An Adaptive Hybrid OLAP Architecture with optimized memory access patterns

摘要

OLAP (On-Line Analytical Processing) is an approach to efficiently evaluate multidimensional data for business intelligence applications. OLAP contributes to business decision-making by identifying, extracting, and analyzing multidimensional data. The fundamental structure of OLAP is a data cube that enables users to interactively explore the distinct data dimensions. Processing depends on the complexity of queries, dimensionality, and growing size of the data cube. As data volumes keep on increasing and the demands by business users also increase, higher processing speed than ever is needed, as faster processing means faster decisions and more profit to industry. In this paper, we are proposing an Adaptive Hybrid OLAP Architecture that takes advantage of heterogeneous systems with GPUs and CPUs and leverages their different memory subsystems characteristics to minimize response time. Thus, our approach (a) exploits both types of hardware rather than using the CPU only as a frontend for GPU; (b) uses two different data formats (multidimensional cube and relational cube) to match the GPU and CPU memory access patterns and diverts queries adaptively to the best resource for solving the problem at hand; (c) exploits data locality of multidimensional OLAP on NUMA multicore systems through intelligent thread placement; and (d) guides its adaptation and choices by an architectural model that captures the memory access patterns and the underlying data characteristics. Results show an increase in performance by roughly four folds over the best known related approach. There is also the important economical factor. The proposed hybrid system costs only 10 % more than same system without GPU. With this small extra cost, the added GPU increases query processing by almost 2 times.