Predicting the Performance of Automatically Scalable Computation (ASC)
dc.contributor.author | Wang, Serena Lutong | |
dc.date.accessioned | 2019-03-26T10:41:32Z | |
dc.date.created | 2016-05 | |
dc.date.issued | 2016-06-21 | |
dc.date.submitted | 2016 | |
dc.identifier.uri | http://nrs.harvard.edu/urn-3:HUL.InstRepos:38811450 | * |
dc.description.abstract | The Automatically Scalable Computation (ASC) architecture is a new approach to automatic parallelization that transforms parallelization into a machine learning problem. The underlying principle is that if we observe the state of the machine repeatedly at a given place in a sequential program, we can build a model to predict the state of the machine at that place over time. The ``place" in the program is defined by an address loaded into the instruction pointer (IP). We present a machine learning approach to automatic IP selection. Our approach relies on the observation that the error function of ASC's internal machine learning model decreases over time for a good IP value. For a given program and IP value, we build a predictive model for ASC's error function value over time. The inputs are a program and IP value and the target is ASC's error function value. We present methods for representing the program and IP value as a feature set. We also present and evaluate three different machine learning models for predicting the error function values. | |
dc.format.mimetype | application/pdf | |
dc.language.iso | en | |
dash.license | LAA | |
dc.subject | Computer Science | |
dc.title | Predicting the Performance of Automatically Scalable Computation (ASC) | |
dc.type | Thesis or Dissertation | |
dash.depositing.author | Wang, Serena Lutong | |
dc.date.available | 2019-03-26T10:41:32Z | |
thesis.degree.date | 2016 | |
thesis.degree.grantor | Harvard College | |
thesis.degree.level | Undergraduate | |
thesis.degree.name | AB | |
dc.type.material | text | |
thesis.degree.department | Computer Science | |
dash.identifier.vireo | http://etds.lib.harvard.edu/college/admin/view/149 | |
dc.identifier.orcid | 0000-0001-9664-4609 | |
dash.author.email | serenalwang@gmail.com |
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FAS Theses and Dissertations [6136]