Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware

Sebastian Bress, Max Heimel, Michael Saecker, Bastian Kocher, Volker Markl, Gunter Saake
TU Dortmund University
40th International Conference on Very Large Data Bases (VLDB), 2014

   title={Ocelot/HyPE: Optimized Data Processing on Heterogeneous Hardware},

   author={Bre{ss}, Sebastian and Heimel, Max and Saecker, Michael and K{"o}cher, Bastian and Markl, Volker and Saake, Gunter},

   journal={Proceedings of the VLDB Endowment},





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The past years saw the emergence of highly heterogeneous server architectures that feature multiple accelerators in addition to the main processor. Efficiently exploiting these systems for data processing is a challenging research problem that comprises many facets, including how to find an optimal operator placement strategy, how to estimate runtime costs across different hardware architectures, and how to manage the code and maintenance blowup caused by having to support multiple architectures. In prior work, we already discussed solutions to some of these problems: First, we showed that specifying operators in a hardware-oblivious way can prevent code blowup while still maintaining competitive performance when supporting multiple architectures. Second, we presented learning cost functions and several heuristics to efficiently place operators across all available devices. In this demonstration, we provide further insights into this line of work by presenting our combined system Ocelot/HyPE. Our system integrates a hardware-oblivious data processing engine with a learning query optimizer for placement decisions, resulting in a highly adaptive DBMS that is specifically tailored towards heterogeneous hardware environments.
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