| dc.contributor.advisor |
Wonnacott, David G. |
|
| dc.contributor.advisor |
Strout, Michelle Mills |
|
| dc.contributor.author |
Wood, Samantha |
|
| dc.date.accessioned |
2011-10-19T12:57:13Z |
|
| dc.date.available |
2011-10-19T12:57:13Z |
|
| dc.date.issued |
2011 |
|
| dc.identifier.uri |
http://hdl.handle.net/10066/7572 |
|
| dc.description.abstract |
Despite their widespread use, sparse matrix computations exhibit poor performance, due to their memory-bandwidth bound nature.
Techniques have been developed that help these computations take advantage of unexploited data reuse by transforming it into data locality,
generally improving performance. One such technique is to reorder
the matrix prior to running a computation on it. However, reordering
a matrix takes time and does not always provide performance improvements.
We present a classification model that predicts, with 82%
accuracy and no significantly incorrect predictions, whether reordering a matrix will improve the performance of the matrix power kernel, Akx. Our classifier is an ensemble of decision stumps generated by
the AdaBoost learning algorithm and is trained on 60 matrices with
a wide range of memory footprints and average number of nonzeros per
row. |
en |
| dc.description.provenance |
Made available in DSpace on 2011-10-19T12:57:13Z (GMT). No. of bitstreams: 2
2011WoodS_release.pdf: 82602 bytes, checksum: 7400087a6fab2f7cca8e83209c45fe5f (MD5)
2011WoodS_thesis.pdf: 667107 bytes, checksum: 89d3088b9aa5b651ddbb319ab6576e55 (MD5) |
en |
| dc.description.provenance |
Submitted by Haverford Student (library@haverford.edu) on 2011-10-18T17:31:35Z
No. of bitstreams: 2
2011WoodS_release.pdf: 82602 bytes, checksum: 7400087a6fab2f7cca8e83209c45fe5f (MD5)
2011WoodS_thesis.pdf: 667107 bytes, checksum: 89d3088b9aa5b651ddbb319ab6576e55 (MD5) |
en |
| dc.description.sponsorship |
Haverford College. Dept. of Computer Science |
en |
| dc.language.iso |
en_US |
en |
| dc.rights.uri |
http://creativecommons.org/licenses/by-nc/3.0/us/ |
|
| dc.subject.lcsh |
Sparse matrices |
|
| dc.subject.lcsh |
Sparse matrices -- Mathematical models |
|
| dc.title |
SMOReS: Sparse Matrix Omens of Reordering Success |
en |
| dc.type |
Thesis (B.S.) |
en |