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- W2805862937 abstract "In this work, we present a novel access pattern estimation approach for parallel particle tracing in flow field visualization based on deep neural networks. With strong generalization ability, we develop a Long Short-term Memory (LSTM)-based model, which is capable of learning accurate access patterns with only a few training samples and representing the learned patterns with small storage overhead. Equipped with prediction and prefetching functions driven by the developed model, our parallel particle tracing framework employs CPUs and GPUs together for particle tracing tasks. We demonstrate the accuracy and time efficiency of our approach with various flow visualization applications in three different flow datasets." @default.
- W2805862937 created "2018-06-13" @default.
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- W2805862937 date "2018-04-01" @default.
- W2805862937 modified "2023-10-10" @default.
- W2805862937 title "Access Pattern Learning with Long Short-Term Memory for Parallel Particle Tracing" @default.
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- W2805862937 doi "https://doi.org/10.1109/pacificvis.2018.00018" @default.
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