초록

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Abstract ▼ AI-Helper

We propose a visualization pipeline design for cluster-based scientific visualization tools and share an implementation example. Our visualization pipeline is composed of visualization pipes that represent each visualization method. Visualization pipes follow a unified structure, and this structure ...

We propose a visualization pipeline design for cluster-based scientific visualization tools and share an implementation example. Our visualization pipeline is composed of visualization pipes that represent each visualization method. Visualization pipes follow a unified structure, and this structure guarantees flexible connectivity among pipes. By using dedicated pipes (i.e., interface pipes) for communication between our pipeline and visualization tools using that, we can implement visualization pipes independently with the tools. The proposed visualization pipeline is simply extended to a cluster-based visualization pipeline, by building a sub-pipeline consisting of only visualization pipes on each slave node and connecting with interface pipes in the master node. A visualization pipeline is built on the fly according to the visualization request from users. We applied our visualization pipeline design to an existing cluster-based visualization framework (GLOVE) and implemented it in a cluster with 32 computing nodes (i.e., slave nodes). Then we applied it to four different large scientific datasets, and found that our cluster-based visualization pipeline successfully handles various combinations of visualization queries. Also, it remained stable, as the number of slave nodes increased. For a given combination of visualization queries, GLOVE with our pipeline shows up to 7.72 times higher performance, compared with the original GLOVE. These results demonstrate the usefulness and scalability of our visualization pipeline design.

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