Skew Handling in Aggregate Streaming Queries on GPUs

Abstract: Nowadays, the data to be processed by database systems has grown so large that any conventional, centralized technique is inadequate. At the same time, general purpose computation on GPU (GPGPU) recently has successfully drawn attention from the data management community due to its ability to achieve significant speed-ups at a small cost. Efficient skew handling is a well-known problem in parallel queries, independently of the execution environment. In this work, we investigate solutions to the problem of load imbalances in parallel aggregate queries on GPUs that are caused by skewed data. We present a generic load-balancing framework along with several instantiations, which we experimentally evaluate. To the best of our knowledge, this is the first attempt to present runtime load-balancing techniques for database operations on GPUs.