Revisiting Approximate Query Processing and Bootstrap Error Estimation on GPU

Sampling-based Approximate Query Processing (AQP) is one of the promising approaches for timely and cost-effective analytics over big data. There are mainly two methods to estimate errors of approximate query results, namely analytical method and bootstrap method. Although the bootstrap method is much more general than the first method, it is rarely used in the existing AQP system due to its high computation overhead. In this paper, we propose to use the powerful GPU and a series of advanced optimization mechanisms to accelerate bootstrap, thus make it feasible to address the essential err r estimation problem for AQP by utilizing bootstrap. Besides, since modern GPUs have bigger and bigger memory capacity, we can store samples in the GPU memory and use GPU to accelerate the execution of AQP queries in addition to using GPU to accelerate the bootstrap-based error estimation. Extensive experiments on the SSB benchmark show that our GPU-accelerated method is at most about two orders of magnitude faster than the CPU method.