Traditionally, data warehousing workloads have been processed using CPU-focused clusters, such as those that make up the bulk of available machines in Amazon's EC2, and the focus on improving analytics performance has been to utilize a homogenous, multi-threaded CPU environment with optimized algorithms for this infrastructure. The increasing availability of highly parallel accelerators, like the GPU and Xeon Phi discrete accelerators, in these types of clusters has provided an opportunity to further accelerate analytics operations but at a high programming cost due to optimizations required to fully utilize each of these new pieces of hardware. This work describes and analyzes highly parallel relational algebra primitives that are developed to focus on data warehousing queries through the use of a common OpenCL framework that can be executed both on standard multi-threaded processors and on emerging accelerator architectures. As part of this work, we propose a set of data-intensive benchmarks to help compare and differentiate the performance of accelerator hardware and to determine the key characteristics for efficiently running data warehousing queries on accelerators.