GPU-GIST – a case of generalized database indexing on modern hardware

Abstract

Abstract A lot of different indexes have been developed for accelerating search operations on large data sets. Search trees, representing the most prominent class, are ubiquitous in database management systems but are also widely used in non-DBMS applications. An approach for lowering the implementation complexity of these structures are index frameworks like generalized search trees (GiST). Common data management operations are implemented within the framework which can be specialized by data organization and evaluation strategies in order to model the actual index type. These frameworks are particularly useful in scientific and engineering applications where characteristics of the underlying data set are not known a priori and a lot of prototyping is required in order to find suitable index structures for the workload. However, existing frameworks only abstract data organization and data maintenance aspects to model different index families, while traversal operations for executing searches are implemented serially. This paper presents an approach for enabling parallel processing in GiST in order to leverage the full power of parallel processor architectures for different index implementations at once. Further, results of a prototypical implementation are evaluated on a hybrid CPU/GPU system architecture to verify the applicability of this generic framework idea on different hardware platforms.