Name
Abstract | ||
|---|---|---|
We present MaSiF, a novel tool to auto-tune parallelization parameters of skeleton parallel programs. It reduces the cost of searching the optimization space using a combination of machine learning and linear dimensionality reduction. To auto-tune a new program, a set of program features is determined statically and used to compute k nearest neighbors from a set of training programs. Previously collected performance data for the nearest neighbors is used to reduce the size of the search space using Principal Components Analysis. This results in a set of eigenvectors that are used to search the reduced space. MaSiF achieves 88% of the performance of the oracle, which searches a random set of 10,000 parameter values. MaSiF searches just 45 points, or 0.45% of the optimization space, to achieve this performance. MaSiF provides an average speedup of 1.18x over parallelization parameters chosen by a human expert. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2370816.2370884 | HiPC |
Keywords | DocType | ISSN |
multi core,machine learning | Conference | 1089-795X |
ISBN | Citations | PageRank |
978-1-5090-6609-4 | 5 | 0.44 |
References | Authors | |
11 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| alexander collins | 1 | 5 | 0.44 |
| Christian Fensch | 2 | 170 | 8.91 |
| Hugh Leather | 3 | 182 | 14.33 |
| Murray Cole | 4 | 876 | 57.81 |