A scalable parallel finite element framework for growing geometries. Application to metal additive manufacturing

This work introduces an innovative parallel, fully-distributed finite element framework for growing geometries and its application to metal additive manufacturing. It is well-known that virtual part design and qualification in additive manufacturing requires highly-accurate multiscale and multiphysics analyses. Only high performance computing tools are able to handle such complexity in time frames compatible with time-to-market. However, efficiency, without loss of accuracy, has rarely held the centre stage in the numerical community. Here, in contrast, the framework is designed to adequately exploit the resources of high-end distributed-memory machines. It is grounded on three building blocks: (1) Hierarchical adaptive mesh refinement with octree-based meshes; (2) a parallel strategy to model the growth of the geometry; (3) state-of-the-art parallel iterative linear solvers. Computational experiments consider the heat transfer analysis at the part scale of the printing process by powder-bed technologies. After verification against a 3D benchmark, a strong-scaling analysis assesses performance and identifies major sources of parallel overhead. A third numerical example examines the efficiency and robustness of (2) in a curved 3D shape. Unprecedented parallelism and scalability were achieved in this work. Hence, this framework contributes to take on higher complexity and/or accuracy, not only of part-scale simulations of metal or polymer additive manufacturing, but also in welding, sedimentation, atherosclerosis, or any other physical problem where the physical domain of interest grows in time

Dynamic Resource Management in Virtualized Data Centres

In the last decade, Cloud Computing has become a disruptive force in the computing landscape, changing the way in which software is designed, deployed and used over the world. Its adoption has been substantial and it is only expected to continue growing. The growth of this new model is supported by the proliferation of large-scale data centres, built for the express purpose of hosting cloud workloads. These data centres rely on systems virtualization to host multiple workloads per physical server, thus increasing their infrastructures\u27 utilization and decreasing their power consumption. However, the owners of the cloud workloads expect their applications\u27 demand to be satisfied at all times, and placing too many workloads in one physical server can risk meeting those service expectations. These and other management goals make the task of managing a cloud-supporting data centre a complex challenge, but one that needs to be addressed. In this work, we address a few of the management challenges associated with dynamic resource management in virtualized data centres. We investigate the application of First Fit heuristics to the Virtual Machine Relocation problem (that is, the problem of migrating VMs away from stressed or overloaded hosts) and the effect that different heuristics have, as reflected in the performance metrics of the data centre. We also investigate how to pursue multiple goals in data centre management and propose a method to achieve precisely that by dynamically switching management strategies at runtime according to data centre state. In order to improve system scalability and decrease network management overhead, we propose architecting the management system as a topology-aware hierarchy of managing elements, which limits the flow of management data across the data centre. Finally, we address the challenge of managing multi-VM applications with placement constraints in data centres, while still trying to achieve high levels of resource utilization and client satisfaction

Clustering and its Application in Requirements Engineering

Large scale software systems challenge almost every activity in the software development life-cycle, including tasks related to eliciting, analyzing, and specifying requirements. Fortunately many of these complexities can be addressed through clustering the requirements in order to create abstractions that are meaningful to human stakeholders. For example, the requirements elicitation process can be supported through dynamically clustering incoming stakeholders’ requests into themes. Cross-cutting concerns, which have a significant impact on the architectural design, can be identified through the use of fuzzy clustering techniques and metrics designed to detect when a theme cross-cuts the dominant decomposition of the system. Finally, traceability techniques, required in critical software projects by many regulatory bodies, can be automated and enhanced by the use of cluster-based information retrieval methods. Unfortunately, despite a significant body of work describing document clustering techniques, there is almost no prior work which directly addresses the challenges, constraints, and nuances of requirements clustering. As a result, the effectiveness of software engineering tools and processes that depend on requirements clustering is severely limited. This report directly addresses the problem of clustering requirements through surveying standard clustering techniques and discussing their application to the requirements clustering process

Comparison of input devices in an ISEE direct timbre manipulation task

The representation and manipulation of sound within multimedia systems is an important and currently under-researched area. The paper gives an overview of the authors' work on the direct manipulation of audio information, and describes a solution based upon the navigation of four-dimensional scaled timbre spaces. Three hardware input devices were experimentally evaluated for use in a timbre space navigation task: the Apple Standard Mouse, Gravis Advanced Mousestick II joystick (absolute and relative) and the Nintendo Power Glove. Results show that the usability of these devices significantly affected the efficacy of the system, and that conventional low-cost, low-dimensional devices provided better performance than the low-cost, multidimensional dataglove

Different types of sounds influence gaze differently in videos

This paper presents an analysis of the effect of different types of sounds on visual gaze when a person is looking freely at videos, which would be helpful to predict eye position. In order to test the effect of sound, an audio-visual experiment was designed with two groups of participants, with audio-visual (AV) and visual (V) conditions. By using statistical tools, we analyzed the difference between eye position of participants with AV and V conditions. We observed that the effect of sound is different depending on the kind of sound, and that the classes with human voice (i.e. speech, singer, human noise and singers) have the greatest effect. Furthermore, the results of the distance between sound source and eye position of the group with AV condition, suggested that only particular types of sound attract human eye position to the sound source. Finally, an analysis of the fixation duration between AV and V conditions showed that participants with AV condition move eyes more frequently than those with V condition

Advances in Dynamic Virtualized Cloud Management

Cloud computing continues to gain in popularity, with more and more applications being deployed into public and private clouds. Deploying an application in the cloud allows application owners to provision computing resources on-demand, and scale quickly to meet demand. An Infrastructure as a Service (IaaS) cloud provides low-level resources, in the form of virtual machines (VMs), to clients on a pay-per-use basis. The cloud provider (owner) can reduce costs by lowering power consumption. As a typical server can consume 50% or more of its peak power consumption when idle, this can be accomplished by consolidating client VMs onto as few hosts (servers) as possible. This, however, can lead to resource contention, and degraded VM performance. As such, VM placements must be dynamically adapted to meet changing workload demands. We refer to this process as dynamic management. Clients should also take advantage of the cloud environment by scaling their applications up and down (adding and removing VMs) to match current workload demands. This thesis proposes a number of contributions to the field of dynamic cloud management. First, we propose a method of dynamically switching between management strategies at run-time in order to achieve more than one management goal. In order to increase the scalability of dynamic management algorithms, we introduce a distributed version of our management algorithm. We then consider deploying applications which consist of multiple VMs, and automatically scale their deployment to match their workload. We present an integrated management algorithm which handles both dynamic management and application scaling. When dealing with multi-VM applications, the placement of communicating VMs within the data centre topology should be taken into account. To address this consideration, we propose a topology-aware version of our dynamic management algorithm. Finally, we describe a simulation tool, DCSim, which we have developed to help evaluate dynamic management algorithms and techniques