Resource-efficient parallel query execution requires a detailed insight into its query execution affecting parameters. We design and develop new visual analysis techniques and tools that help to identify and rank performance bottlenecks of parallel query execution on multi-core systems.
We design and develop a novel learning based adaptive technique for multi-core parallel plan generation using query execution feedback. This techniques proves to be particularly efficient with concurrent workloads, a scenario which is very common in practice but has been largely uncharted in database query parallelization research.
We also introduce a simple technique where a multi-socket system is treated as a distributed shared nothing database system, where the remote memory accesses could be constrained thereby having a controlled query execution performance.
Many-core system architectures imitate GPU style parallel execution, however, data transfer on the PCle bus which connects Xeon-Phi co-processor to the host, is a bottleneck. We analyze the effect of streaming execution of selected queries, to utilize PCle bandwidth optimally.
The lessons, experiences and insights gained in this thesis are valuable for the emerging analytical database systems in the context of multi and many-core systems.
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