Accelerating range queries for large-scale unstructured meshes

Scientific datasets are steadily growing in size, due to increasing resolution and scale. Unstructured meshes are essential to certain fields of engineering and science, but they present special challenges for efficient access and processing. The work described in this paper accelerates range queries for very large unstructured meshes using the GPU. Prior work in the area introduced a preprocessing phase that partitions large unstructured meshes in order to improve locality in storage and memory. Here, we apply the computational power and bandwidth of GPUs to the partitioning problem, significantly reducing preprocessing time. In order to keep the GPU busy, we have to overcome the poor locality of the original unstructured mesh. Toward this end, we developed our own approach to unstructured mesh I/O, called Direct Load. We show that Direct Load significantly outperforms a typical LRU cache. Our ultimate goal is to accelerate range queries. Our preprocessing steps allow us to parallelize range query processing with relatively simple GPU code. Experimental results show that our implementation outperforms the serial implementations by 4x for preprocessing and over 100 χ for range queries.