16 research outputs found

    DECENTRALIZED RESOURCE ORCHESTRATION FOR HETEROGENEOUS GRIDS

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    Modern desktop machines now use multi-core CPUs to enable improved performance. However, achieving high performance on multi-core machines without optimized software support is still difficult even in a single machine, because contention for shared resources can make it hard to exploit multiple computing resources efficiently. Moreover, more diverse and heterogeneous hardware platforms (e.g. general-purpose GPU and Cell processors) have emerged and begun to impact grid computing. Given that heterogeneity and diversity are now a major trend going forward, grid computing must support these environmental changes. In this dissertation, I design and evaluate a decentralized resource management scheme to exploit heterogeneous multiple computing resources effectively. I suggest resource management algorithms that can efficiently utilize a diverse computational environment, including multiple symmetric computing entities and heterogeneous multi-computing entities, and achieve good load-balancing and high total system throughput. Moreover, I propose expressive resource description techniques to accommodate more heterogeneous environments, allowing incoming jobs with complex requirements to be matched to available resources. First, I develop decentralized resource management frameworks and job scheduling schemes to exploit multi-core nodes in peer-to-peer grids. I present two new load-balancing schemes that explicitly account for resource sharing and contention across multiple cores within a single machine, and propose a simple performance prediction model that can represent a continuum of resource sharing among cores of a CPU. Second, I provide scalable resource discovery and load balancing techniques to accommodate nodes with many types of computing elements, such as multi-core CPUs and GPUs, in a peer-to-peer grid architecture. My scheme takes into account diverse aspects of heterogeneous nodes to maximize overall system throughput as well as minimize messaging costs without sacrificing the failure resilience provided by an underlying peer-to-peer overlay network. Finally, I propose an expressive resource discovery method to support multi-attribute, range-based job constraints. The common approach of using simple attribute indexes does not suffice, as range-based constraints may be satisfied by more than a single value. I design a compact ID-based representation for resource characteristics, and integrate this representation into the decentralized resource discovery framework. By extensive experimental results via simulation, I show that my schemes can match heterogeneous jobs to heterogeneous resources both effectively (good matches are found, load is balanced), and efficiently (the new functionality imposes little overhead)

    Evaluating the graphics processing unit for digital audio synthesis and the development of HyperModels

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    The extraordinary growth in computation in single processors for almost half a century is becoming increasingly difficult to maintain. Future computational growth is expected from parallel processors, as seen in the increasing number of tightly coupled processors inside the conventional modern heterogeneous system. The graphics processing unit (GPU) is a massively parallel processing unit that can be used to accelerate particular digital audio processes; However, digital audio developers are cautious of adopting the GPU into their designs to avoid any complications the GPU architecture may have. For example, linear systems simulated using finite-difference-based physical model synthesis is highly suited for the GPU, but developers will be reluctant to use it without a complete evaluation of the GPU for digital audio. Previously limited by computation, the audio landscape could see future advancement by providing a comprehensive evaluation of the GPU in digital audio and developing a framework for accelerating particular audio processes. This thesis is separated into two parts; Part one evaluates the utility of the GPU as a hardware accelerator for digital audio processing using bespoke performance benchmarking suites. The results suggest that the GPU is appropriate under particular conditions; For example, the sample buffer size dispatched to the GPU must be within 32 to 512 to meet real-time digital audio requirements. However, despite some constraints, the GPU could support linear finite-difference-based physical models with 4X higher resolution than the equivalent CPU version. These results suggest that the GPU is superior to the CPU for high-resolution physical models. Therefore, the second part of this thesis presents the design of the novel HyperModels framework to facilitate the development of real-time linear physical models for interaction and performance. HyperModels uses vector graphics to describe a model's geometry and a domain-specific language (DSL) to define the physics equations that operate in the physical model. An implementation of the HyperModels framework is then objectively evaluated by comparing the performance with manually written CPU and GPU equivalent versions. The automatically generated GPU programs from HyperModels were shown to outperform the CPU versions for resolutions 64x64 and above whilst maintaining similar performance to the manually written GPU versions. To conclude part 2, the expressibility and usability of HyperModels is demonstrated by presenting two instruments built using the framewor

    Advances in Grid Computing

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    This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

    SPICA:revealing the hearts of galaxies and forming planetary systems : approach and US contributions

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    How did the diversity of galaxies we see in the modern Universe come to be? When and where did stars within them forge the heavy elements that give rise to the complex chemistry of life? How do planetary systems, the Universe's home for life, emerge from interstellar material? Answering these questions requires techniques that penetrate dust to reveal the detailed contents and processes in obscured regions. The ESA-JAXA Space Infrared Telescope for Cosmology and Astrophysics (SPICA) mission is designed for this, with a focus on sensitive spectroscopy in the 12 to 230 micron range. SPICA offers massive sensitivity improvements with its 2.5-meter primary mirror actively cooled to below 8 K. SPICA one of 3 candidates for the ESA's Cosmic Visions M5 mission, and JAXA has is committed to their portion of the collaboration. ESA will provide the silicon-carbide telescope, science instrument assembly, satellite integration and testing, and the spacecraft bus. JAXA will provide the passive and active cooling system (supporting the

    The Apertif Surveys:The First Six Months

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    Apertif is a new phased-array feed for the Westerbork Synthesis Radio Telescope (WSRT), greatly increasing its field of view and turning it into a natural survey instrument. In July 2019, the Apertif legacy surveys commenced; these are a time-domain survey and a two-tiered imaging survey, with a shallow and medium-deep component. The time-domain survey searches for new (millisecond) pulsars and fast radio bursts (FRBs). The imaging surveys provide neutral hydrogen (HI), radio continuum and polarization data products. With a bandwidth of 300 MHz, Apertif can detect HI out to a redshift of 0.26. The key science goals to be accomplished by Apertif include localization of FRBs (including real-time public alerts), the role of environment and interaction on galaxy properties and gas removal, finding the smallest galaxies, connecting cold gas to AGN, understanding the faint radio population, and studying magnetic fields in galaxies. After a proprietary period, survey data products will be publicly available through the Apertif Long Term Archive (ALTA, https://alta.astron.nl). I will review the progress of the surveys and present the first results from the Apertif surveys, including highlighting the currently available public data
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