Optimizations to a massively parallel database and support of a shared scan architecture
Name
893859361-MIT.pdf
Description
Full printable version
Size
12.4 MB
Format
Adobe PDF
Checksum (MD5)
6eb99a9c59be5354e9d327bc881d47ed
Author(s)
Ahwal, Saher B
Advisor(s)
Samuel Madden.
Date Issued
2014
Publisher
Massachusetts Institute of Technology
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. Moreover, we tackle the challenge of processing many queries (perhaps issued concurrently) where queries have very fast latency constraints, e.g, for visualization. We implement a software architecture which allows for scheduling concurrent client query requests to share processing of many queries in a single pass through the data ("shared scans"). Our experiments exhibit orders of magnitude improvement in query throughput for both, skewed and non-skewed workloads, for shared scans as opposed to serial execution.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014.
34
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 92-94).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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