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Auflistung nach Autor:in "Funke, Henning"

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  • Zeitschriftenartikel
    Energy Efficiency in Main-Memory Databases
    (Datenbank-Spektrum: Vol. 17, No. 3, 2017) Noll, Stefan; Funke, Henning; Teubner, Jens
    As the operating costs of today’s data centres continue to increase and processor manufacturers are forced to meet thermal design power constraints when designing new hardware, the energy efficiency of a main-memory database management system becomes more and more important. Plus, lots of database workloads are more memory-intensive than compute-intensive, which results in computing power being unused and wasted. This can become a problem because wasting computing also means wasting electrical power.In this paper, we experimentally study the impact of reducing the clock frequency of the processor and the impact of using fewer processor cores on the energy efficiency of common database algorithms such as scans, simple aggregations, simple hash joins, and state-of-the-art join algorithms. We stress the fundamental trade-off between peak performance and energy efficiency, as opposed to the established race-to-idle strategy. Ultimately, we show that reducing unused computing power significantly improves the energy efficiency of memory-bound database algorithms.
  • Textdokument
    An Overview of Hawk: A Hardware-Tailored Code Generator for the Heterogeneous Many Core Age
    (BTW 2019 – Workshopband, 2019) Breß, Sebastian; Funke, Henning; Zeuch, Steffen; Rabl, Tilmann; Markl, Volker
    Processor manufacturers build increasingly specialized processors to mitigate the effects of the power wall in order to deliver improved performance. Currently, database engines have to be manually optimized for each processor which is a costly and error prone process. In this paper, we provide a summary of our recent VLDB Journal publication, where we propose concepts to adapt to performance enhancements of modern processors and to exploit their capabilities automatically. Our key idea is to create processor-specific code variants and to learn a well-performing code variant for each processor. These code variants leverage various parallelization strategies and apply both generic and processor-specific code transformations. We observe that performance of code variants may diverge up to two orders of magnitude. Thus, we need to generate custom code for each processor for peak performance. Hawk automatically finds efficient code variants for CPUs, GPUs, and MICs.