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On the Impact of Hardware on Relational Query Processing
Dr.-Ing. David Broneske
Satisfying the performance needs of tomorrow typically implies using modern processor capabilities (such as single instruction, multiple data) and co-processors (such as graphics processing units) to accelerate database operations. Algorithms are typically hand-tuned to the underlying (co-)processors. This solution is error-prone, introduces high implementation and maintenance cost and is not portable to other (co-)processors. To this end, we argue for a combination of database research with modern software-engineering approaches, such as feature-oriented software development (FOSD). Thus, the goal of this project is to generate optimized database algorithms tailored to the underlying (co-)processors from a common code base. With this, we maximize performance while minimizing implementation and maintenance effort in databases on new hardware. Project milestones: 
  • Creating a feature model: Arising from heterogeneous processor capabilities, promising capabilities have to be identified and structured to develop a comprehensive feature model. This includes fine-grained features that exploit the processor capabilities of each device.
  • Annotative vs. compositional FOSD approaches: Both approaches have known benefits and drawbacks. To have a suitable mechanism to construct hardware-tailored database algorithms using FOSD, we have to evaluate which of these two approaches is the best for our scenario.
  • Mapping features to code: Arising from the feature model, possible code snippets to implement a feature have to be identified.
  • Performance evaluation: To validate our solution and derive rules for processor allocation and algorithm selection, we have to perform an evaluation of our algorithms.


APU, CPU, FPGA, GPU, MIC, heterogeneity of processing devices, tailored database operations

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