Distributed Computer Architectures Research Articles

Evaluating clustering methods within the Artificial Ecosystem Algorithm and their application to bike redistribution in London

This paper proposes and evaluates a solution to the truck redistribution problem prominent in London's Santander Cycle scheme. Due to the complexity of this NP-hard combinatorial optimisation problem, no efficient optimisation techniques are known to solve the problem exactly. This motivates our use of the heuristic Artificial Ecosystem Algorithm (AEA) to find good solutions in a reasonable amount of time. The AEA is designed to take advantage of highly distributed computer architectures and adapt to changing problems. In the AEA a problem is first decomposed into its relative sub-components; they then evolve solution building blocks that fit together to form a single optimal solution. Three variants of the AEA centred on evaluating clustering methods are presented: the baseline AEA, the community-based AEA which groups stations according to journey flows, and the Adaptive AEA which actively modifies clusters to cater for changes in demand. We applied these AEA variants to the redistribution problem prominent in bike share schemes (BSS). The AEA variants are empirically evaluated using historical data from Santander Cycles to validate the proposed approach and prove its potential effectiveness.

Dataflow Computing and its Impact on Automation Applications

Developing efficient, scalable and reconfigurable supervisory control and data acquisition (SCADA) systems presents a unique set of challenges inherent in the development of distributed computer architectures, algorithms, protocols and procedures for large-scale networked industrial automation applications. A research project on dataflow automation system architecture is presented exploring the research and engineering issues related to the development of computer architecture for the next generation distributed supervision and control systems that exploits and leverages dataflow computation model. Major focus of research is on new object-attribute architecture of dataflow computing environment which is particularly suitable for the design and implementation of reconfigurable scalable heterogeneous distributed applications in industrial automation area. The state-of- the-art in the application of the dataflow model of computation is accompanied with description of software tools developed for programming, simulation, debugging and test of automation applications that follow the object-attribute dataflow model.

Review of parallel and distributed architectures for micromagnetic codes

Purpose – Rapid advancements in computer technologies over the past decade have recorded significant growth in the area of computational micromagnetics. As a result, current micromagnetic codes exploit the scalability offered by parallel and distributed computer architectures to deliver maximum performance. The purpose of this paper is to present a review, which explores various aspects of this relationship. Design/methodology/approach – The authors arrange the theme for this paper around the micromagnetic code development process. The review involves a discussion of the micromagnetic model, some new parallel architectures, and computational aspects based on different numerical methods. Findings – As current micromagnetic code is not readily portable to different architectures, most of the development effort goes towards this area, with a focus on writing/rewriting code for streaming hardware (particularly graphic cards). Originality/value – The paper identifies key challenges and avenues for further research on computational aspects of micromagnetic modelling and suggests recommendations based on the review.

A Parallel EM Algorithm for Model-Based Clustering Applied to the Exploration of Large Spatio-Temporal Data

We develop a parallel expectation–maximization (EM) algorithm for multivariate Gaussian mixture models and use it to perform model-based clustering of a large climate dataset. Three variants of the EM algorithm are reformulated in parallel and a new variant that is faster is presented. All are implemented using the single program, multiple data programming model, which is able to take advantage of the combined collective memory of large distributed computer architectures to process larger datasets. Displays of the estimated mixture model rather than the data allow us to explore multivariate relationships in a way that scales to arbitrary size data. We study the performance of our methodology on simulated data and apply our methodology to a high-resolution climate dataset produced by the community atmosphere model (CAM5). This article has supplementary material online.

Playable Serious Games for Studying and Programming Computational Stem and Informatics Applications of Distributed and Parallel Computer Architectures

Carefully engineered playable games can serve as vehicles for students and practitioners to learn and explore the programming of advanced computer architectures to execute applications, such as high performance computing (HPC) and complex, inter-networked, distributed systems. The article presents families of playable games that are grounded in the concurrent, parallel, and distributed manipulations and processing of sequences. Some examples of such manipulations are games that involve pattern matching, recognition, alignment, transformation, and evolution of sequences.

Parallel medical image reconstruction: from graphics processing units (GPU) to Grids

We present and compare a variety of parallelization approaches for a real-world case study on modern parallel and distributed computer architectures. Our case study is a production-quality, time-intensive algorithm for medical image reconstruction used in computer tomography (PET). We parallelize this algorithm for the main kinds of contemporary parallel architectures: shared-memory multiprocessors, distributed-memory clusters, graphics processing units (GPU) using the CUDA framework, the Cell processor and, finally, how various architectures can be accessed in a distributed Grid environment. The main contribution of the paper, besides the parallelization approaches, is their systematic comparison regarding four important criteria: performance, programming comfort, accessibility, and cost-effectiveness. We report results of experiments on particular parallel machines of different architectures that confirm the findings of our systematic comparison.

A new system architecture for crowd simulation

Crowd simulation requires both rendering visually plausible images and managing the behavior of autonomous agents. Therefore, these applications need an efficient design that allows them to simultaneously handle these two requirements. Although several proposals have focused on the software architectures for these systems, no proposals have focused on the computer systems supporting them. In this paper, we analyze the computer architectures used in the literature to support distributed virtual environments. Also, we propose a distributed computer architecture which is efficient enough to support simulations of thousand of autonomous agents. This proposal consists of a cluster of interconnected computers in order to improve flexibility and robustness, as well as a hierarchical software architecture that efficiently provides consistency. Performance evaluation results show that the trade-off between flexibility and consistency allows to efficiently manage thousands of autonomous agents. Therefore, this network-based system architecture can provide the required scalability for large-scale crowd simulations.

A survey of peer-to-peer content distribution technologies

Distributed computer architectures labeled "peer-to-peer" are designed for the sharing of computer resources (content, storage, CPU cycles) by direct exchange, rather than requiring the intermediation or support of a centralized server or authority. Peer-to-peer architectures are characterized by their ability to adapt to failures and accommodate transient populations of nodes while maintaining acceptable connectivity and performance.Content distribution is an important peer-to-peer application on the Internet that has received considerable research attention. Content distribution applications typically allow personal computers to function in a coordinated manner as a distributed storage medium by contributing, searching, and obtaining digital content.In this survey, we propose a framework for analyzing peer-to-peer content distribution technologies. Our approach focuses on nonfunctional characteristics such as security, scalability, performance, fairness, and resource management potential, and examines the way in which these characteristics are reflected in---and affected by---the architectural design decisions adopted by current peer-to-peer systems.We study current peer-to-peer systems and infrastructure technologies in terms of their distributed object location and routing mechanisms, their approach to content replication, caching and migration, their support for encryption, access control, authentication and identity, anonymity, deniability, accountability and reputation, and their use of resource trading and management schemes.

Virtual reality neurosurgery: a simulator blueprint.

This article details preliminary studies undertaken to integrate the most relevant advancements across multiple disciplines in an effort to construct a highly realistic neurosurgical simulator based on a distributed computer architecture. Techniques based on modified computational modeling paradigms incorporating finite element analysis are presented, as are current and projected efforts directed toward the implementation of a novel bidirectional haptic device. Patient-specific data derived from noninvasive magnetic resonance imaging sequences are used to construct a computational model of the surgical region of interest. Magnetic resonance images of the brain may be coregistered with those obtained from magnetic resonance angiography, magnetic resonance venography, and diffusion tensor imaging to formulate models of varying anatomic complexity. The majority of the computational burden is encountered in the presimulation reduction of the computational model and allows realization of the required threshold rates for the accurate and realistic representation of real-time visual animations. Intracranial neurosurgical procedures offer an ideal testing site for the development of a totally immersive virtual reality surgical simulator when compared with the simulations required in other surgical subspecialties. The material properties of the brain as well as the typically small volumes of tissue exposed in the surgical field, coupled with techniques and strategies to minimize computational demands, provide unique opportunities for the development of such a simulator. Incorporation of real-time haptic and visual feedback is approached here and likely will be accomplished soon.

Climate modeling

This special issue on climate modeling presents a snapshot of some of the researchscientists are conducting to improve our climate modeling capabilities. We present articles on promising atmospheric and ocean models, meshing issues for geophysicalapplications, implementation on distributed computer architectures, atmospheric-chemistry models, and the testing and evaluation of models.These articles, taken together, provide a good indication of the problems facing climate modelers today andsome of the solutions they are pursuing.

Distributed computing environments: Effects on software maintenance difficulty

The computing world is undergoing a significant transformation from centralized computer architectures to non-centralized or distributed computer architectures. Historically, the largest single life cycle computer system cost has been for maintaining information system software. This paper examines a new area of research in software maintenance (and development in general), focusing on the issue of whether and to what extent distributed computer operating environments directly affect software maintenance. The issue depends on two diametrics of information system architectures: component simplicity and system complexity. The smaller (but more numerous) the system components, the easier they are to deal with individually (i.e., lower software maintenance costs) but the more difficult it is to deal with the overall system (i.e., higher software maintenance costs). This paper proposes a new conceptual model for information systems complexity based on component number and variety, interaction number and variety, and the overall rate of change. By applying this complexity model to distributed computing environments, this research sought empirical quantitative and qualitative data from IS system and application software analysts, designers, programmers, testers, and customer service representatives to determine whether the complexity of a distributed computer system has a greater effect on software maintenance than component simplicity and what the related explanatory factors are. Results indicate that the overall complexity of the studied distributed systems overwhelmed the ease-of-use and simplicity of their components — thus increasing the overall difficulty of software maintenance. Some distributed computing factors had a much greater effect than others. Finally, this paper offers some management guidance on lowering the cost of distributed computing environment software maintenance costs by adjusting the level of system distribution and by minimizing component and system difficulties.

Relative end-effector control using Cartesian position based visual servoing

This paper presents a complete design methodology for Cartesian position based visual servo control for robots with a single camera mounted at the end-effector. Position based visual servo control requires the explicit calculation of the relative position and orientation (POSE) of the workpiece object with respect to the camera. This is accomplished using image plane measurements of a number of known feature points on the object, and then applying an extended Kalman filter to obtain a recursive solution of the photogrammetric equations, and to properly combine redundant measurements. The control is then designed by specifying the desired trajectories with respect to the object and forming the control error in the end-effector frame. The implementation using a distributed computer architecture is described. An experimental system has been built and used to evaluate the performance of the POSE estimation and the position based visual servo control. Several results for relative trajectory control and target tracking are presented. Results of the experiments showing the effect of loss of some of the redundant features are also presented.

Robust Optimization of Large-Scale Systems

Mathematical programming models with noisy, erroneous, or incomplete data are common in operations research applications. Difficulties with such data are typically dealt with reactively—through sensitivity analysis—or proactively—through stochastic programming formulations. In this paper, we characterize the desirable properties of a solution to models, when the problem data are described by a set of scenarios for their value, instead of using point estimates. A solution to an optimization model is defined as: solution robust if it remains “close” to optimal for all scenarios of the input data, and model robust if it remains “almost” feasible for all data scenarios. We then develop a general model formulation, called robust optimization (RO), that explicitly incorporates the conflicting objectives of solution and model robustness. Robust optimization is compared with the traditional approaches of sensitivity analysis and stochastic linear programming. The classical diet problem illustrates the issues. Robust optimization models are then developed for several real-world applications: power capacity expansion; matrix balancing and image reconstruction; air-force airline scheduling; scenario immunization for financial planning; and minimum weight structural design. We also comment on the suitability of parallel and distributed computer architectures for the solution of robust optimization models.

SIMON: A distributed computer architecture for intelligent patient monitoring

Abstract Intelligent real-time patient monitoring encompasses data acquisition and reduction, sensor validation, diagnosis, therapy advice, and selective display of information. This paper describes the architecture and the functionality of a prototype intelligent patient monitoring system, named SIMON, designed to meet these requirements. In SIMON, the various aspects of a monitoring task are performed by three semi-independent modules running asynchronously: the feature extraction, the patient model, and the display modules. Central to SIMON is the notion of context sensitivity which permits (a) the adaptation of the monitoring strategy in response to changes either in the patient state or in the monitoring equipment and (b) the contextual interpretation of incoming data. SIMON is currently applied to the task of monitoring newborn infants with respiratory distress syndrome (RDS) and undergoing assisted ventilation.

Integrating information from decentralized laboratory testing sites. The creation of a value-added network.

The decentralization of laboratory testing provides distinct advantages for clinicians and patients, such as the reduction of test turnaround time, but also promotes the development of scattered caches of results. This problem could be ameliorated by the creation of an integrated laboratory data base. Such an approach would provide clinicians with a patient view of laboratory information in addition to the functional view of it that is offered currently. Impressive technologic advances are being made in the development of a distributed computer architecture in which processing tasks and information are shared across multiple hardware platforms attached to a network. Such architecture could be used to create a regional value-added laboratory network to integrate test information generated in decentralized testing sites. Independent reference laboratories and tertiary-care referral hospitals are the most likely candidates to create distributed value-added networks. Pathologists should view themselves as health-care professionals responsible for the processing, storage, and transmission of information, as well as for its generation. In time, the partition of information along hospital geopolitical boundaries will appear archaic and will be replaced by an emphasis on local and regional integration of medical information similar to that advocated here.

Innovations in Training Simulation

With the Navy leaning towards a smaller but better trained force, using simulators in training becomes a cost effective way of keeping our military edge. Several innovations are being utilized in today's simulators. Hardware and software designs now include distributed computer architectures, real-time, multi-tasking operating systems, flexible generic consoles, commercial off-the-shelf equipment, standard real-time ocean acoustic models, and standard acoustic data bases. This paper describes these technologies and their innovative application in the Navy's newest Anti-Submarine Warfare Team Trainer, Device 14A12. Device 14A12 simulates sonar, fire control, electronic warfare, bridge, ownship aircraft and Naval Tactical Data Systems (NTDS) for 16 surface ownship classes. Device 14A12 also simulates multiple air, surface and subsurface threat, neutral and friendly platforms as well as missiles and torpedoes. The innovative modeling of these systems along with Device 14A12's enormous flexibility will allow the Navy to provide effective tactical training for the surface fleet now and in the future.

A Reliable Multicast Protocol for Distributed Real-Time Systems

Distributed computer architectures are well accepted in the domain of real-time ap-plications. To realize fault-tolerance, fail-silent node computers providing the same service can be clustered into Fault-Tolerant Units (FTUs). Each FTU provides a specified service as long as at least one of its node computers is operational. The communication between these FTUs has to be deterministic, reliable and timely, i.e. there must be a tight upper bound on the time it takes to send a message from one FTU to the other FTUs. This paper presents a communication system suitable for real-time applications that meets these requirements.

Role of Automation and Robotics in Semiconductor Industry

This paper presents the current trends in automation and robotics in the semiconductor industry. The semiconductor manufacturer, in order to survive in this industry, has to maintain a state-of-the-art facility and for that he has to invest in automation. The reasons for automation are myriad; principal among them are yield and throughput enhancement, improved process control, faster turnaround time and development of a sophisticated test bed for research activities. The paper dwells extensively on reasons, benefits and objectives of automation.Automation systems comprise of a distributed computer architecture; mechanization systems; process control systems and logistics systems. Implementation of automation systems should be carried out in a phased manner so as to achieve bottoms up integration. Both system buildup and implementation strategies are discussed in detail.In the Robotics section development of robots for clean room application is discussed. Starting from repeatable jobs robots have acquired ...

Distributed architecture of an adaptive positioning servo

A distributed computer architecture which offers high precision and high-speed computing capabilities that are needed when complex system identification and controller adaptation algorithms are implemented in real time. The distributed implementation offers high immunity against external disturbances. An elevator control application is considered. However, the principles presented are applicable to different types of industrial automation problems. The individual computer units in this system are programmable VLSI signal processors (e.g. TMS-320C25). All the precision-sensitive computations are performed using simulated single-precision floating-point arithmetic (IEEE Standard 754). This is possible because of the high speed of the signal processor and low sampling rate (i.e. >

Specifying distributed computer architectures in AADL

This paper presents a specification-methodology for distributed computer architectures based on the axiomatic architecture description language AADL. AADL allows for a modular and concise specification of multiprocessor architectures at levels of abstraction ranging from compiler/operating-system interface down to chip-level. The specification method is illustrated by several examples taken from an AADL-definition of an abstract view of DOOM, a distributed object oriented machine, currently developed at Philips Research Laboratories, Eindhoven within ESPRIT-project 415.