1,721,058 research outputs found
Marine GIS development: mapping the Bay of Naples
No full textThe primary goal of our GIS design for marine applications is to facilitate the surveyor in environmental data acquisition and in retrieving, preprocessing and visualizing georeferenced information from oceanographic cruises. The system called OSIRIS (ocean survey integrated research information system) has a modular architecture tailored to support environmental data acquisition. It provides a framework where updating and expanding each module is straightforward. An on-going program of seafloor mapping in Naples Bay (southeastern Tyrrhenian margin) is among the main research projects of the Geomare sud Institute, CNR. Financed by the National Geological Survey of Italy (CARG project), this research aims at producing, during the years 1998-2000, geological maps of selected marine coastal zones at the 1: 50.000 scale. OSIRIS has been used and tested in this frame and in particular during two oceanographic cruises organized by the Geomare Sud and by other institutions during the 1997-1998 time span
FACE-IT: A Science Gateway for Food Security Research
No full textUnderstanding the potential impacts of climate change and the likely effectiveness of adaptation strategies is of crucial importance to the sustainability of both agriculture and natural ecosystems. Improvements in data availability and simulation model fidelity promises to enable significant improvements in knowledge. However, progress is hindered by the challenges inherent in creating and managing increasingly complex data acquisition, processing, simulation, post-processing, and intercomparison pipelines. To address these challenges, we are developing the Framework to Advance Climate, Economic, and Impact Investigations with Information Technology (FACE-IT) for crop and climate impact assessments. This integrated geospatial data processing, delivery, and simulation framework enables data ingest from diverse geospatial data archives; data regridding, aggregation, and other relevant processing required prior to simulation; large-scale climate impact simulation using a range of applications, including different agricultural models, and leveraging high-performance and cloud computing; and post-processing to produce aggregated yields and other output variables needed to enable model intercomparison and to connect biophysical model outputs to global and regional economic models and assessments. It leverages the capabilities of the Globus Galaxies platform to enable the capture of both workows and simulation outputs in well-defined, reusable, and easily comparable forms. We describe FACE-IT and its application to studies within the Agricultural Model Intercomparison and Improvement Project
Development of a GT4-based Resource Broker Service: an application to on-demand weather and marine forecasting
No full textThe discovery and selection of needed resources, taking into account optimization criteria, local policies, computing and storage availability, resource reservations, and grid dynamics, is a technological challenge in the emerging technology of grid computing.
The Condor Project’s ClassAd language is commonly adopted as a “lingua franca” for describing grid resources, but Condor itself does not make extensive use of Web Services. In contrast, the strongly service-oriented Globus Toolkit is implemented using the web services resource framework, and offers basic services for job submission, data replica and location, reliable file transfers and resource indexing, but does not provide a resource broker and matchmaking service.
In this paper we describe the development of a Resource Broker Service based on the Web Services technology offered by the Globus Toolkit version 4 (GT4). We implement a fully configurable and customizable matchmaking algorithm within a framework that allows users to direct complex queries to the GT4 index service and thus discover any published resource. The matchmaking algorithm supports both the native simple query form and the Condor ClassAd notation. We achieve this flexibility via a matchmaking API java class framework implemented on the extensible GT4 index service, which maps queries over ClassAds in a customizable fashion.
We show an example of the proposed grid application, namely an on demand weather and marine forecasting system. This system implements a Job Flow Scheduler and a Job Flow Description Language in order to access and exploit shared and distributed observations, model software, and 2D/3D graphical rendering resources. The system combines GT4 components and our Job Flow Scheduler and Resource Broker services to provide a fully grid-aware system
A Grid Computing Based Virtual Laboratory for Environmental Simulations
No full textThe effective and efficient computing resource allocation, leveraging on a flexible, but straightforward to deploy, configure and use matchmaking tool, is a key component in the grand challenge of realistic, high resolution and computing time affordable environmental simulations and forecasts. With the aim of building a computing environmental science virtual laboratory, we developed a suite of Globus Toolkit 4 based web services to implement an interoperable service oriented architecture integrating matchmaking and resource broking, workflow management, data and metadata advertisement and instruments integration
SIaaS-Sensing Instrument as a Service Using Cloud Computing to Turn Physical Instrument into Ubiquitous Service
No full textSensing Instrument as a Service (SIaaS) is a new cloud service that allows users to use data acquisition instruments shared through cloud infrastructure. It offers a common interface to managing physical sensing instrument and it permits to take all advantages of using cloud computing technology in storing and processing acquired data. With SIaaS different research groups could share physical instrument, in a controlled way, event if they are geographically distributed and user can access to them using an internet connected device without the need to install any sort of program. With the interaction of different own framework we have created a new cloud service for transforming physical resources in a ubiquitous service
A Scalable Unified Model for Dynamic Data Structures in Message Passing (Clusters) and Shared Memory (multicore CPUs) Computing environments
No full textConcurrent data structures are widely used in many software stack levels, ranging from high level parallel scientific applications to low level operating systems. The key issue of these objects is their concurrent use by several computing units (threads or process) so that the design of these structures is much more difficult compared to their sequential counterpart, because of their extremely dynamic nature requiring protocols to ensure data consistency, with a significant cost overhead. At this regard, several studies emphasize a tension between the needs of sequential correctness of the concurrent data structures and scalability of the algorithms, and in many cases it is evident the need to rethink the data structure design, using approaches based on randomization and/or redistribution techniques in order to fully exploit the computational power of
the recent computing environments. The problem is grown in importance with the new generation High Performance Computing systems aimed to achieve extreme performance. It is easy to observe that such systems are based on heterogeneous architectures integrating several independent nodes in the form of clusters or MPP systems, where each node is composed by powerful computing elements (CPU core, GPUs or other
acceleration devices) sharing resources in a single node.
These systems therefore make massive use of communication libraries to exchange data among the nodes, as well as other tools for the management of the shared resources inside a single node. For such a reason, the development of algorithms and scientific software for dynamic data structures on these heterogeneous systems implies a suitable combination of several methodologies and tools to deal with the different kinds of
parallelism corresponding to each specific device, so that to be aware of the underlying platform.
The present work is aimed to introduce a scalable model to manage a special class of dynamic data structure known as heap based priority queue (or simply heap) on these heterogeneous architectures. A heap is generally used when the applications needs set of data not requiring a complete ordering, but only the access to some items tagged with high priority. In order to ensure a tradeoff between the correct access to high priority items by the several computing units with a low communication and synchronization overhead, a suitable reorganization of the heap is needed. More precisely we introduce a unified scalable model that can be used, with no modifications, to redeploy the items of a heap both in message passing environments (such as clusters and or MMP multicomputers with several nodes) as well as in shared memory environments (such as CPUs and multiprocessors with several cores) with an overhead independent of the number of computing units. Computational results related to the application of the proposed strategy on some numerical case studies are presented for different types of computing environments
Using hybrid grid/cloud computing technologies for environmental data elastic storage, processing and provisioning
No full textHigh-resolution climate and weather forecast models, and regional and global sensor networks, are producing ever-larger quantities of multidimensional environmental data. To be useful, this data must be stored, managed, and made available to a global community of researchers, policymakers, and others.
The usual approach to addressing these problems is to operate dedicated data storage and distribution facilities. For example, the Earth System Grid (ESG) (Bernholdt et al., 2005) comprises data systems at several US laboratories, each with large quantities of storage and a high-end server configured to support requests from many remote users. Distributed services such as replica and metadata catalogs integrate these different components into a single distributed system
An integrated ClassAd-Latent Semantic Indexing matchmaking algorithm for Globus Toolkit based computing grids
No full textThe effective and efficient computing resource allocation is a critical issue in the challenging achievement of realistic, high space and temporal resolution and computing time affordable weather, marine and soil flooding simulation and forecast. Resource allocation relies on matchmaking tool, which must be flexible and straightforward to be deployed, configured and used. Using the Globus Toolkit 4 and our resource broker service, we compared the ClassAd matchmaking algorithm with our matchmaking algorithm and then we integrated the two algorithms in order to avoid their drawbacks
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