Name
Abstract | ||
|---|---|---|
Parallel programming and effective partitioning of applications for embedded many-core architectures requires optimization algorithms. However, these algorithms have to quickly evaluate thousands of different partitions. We present a fast performance estimator embedded in a parallelizing compiler for streaming applications. The estimator combines a single execution-based simulation and an analytic approach. Experimental results demonstrate that the estimator has a mean error of 2.6% and computes its estimation 2848 times faster compared to a cycle accurate simulator. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/2852339.2852342 | PROCEEDINGS OF THE 2016 WORKSHOP ON RAPID SIMULATION AND PERFORMANCE EVALUATION: METHODS AND TOOLS, RAPIDO'16 |
Field | DocType | Citations |
Computer science,Parallel computing,Performance estimation,Mean squared error,Compiler,Optimization algorithm,MPSoC,Estimator | Conference | 0 |
PageRank | References | Authors |
0.34 | 9 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Martin Flasskamp | 1 | 10 | 2.63 |
| Gregor Sievers | 2 | 19 | 3.26 |
| Johannes Ax | 3 | 15 | 3.81 |
| Christian Klarhorst | 4 | 0 | 2.03 |
| Thorsten Jungeblut | 5 | 33 | 7.67 |
| Wayne A. Kelly | 6 | 25 | 6.10 |
| Michael Thies | 7 | 84 | 10.01 |
| Mario Porrmann | 8 | 420 | 50.91 |