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GPU-accelerated Join-Order Optimization
MSc Andreas Meister
Different join orders can lead to a variation of execution times by several orders of magnitude, which makes join-order optimization to one of the most critical optimizations within DBMSs. At the same time, join-order optimization is an NP-hard problem, which makes the computation of an optimal join-order highly compute-intensive. Because current hardware architectures use highly specialized and parallel processors, the sequential algorithms for join-order optimization proposed in the past cannot fully utilize  the computational power of current hardware architectures. Although existing approaches for join-order optimization such as dynamic programming benefit from parallel execution, there are no approaches for join-order optimization on highly parallel co-processors such as GPUs. 
In this project, we are building a GPU-accelerated  join-order optimizer by adapting existing join-order optimization approaches. Here, we are interested in the effects of GPUs on join-order optimization itself as well as the effects for query processing. For GPU-accelerated DBMSs, such as CoGaDB, using GPUs for query processing, we need to identify efficient scheduling strategies for query processing and query optimization tasks such that the GPU-accelerated optimization does not 
slow down query processing on GPUs.


gpu-accelerated datamangement, self-tuning

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