Adaptive algorithms are an important technique to achieve portable high performance. They choose among solution methods and optimizations according to expected performance on a particular machine. Grid environments make the adaptation problem harder, because the optimal decision may change across runs and even during runtime. Therefore, the performance model used by an adaptive algorithm must be able to change decisions without high overhead. In this paper, we present work that is modifying previous research into rapid performance modeling to support adaptive grid applications through sampling and high granularity modeling. We also outline preliminary results that show the ability to predict differences in performance among algorithms in the same program.
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The center for Data-intensive Systems (Daisy) at Aalborg University is currently working on a large project in which they combine the two worlds of Web 2.0 and Location-based services.

The project known as the StreamSpin-project currently employs numerous developers. Furthermore it is planned that students will have the opportunity to develop their own services for the system during the coming semesters in order to mature the system and its service deliveries.
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Stellar Phoenix Macintosh ensures a complete solution for your data loss. This comprehensive software recovers and restores lost data from a corrupt, damaged, deleted HFS+, HFS, and HFSX partitions of Mac OS. The intensive scan finds all files/folders including the deleted ones from the formatted, deleted or damaged Mac drive. This user-friendly software displays all the files and folders in tree view after scanning. The advanced recovery will restore all these files/folders to a safe location and ensure that your precious data are retrieved from the inaccessible drives.
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Whether you want to build an AJAX-based web application, or a Windows game, the Academic Resource Kit is a great place to start. A Microsoft Romania initiative aimed at driving technology access and adoption, ARK is designed as a comprehensive collection of tools and resources addressing both the development and design aspects of building software solutions. I had the chance to chat with Microsoft Romania’s Todi Pruteanu about the ARK initiative, and the interview below will provide you with a great insight into the Academic Resource Kit.
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The unbounded increase in the computation and data requirements of scientific applications has necessitated the use of widely distributed compute and storage resources to meet the demand. In a widely distributed environment, data is no more locally accessible and has thus to be remotely retrieved and stored. Efficient and reliable access to data sources and archiving destinations in such an environment brings new challenges. Placing data on temporary local storage devices offers many advantages, but such “data placements” also require careful management of storage resources and data movement, i.e. allocating storage space, staging-in of input data, staging-out of generated data, and de-allocation of local storage after the data is safely stored at the destination. Traditional systems closely couple data placement and computation, and consider data placement as a side effect of computation. Data placement is either embedded in the computation and causes the computation to delay, or performed as simple scripts which do not have the privileges of a job.
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Google ABSTRACT We have designed and implemented the Google File System, a scalable distributed file system for large distributed data-intensive applications. It provides fault tolerance while running on inexpensive commodity hardware, and it delivers high aggregate performance to a large number of clients.
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