Advances in computer architecture research yield increasingly powerful processors which can execute code at a much faster pace than they can access data in the memory hierarchy. Database management systems (DBMS), due to their intensive data processing nature, are in the front line of commercial applications which cannot harness the available computing power. To prevent processors from idling, a multitude of hardware mechanisms and software optimizations have been proposed. Their effectiveness, however, is limited by the sheer volume of data accessed and by the unpredictable sequence of memory requests. This Ph.D. dissertation introduces Staged Database Systems, a new software architecture for optimizing data and instruction locality at all levels of the memory hierarchy. The key idea is to break database request execution in stages and process a group of subrequests at each stage. Group processing at each stage allows for a context-aware execution sequence of requests that promotes reusability of both instructions and data. The Staged Database System design requires only a small number of changes to the existing DBMS codebase and provides a new set of execution primitives that allow software to gain increased control over what data and instructions are accessed, when, and by which requests. The central thesis is the following:

"By organizing and assigning system components into self-contained stages, database systems can exploit instruction and data commonality across concurrent requests thereby improving performance."

166 pages

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