Name
Abstract | ||
|---|---|---|
We investigate an automatic method for classifying which regions of sequential programs could be parallelized, using dynamic features of the code collected at runtime. We train a supervised learning algorithm on versions of the NAS Parallel Benchmark (NPB) code hand-annotated with OpenMP parallelization directives in order to approximate the parallelization that might be produced by a human expert. A model comparison shows that support vector machines and decision trees have comparable performance on this classification problem, but boosting using AdaBoost is able to increase the performance of the decision trees. We further analyze the relative importance of the collected program features and demonstrate that within-loop instruction counts provide the greatest contribution to decision tree error reduction, with dependency graph features of secondary importance. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/ICMLA.2013.108 | ICMLA (2) |
Keywords | Field | DocType |
secondary importance,openmp parallelization directive,predicting parallelization,dependency graph feature,decision tree error reduction,comparable performance,decision tree,classification problem,automatic method,parallel benchmark,sequential programs,relative importance,supervised learning,parallel programming,decision trees,learning artificial intelligence,support vector machines,feature extraction | Decision tree,Computer science,Artificial intelligence,Dependency graph,AdaBoost,Pattern recognition,Support vector machine,Parallel computing,Feature extraction,Supervised learning,Boosting (machine learning),Machine learning,Automatic parallelization | Conference |
Citations | PageRank | References |
0 | 0.34 | 7 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Fried | 1 | 83 | 7.69 |
| Zhen Li | 2 | 32 | 4.10 |
| Ali Jannesari | 3 | 149 | 20.11 |
| Felix Wolf | 4 | 57 | 12.00 |