Automatic creation of tile size selection models using neural networks
| dc.contributor.author | Yuki, Tomofumi, author | |
| dc.contributor.author | Rajopadhye, Sanjay, advisor | |
| dc.contributor.author | Anderson, Charles, committee member | |
| dc.contributor.author | Casterella, Gretchen, committee member | |
| dc.contributor.author | Strout, Michelle, committee member | |
| dc.date.accessioned | 2022-04-08T19:04:29Z | |
| dc.date.available | 2022-04-08T19:04:29Z | |
| dc.date.issued | 2010 | |
| dc.description | Covers not scanned. | |
| dc.description | Print version deaccessioned 2022. | |
| dc.description.abstract | Tiling is a widely used loop transformation for exposing/exploiting parallelism and data locality. Effective use of tiling requires selection and tuning of the tile sizes. This is usually achieved by hand-crafting tile size selection (TSS) models that characterize the performance of the tiled program as a function of tile sizes. The best tile sizes are selected by either directly using the TSS model or by using the TSS model together with an empirical search. Hand-crafting accurate TSS models is hard, and adapting them to different architecture/compiler, or even keeping them up-to-date with respect to the evolution of a single compiler is often just as hard. Instead of hand-crafting TSS models, can we automatically learn or create them? In this paper, we show that for a specific class of programs fairly accurate TSS models can be automatically created by using a combination of simple program features, synthetic kernels, and standard machine learning techniques. The automatic TSS model generation scheme can also be directly used for adapting the model and/or keeping it up-to-date. We evaluate our scheme on six different architecture-compiler combinations (chosen from three different architectures and four different compilers). The models learned by our method have consistently shown near-optimal performance (within 5% of the optimal on average) across the tested architecture-compiler combinations. | |
| dc.format.medium | masters theses | |
| dc.identifier.uri | https://hdl.handle.net/10217/234648 | |
| dc.language | English | |
| dc.language.iso | eng | |
| dc.publisher | Colorado State University. Libraries | |
| dc.relation | Catalog record number (MMS ID): 991014242009703361 | |
| dc.relation | QA76.87 .Y955 2010 | |
| dc.relation.ispartof | 2000-2019 | |
| dc.rights | Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright. | |
| dc.subject.lcsh | Tiling (Mathematics) | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.title | Automatic creation of tile size selection models using neural networks | |
| dc.type | Text | |
| dcterms.rights.dpla | This Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s). | |
| thesis.degree.discipline | Computer Science | |
| thesis.degree.grantor | Colorado State University | |
| thesis.degree.level | Masters | |
| thesis.degree.name | Master of Science (M.S.) |
Files
Original bundle
1 - 1 of 1
Loading...
- Name:
- ETDF_2010_Spring_Yuki_Tomofumi.pdf
- Size:
- 14.95 MB
- Format:
- Adobe Portable Document Format