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The subject of this work is accelerating data uncertainty quantification. In particular, we are interested in expediting the stochastic estimation of the diagonal of the inverse covariance (precision) matrix that holds a wealth of information concerning the quality of data collections, especially when the matrices are symmetric positive definite and dense.(More)
In this paper we propose an approach for preparing HPC applications for Exascale. A high-level application model capturing non-functional properties combined with an abstract machine model will enable application improvements, and will serve as input to runtime systems to handle performance and energy optimizations, and self-aware fault management. A(More)
Particle accelerators are invaluable tools for research in the basic and applied sciences, in fields such as materials science, chemistry, the biosciences, particle physics, nuclear physics and medicine. The design, commissioning, and operation of accelerator facilities is a non-trivial task, due to the large number of control parameters and the complex(More)
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