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dc.contributor.advisorSaman Amarasinghe.en_US
dc.contributor.authorWang, Ziheng,M. Eng.Massachusetts Institute of Technology.en_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2020-09-15T22:02:34Z
dc.date.available2020-09-15T22:02:34Z
dc.date.copyright2020en_US
dc.date.issued2020en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/127536
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020en_US
dc.descriptionCataloged from the official PDF of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 73-75).en_US
dc.description.abstractIn this thesis, I attempt to give some guidance on how to automatically optimize programs using a domain-specific-language (DSLs) compiler that exposes a set of scheduling commands. These DSLs have proliferated as of late, including Halide, TACO, Tiramisu and TVM, to name a few. The scheduling commands allow succinct expression of the programmer's desire to perform certain loop transformations, such as strip-mining, tiling, collapsing and parallelization, which the compiler proceeds to carry out. I explore if we can automatically generate schedules with good performance. The main difficulty in searching for good schedules is the astronomical number of valid schedules for a particular program. I describe a system which generates a list of candidate schedules through a set of modular stages. Different optimization decisions are made at each stage, to trim down the number of schedules considered. I argue that certain sequences of scheduling commands are equivalent in their effect in partitioning the iteration space, and introduce heuristics that limit the number of permutations of variables. I implement these ideas for the open-source TACO system. I demonstrate several orders of magnitude reduction in the effective schedule search space. I also show that for most of the problems considered, we can generate schedules better than or equal to hand-tuned schedules in terms of performance.en_US
dc.description.statementofresponsibilityby Ziheng Wang.en_US
dc.format.extent75 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleAutomatic optimization of sparse tensor algebra programsen_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Scienceen_US
dc.identifier.oclc1193031233en_US
dc.description.collectionM.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Scienceen_US
dspace.imported2020-09-15T22:02:34Zen_US
mit.thesis.degreeMasteren_US
mit.thesis.departmentEECSen_US


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