GLOVE: An Interactive Visualization Service Framework with Multi-Dimensional Indexing on the GPU
Authors: Jinwoong Kim (Ulsan National Institute of Science and Technology), Sehoon Lee (Korea Institute of Science and Technology Information), Joong-Youn Lee (Korea Institute of Science and Technology Information), Beomseok Nam (Ulsan National Institute of Science and Technology), Min Ah Kim (Korea Institute of Science and Technology Information)
Abstract: In this poster, we present an interactive visualization service framework - GLOVE for massively large scale scientific datasets. GLOVE is designed to support multiple user workloads and to provide interactive user interfaces. GLOVE improves the scalability of the framework and reduces the query latency by managing datasets in large scale distributed memories and by employing multi-dimensional indexing trees that help navigate through massively large datasets. In this poster, we primarily focus on our design and implementation of GPU-based multi-dimensional indexing for GLOVE. Our massively parallel tree traversal algorithm - MPRS for the
GPU avoids irregular memory access patterns and recursive backtracking and significantly improves the warp efficiency of the GPU and reduces query response time. Our preliminary experiments show GPU-based indexing accelerates the query performance by an order of magnitude.
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