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dc.contributor.advisor Jermaine, Christopher
dc.creatorLuo, Shangyu
dc.date.accessioned 2020-11-03T20:02:22Z
dc.date.available 2020-11-03T20:02:22Z
dc.date.created 2020-05
dc.date.issued 2020-10-30
dc.date.submitted May 2020
dc.identifier.citation Luo, Shangyu. "Automatic Matrix Format Exploration for Large Scale Linear Algebra." (2020) Diss., Rice University. https://hdl.handle.net/1911/109492.
dc.identifier.urihttps://hdl.handle.net/1911/109492
dc.description.abstract The input of a linear algebra (LA) operation, such as matrices and vectors, could be stored in multiple ways: rows/columns, strips, blocks, etc. Usually, it is very difficult for a programmer to figure out the proper format to use to make a LA computation run fast. Predicting and optimizing the runtime behavior of a LA computation is not an easy task, even when one has expert knowledge of the underlying execution engine. The situation is particularly difficult if the computation consists of thousands of operations, and those operations must be run in a distributed manner. In this paper, we argue that we can render a parallel relational database to automatically explore the formats of LA computations. More specifically, our system would take in the existing code and analyze the operations in the code, explore different formats for those operations and select the most efficient formats, and finally automatically generate the new code to run those operations in their selected formats. We show that our implementation is able to find the formats that have a better performance than the formats that are manually picked up by an expert user of the system.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subjectDistributed Database Systems
Large-scale Linear Algebra
Machine Learning
dc.title Automatic Matrix Format Exploration for Large Scale Linear Algebra
dc.type Thesis
dc.date.updated 2020-11-03T20:02:22Z
dc.type.material Text
thesis.degree.department Computer Science
thesis.degree.discipline Engineering
thesis.degree.grantor Rice University
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy


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