http://dl.gi.de/handle/20.500.12116/16392 2026-07-22T20:35:18Z http://dl.gi.de/handle/20.500.12116/16398 GPU-GIST – a case of generalized database indexing on modern hardware Beier, Felix; Sattler, Kai-Uwe 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. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/16397 Exploiting capabilities of modern processors in data intensive applications Broneske, David; Saake, Gunter In main-memory database systems, the time to process the data has become a limiting factor due to the missing access gap. With changing processing capabilities (e.g., branch prediction, pipelining) in every new CPU architecture, code that was optimal once will probably not stay the best code forever. In this article, we analyze processing capabilities of the classical CPU and describe code optimizations to exploit the capabilities. Furthermore, we present state-of-the-art compiler techniques that already implement code optimizations, while also showing gaps for further code optimization integration. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/16399 Architecture of a data analytics service in hybrid cloud environments Beier, Felix; Stolze, Knut DB2 for z/OS is the backbone of many transactional systems in the world. IBM DB2 Analytics Accelerator (IDAA) is IBM's approach to enhance DB2 for z/OS with very fast processing of OLAP and analytical SQL workload. While IDAA was originally designed as an appliance to be connected directly to System z, the trend in the IT industry is towards cloud environments. That offers a broad range of tools for analytical data processing tasks. 2017-01-01T00:00:00Z http://dl.gi.de/handle/20.500.12116/16396 FPGAs for data processing: Current state Teubner, Jens To escape a number of physical limitations (e.g., bandwidth and thermal issues), hardware technology is strongly trending toward heterogeneous system designs, where a large share of the application work can be off-loaded to accelerators, such as graphics or network processors. 2017-01-01T00:00:00Z