Haggis: turbocharge a MapReduce based spatial data warehousing system with GPU engine

Spatial query processing involves complex multidimensional objects and compute intensive spatial operations, and therefore requires a high performance approach to meet the rapid data analytics requirements of modern spatial applications. Recently, MapReduce based spatial query systems have become a viable solution for many data intensive query tasks, and gained widespread adoption in both academia and industry. At the same time, GPUs have been successfully utilized in many applications that require high performance computation. Both approaches, GPU and MapReduce, have their own limitations and advantages, and have been separately utilized in spatial query processing tasks to boost application performance. However, it is unclear that how MapReduce and GPU, two vastly different parallelization techniques, can be fused together to effectively deal with the spatial big data challenges. In this paper, we explore such synergy of parallelization techniques for large scale spatial query processing. We extend Hadoop-GIS, a MapReduce based spatial query system, and provide GPU accelerated spatial query processing capability at the engine level. We evaluate the system on a real world dataset, and demonstrate that GPU accelerated system can gain considerable performance improvements. We also show how other factors such as partition granularity, task scheduling between CPU and GPU can impact the query performance.