Titlebook: Lp-Theory for Incompressible Newtonian Flows; Energy Preserving Bo Matthias K?hne Book 2013 Springer Fachmedien Wiesbaden 2013 Energy Prese

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the algorithm. In this work we discuss an analysis that quickly identifies these objects through a partially flow-sensitive technique called iteration disambiguation. This is done through a relatively simple aging mechanism. We show that this analysis can distinguish objects allocated in different t

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ruct a TCP overlay so as to address connectivity and scalability issues. We have implemented our framework, gluepy as a library for Python. For evaluation, we show on over 900 cores across 9 clusters with complex networks (involving NATs and firewalls) and process managements (involving SSH, torque,

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Matthias K?hneous multicore chip named RP-X integrating 8 general purpose processor cores and 3 types of accelerator cores which was developed by Renesas Electronics, Hitachi, Tokyo Institute of Technology and Waseda University. The framework attains speedups up to 32x for an optical flow program with eight gener

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lected based on a given application and target platform. We have integrated finish accumulators into the Habanero-Java task parallel language, and used them for research and teaching. In addition to their higher-level semantics, experimental results demonstrate that our Java-based implementation of

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Matthias K?hneiagonal block) is computed. The resulting algorithm is challenging to parallelize not only because of the large size of the data sets in social network analysis, but also because it requires computing with very diverse data structures ranging from extremely sparse matrices to dense matrices. In this

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