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DTEND:20220915T120000Z
UID:26118c86500b668c19b06e38a059fab7-330
DTSTAMP:19700101T120015Z
DESCRIPTION:Building a GPU-powered Spatial Query Engine
URL;VALUE=URI:https://www.csa.iisc.ac.in/newweb/event/330/building-a-gpu-powered-spatial-query-engine/
SUMMARY:Given the massive growth in the volume of spatial data, there is a great need for systems that can efficiently evaluate spatial queries over large data sets. These queries are notoriously expensive using traditional database solutions. While faster response times can be attained through powerful clusters or servers with large main memory, these options, due to cost and complexity, are out of reach to many data scientists and analysts making up the long tail.

Graphics Processing Units (GPUs), which are now widely available even in commodity laptops, provide a cost-effective alternative to support high-performance computing, opening up new opportunities to the efficient evaluation of spatial queries. While GPU-based approaches proposed in the literature have shown great improvements in performance, they are tied to specific GPU hardware and only handle specific queries over fixed geometry types.

As a first step towards making GPU spatial query processing mainstream, we propose a new model that uniformly represents spatial data as geometric objects and define an algebra consisting of GPU-friendly composable operators that operate over these objects. This is then used to develop SPADE, a spatial query engine that uses the computer graphics pipeline to realize this algebra and data model to achieve both efficiency and portability across different GPU hardware. In this talk, I will briefly go over the algebra, and discuss the implementation and efficiency of the SPADE system.
DTSTART:20220915T120000Z
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