We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
To achieve scalable parallel performance in Molecular Dynamics Simulation, we have modeled and implemented several dynamic spatial domain decomposition algorithms. The modeling is ...
Lars S. Nyland, Jan Prins, Ru Huai Yun, Jan Herman...
Efficient storage and retrieval of large multidimensional datasets is an important concernfor large-scale scientific computations such as long-running time-dependent simulations w...
Data locality is critical to achievinghigh performance on large-scale parallel machines. Non-local data accesses result in communication that can greatly impact performance. Thus ...
Data sources, storage, computing resources and services are entities on Grids that require mechanisms for publication and lookup. A discovery service relies on efficient lookup to...