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Recently, the computational requirements for large scale data-intensive analysis of scientific data have grown significantly. In High Energy Physics (HEP) for example, the Large Hadron Collider (LHC) produced 13 petabytes of data in 2010. This huge amount of data are processed on more than 140 computing centers distributed across 34 countries. The MapReduce paradigm has emerged as a highly successful...
Cloud Computing is a novel computing paradigm. It provides on-demand computing power in a simple way that users can access the resources via Internet with a friendly interface based on Web services. Grid Computing is another computing paradigm that has been established for more than a dozen of years. In contrast to Cloud Computing, Grid Computing adopts a rigid access interface that requires users'...
We present a scalable run-time configurable and programmable signal processing architecture for real-time applications which covers a wide performance spectrum. Our approach goes beyond conventional special purpose signal processing engines. Scalability has multiple dimensions: on core- and network-level. We base our novel architecture on programmable components which can be re-combined and re-configured...
Filter caches have been proposed to decrease the energy consumption on embedded systems. However, the achievement in energy is usually acquired with a loss in performance. This work investigates a novel filter cache architecture that outperforms a traditional one, while maintaining the energy advantages. The performance gain is achieved by only allocating the data with a high reuse potential into...
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