MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
  • DSpace@MIT Home
  • MIT Libraries
  • MIT Theses
  • Graduate Theses
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Automatic optimization of sparse tensor algebra programs

Author(s)
Wang, Ziheng,M. Eng.Massachusetts Institute of Technology.
Thumbnail
Download1193031233-MIT.pdf (922.1Kb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Saman Amarasinghe.
Terms of use
MIT 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. http://dspace.mit.edu/handle/1721.1/7582
Metadata
Show full item record
Abstract
In 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.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, May, 2020
 
Cataloged from the official PDF of thesis.
 
Includes bibliographical references (pages 73-75).
 
Date issued
2020
URI
https://hdl.handle.net/1721.1/127536
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.

Collections
  • Graduate Theses

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.