In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a ...
Our current study examines in detail the performance of streamline computation for large-scale data sets. The same considerations also apply to pathlines ...
In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a ...
In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a ...
Bibliographic details on Scalable computation of streamlines on very large datasets.
Ahern, and G. Weber, “Scalable computation of streamlines on very large datasets,” in High Performance. Computing Networking, Storage and Analysis, Proceedings ...
A scalable method for ab initio computation of free energies in nanoscale systems... ... Scalable Computation of Streamlines on Very Large Datasets... Conference ...
May 22, 2024 · Simpler models, such as linear models, decision trees, or Naive Bayes classifiers, can scale well to large datasets and offer satisfactory ...
Contact. 865.241.8990 | [email protected]. Publications. November 2009. Scalable Computation of Streamlines on Very Large Datasets... Conference Paper.
To compute streamlines on very large datasets in a distributed setting, Camp et al. [CGC∗11] study the benefits of parallelize-over-seeds and parallelize ...