Parallel GPU architecture for hyperspectral unmixing based on augmented Lagrangian method

Hyperspectral imaging has become one of the main topics in remote sensing applications, which comprise hundreds of spectral bands at different (almost contiguous) wavelength channels over the same area generating large data volumes comprising several GBs per flight. This high spectral resolution can be used for object detection and for discriminate between different objects based on their spectral characteristics. One of the main problems involved in hyperspectral analysis is the presence of mixed pixels, which arise when the spacial resolution of the sensor is not able to separate spectrally distinct materials. Spectral unmixing is one of the most important task for hyperspectral data exploitation. However, the unmixing algorithms can be computationally very expensive, and even high power consuming, which compromises the use in applications under on-board constraints. In recent years, graphics processing units (GPUs) have evolved into highly parallel and programmable systems. Specifically, several hyperspectral imaging algorithms have shown to be able to benefit from this hardware taking advantage of the extremely high floating-point processing performance, compact size, huge memory bandwidth, and relatively low cost of these units, which make them appealing for onboard data processing. In this paper, we propose a parallel implementation of an augmented Lagragian based method for unsupervised hyperspectral linear unmixing on GPUs using CUDA. The method called simplex identification via split augmented Lagrangian (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 efficient implementation of SISAL method presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory.

P-SOCRATES: A parallel software framework for time-critical many-core systems

Article in Press, Corrected Proof

Parallel Software framework for time-critical many-core systems

The recent technological advancements and market trends are causing an interesting phenomenon towards the convergence of High-Performance Computing (HPC) and Embedded Computing (EC) domains. On one side, new kinds of HPC applications are being required by markets needing huge amounts of information to be processed within a bounded amount of time. On the other side, EC systems are increasingly concerned with providing higher performance in real-time, challenging the performance capabilities of current architectures. The advent of next-generation many-core embedded platforms has the chance of intercepting this converging need for predictable high-performance, allowing HPC and EC applications to be executed on efficient and powerful heterogeneous architectures integrating general-purpose processors with many-core computing fabrics. To this end, it is of paramount importance to develop new techniques for exploiting the massively parallel computation capabilities of such platforms in a predictable way. P-SOCRATES will tackle this important challenge by merging leading research groups from the HPC and EC communities. The time-criticality and parallelisation challenges common to both areas will be addressed by proposing an integrated framework for executing workload-intensive applications with real-time requirements on top of next-generation commercial-off-the-shelf (COTS) platforms based on many-core accelerated architectures. The project will investigate new HPC techniques that fulfil real-time requirements. The main sources of indeterminism will be identified, proposing efficient mapping and scheduling algorithms, along with the associated timing and schedulability analysis, to guarantee the real-time and performance requirements of the applications.

Multiresponsive supramolecular systems for information processing at the molecular level

Dissertação para obtenção do Grau de Doutor em Química Sustentável

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

Dissertação para obtenção do Grau de Mestre em Engenharia Informática

Gridifying IBM's Generic Log Adapter to Speed-Up the Processing of Log Data

Peer-reviewed

Agent-based management systems for many-core platforms : rigorous design and efficient implementation

Due to various advantages such as flexibility, scalability and updatability, software intensive systems are increasingly embedded in everyday life. The constantly growing number of functions executed by these systems requires a high level of performance from the underlying platform. The main approach to incrementing performance has been the increase of operating frequency of a chip. However, this has led to the problem of power dissipation, which has shifted the focus of research to parallel and distributed computing. Parallel many-core platforms can provide the required level of computational power along with low power consumption. On the one hand, this enables parallel execution of highly intensive applications. With their computational power, these platforms are likely to be used in various application domains: from home use electronics (e.g., video processing) to complex critical control systems. On the other hand, the utilization of the resources has to be efficient in terms of performance and power consumption. However, the high level of on-chip integration results in the increase of the probability of various faults and creation of hotspots leading to thermal problems. Additionally, radiation, which is frequent in space but becomes an issue also at the ground level, can cause transient faults. This can eventually induce a faulty execution of applications. Therefore, it is crucial to develop methods that enable efficient as well as resilient execution of applications. The main objective of the thesis is to propose an approach to design agentbased systems for many-core platforms in a rigorous manner. When designing such a system, we explore and integrate various dynamic reconfiguration mechanisms into agents functionality. The use of these mechanisms enhances resilience of the underlying platform whilst maintaining performance at an acceptable level. The design of the system proceeds according to a formal refinement approach which allows us to ensure correct behaviour of the system with respect to postulated properties. To enable analysis of the proposed system in terms of area overhead as well as performance, we explore an approach, where the developed rigorous models are transformed into a high-level implementation language. Specifically, we investigate methods for deriving fault-free implementations from these models into, e.g., a hardware description language, namely VHDL.

On-line analytical processing with conceptual information systems

A conceptual information system consists of a database together with conceptual hierarchies. The management system TOSCANA visualizes arbitrary combinations of conceptual hierarchies by nested line diagrams and allows an on-line interaction with a database to analyze data conceptually. The paper describes the conception of conceptual information systems and discusses the use of their visualization techniques for on-line analytical processing (OLAP).

Are distortion levels a problem with digital signal processing hearing aids and frequency modulation (FM) systems?

The purpose of this study was to examine objective and subjective distortion present when frequency modulation (FM) systems were coupled with four digital signal processing (DSP) hearing aids. Electroacoustic analysis and subjective listening tests by experienced audiologists revealed that distortion levels varied across hearing aids and channels.

Linear predicted two-pass hexagonal algorithm with parallel implementation for motion estimation

This paper presents a paralleled Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA., Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). We introduced hashtable into video processing and completed parallel implementation. We propose and evaluate parallel implementations of the LHMEA of TPA on clusters of workstations for real time video compression. It discusses how parallel video coding on load balanced multiprocessor systems can help, especially on motion estimation. The effect of load balancing for improved performance is discussed. The performance or the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

Two-pass hexagonal algorithm with parallel implementation for video coding

This paper presents a paralleled Two-Pass Hexagonal (TPA) algorithm constituted by Linear Hashtable Motion Estimation Algorithm (LHMEA) and Hexagonal Search (HEXBS) for motion estimation. In the TPA, Motion Vectors (MV) are generated from the first-pass LHMEA and are used as predictors for second-pass HEXBS motion estimation, which only searches a small number of Macroblocks (MBs). We introduced hashtable into video processing and completed parallel implementation. We propose and evaluate parallel implementations of the LHMEA of TPA on clusters of workstations for real time video compression. It discusses how parallel video coding on load balanced multiprocessor systems can help, especially on motion estimation. The effect of load balancing for improved performance is discussed. The performance of the algorithm is evaluated by using standard video sequences and the results are compared to current algorithms.

Hardware development of the Yorick series of active vision systems

This paper presents a review of the design and development of the Yorick series of active stereo camera platforms and their integration into real-time closed loop active vision systems, whose applications span surveillance, navigation of autonomously guided vehicles (AGVs), and inspection tasks for teleoperation, including immersive visual telepresence. The mechatronic approach adopted for the design of the first system, including head/eye platform, local controller, vision engine, gaze controller and system integration, proved to be very successful. The design team comprised researchers with experience in parallel computing, robot control, mechanical design and machine vision. The success of the project has generated sufficient interest to sanction a number of revisions of the original head design, including the design of a lightweight compact head for use on a robot arm, and the further development of a robot head to look specifically at increasing visual resolution for visual telepresence. The controller and vision processing engines have also been upgraded, to include the control of robot heads on mobile platforms and control of vergence through tracking of an operator's eye movement. This paper details the hardware development of the different active vision/telepresence systems.