{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/91454"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/91454","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Optimizations to a massively parallel database and support of a shared scan architecture","abstract":"This thesis presents a new architecture and optimizations to MapD, a database server which uses a hybrid of multi-CPU/multi-GPU architecture for query execution and analysis. We tackle the challenge of partitioning the data across multiple nodes with many CPUs and GPUs by means of an indexing framework. We implement a QuadTree spatial partitioning scheme and demonstrate how it improves the latencies of many queries when using the index as opposed to not using any. 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