a falhas contribui para um melhor desempenho do sistema através do melhora-mento de aspetos chave como a segurança, a confiabilidade e a disponibilidade dos sis-temas. O trabalho desenvolvido centra-se na prevenção, deteção e tolerância a falhas de tipo ló-gicas (software) e físicas (hardware) e assenta numa arquitetura maioritariamente basea-da no tempo, conjugada com técnicas de redundância. O modelo preocupa-se com a efi-ciência e os custos de execução. Para isso utilizam-se também técnicas tradicionais de to-lerância a falhas, como a redundância e a migração, no sentido de não prejudicar o tempo de execução do serviço, ou seja, diminuindo o tempo de recuperação das réplicas, em ca-so de ocorrência de falhas. Neste trabalho são propostas heurísticas de baixa complexida-de para tempo-de-execução, a fim de se determinar para onde replicar os componentes que constituem o software de tempo-real e de negociá-los num mecanismo de coordena-ção por licitações. Este trabalho adapta e estende alguns algoritmos que fornecem solu-ções ainda que interrompidos. Estes algoritmos são referidos em trabalhos de investiga-ção relacionados, e são utilizados para formação de coligações entre nós coadjuvantes. O modelo proposto colmata as falhas através de técnicas de replicação ativa, tanto virtual como física, com blocos de execução concorrentes. Tenta-se melhorar ou manter a sua qualidade produzida, praticamente sem introduzir overhead de informação significativo no sistema. O modelo certifica-se que as máquinas escolhidas, para as quais os agentes migrarão, melhoram iterativamente os níveis de qualidade de serviço fornecida aos com-ponentes, em função das disponibilidades das respetivas máquinas. Caso a nova configu-ração de qualidade seja rentável para a qualidade geral do serviço, é feito um esforço no sentido de receber novos componentes em detrimento da qualidade dos já hospedados localmente. Os nós que cooperam na coligação maximizam o número de execuções para-lelas entre componentes paralelos que compõem o serviço, com o intuito de reduzir atra-sos de execução. O desenvolvimento desta tese conduziu ao modelo proposto e aos resultados apresenta-dos e foi genuinamente suportado por levantamentos bibliográficos de trabalhos de in-vestigação e desenvolvimento, literaturas e preliminares matemáticos. O trabalho tem também como base uma lista de referências bibliográficas.

Acceleration of physics simulation engine through OpenCL

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Methodologies for the WCET Analysis of Parallel Applications on Many-core Architectures

Euromicro Conference on Digital System Design (DSD 2015), Funchal, Portugal.

Online Admission of Parallel Real-Time Tasks

6th Real-Time Scheduling Open Problems Seminar (RTSOPS 2015), Lund, Sweden.

A Multi-DAG Model for Real-Time Parallel Applications with Conditional Execution

The 30th ACM/SIGAPP Symposium On Applied Computing (SAC 2015). 13 to 17, Apr, 2015, Embedded Systems. Salamanca, Spain.

Allocation of Parallel Real-Time Tasks in Distributed Multi-core Architectures supported by an FTT-SE Network

Distributed real-time systems such as automotive applications are becoming larger and more complex, thus, requiring the use of more powerful hardware and software architectures. Furthermore, those distributed applications commonly have stringent real-time constraints. This implies that such applications would gain in flexibility if they were parallelized and distributed over the system. In this paper, we consider the problem of allocating fixed-priority fork-join Parallel/Distributed real-time tasks onto distributed multi-core nodes connected through a Flexible Time Triggered Switched Ethernet network. We analyze the system requirements and present a set of formulations based on a constraint programming approach. Constraint programming allows us to express the relations between variables in the form of constraints. Our approach is guaranteed to find a feasible solution, if one exists, in contrast to other approaches based on heuristics. Furthermore, approaches based on constraint programming have shown to obtain solutions for these type of formulations in reasonable time.

Generalized Extraction of Real-Time Parameters for Homogeneous Synchronous Dataflow Graphs

23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2015). 4 to 6, Mar, 2015. Turku, Finland.