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- W2802259585 abstract "We explore rearrangements of classical uncertainty quantification methods with the aim of achieving higher aggregate performance for uncertainty quantification calculations on emerging multicore and many core architectures. We show a rearrangement of the stochastic Galerkin method leads to improved performance and scalability on several computational architectures whereby uncertainty information is propagated at the lowest levels of the simulation code improving memory access patterns, exposing new dimensions of fine grained parallelism, and reducing communication. We also develop a general framework for implementing such rearrangements for a diverse set of uncertainty quantification algorithms as well as computational simulation codes to which they are applied." @default.
- W2802259585 created "2018-05-17" @default.
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- W2802259585 date "2014-09-01" @default.
- W2802259585 modified "2023-09-26" @default.
- W2802259585 title "Exploring Heterogeneous Multicore Architectures for Advanced Embedded Uncertainty Quantification" @default.
- W2802259585 doi "https://doi.org/10.2172/1433072" @default.
- W2802259585 hasPublicationYear "2014" @default.
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