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PhD Thesis: Finding the Right Processor for the Job - Co-Processors in a DBMS

Today, more and more Database Management Systems (DBMSs) keep the data com- pletely in memory during processing or even store the entire database there for fast access. In such system more algorithms are limited by the capacity of the processor, be- cause the bottleneck of Input/Output (I/O) to disk vanished. At the same time Graphics Processing Units (GPUs) have exceeded the Central Processing Unit (CPU) in terms of processing power. Research has shown that they can be used not only for graphic pro- cessing but also to solve problems of other domains. However, not every algorithm can be ported to the GPU’s architecture with benefit. First, algorithms have to be adapted to allow for parallel processing in a Single Instruction Multiple Data (SIMD) style. Sec- ond, there is a transfer bottleneck because high performance GPUs are connected via PCI-Express (PCIe) bus.
In this work we explore which tasks can be offloaded to the GPU with benefit. We show, that query optimization, query execution and application logic can be sped up under certain circumstances, but also that not every task is suitable for offloading. By giving a detailed description, implementation, and evaluation of four different examples, we explain how suitable tasks can be identified and ported.
Nevertheless, if there is not enough data to distribute a task over all available cores on the GPU it makes no sense to use it. Also, if the input data or the data generate during processing does not fit into the GPU’s memory, it is likely that the CPU produces a result faster. Hence, the decision which processing unit to use has to be made at run-time. It is depending on the available implementations, the hardware, the input parameters and the input data. We present a self-tuning approach that continually learns which device to use and automatically chooses the right one for every execution.
dissertation PhD thesis



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