Name
Abstract | ||
|---|---|---|
Cache memories are an important component of modern processors and consume a large percentage of the processor's power consumption. The quality of service of this cache memories relies heavily on the memory demands of the software, what means that a certain program might benefit more from a certain cache configuration which is highly inefficient for another program. Moreover, finding the optimal cache configuration for a certain program is not a trivial task and usually, involves exhaustive simulation. In this paper, we propose a machine learning-based methodology that, given an unknown application as input, it outputs a prediction of the optimal cache reconfiguration for that application, regarding energy consumption and performance. We evaluated our methodology using a large benchmark suite, and our results show a 99.8% precision at predicting the optimal cache configuration for a program. Furthermore, further analysis of the results indicates that 85% of the mispredictions produce only up to a 10% increase in energy consumption in comparison to the optimal energy consumption. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1007/978-3-319-56258-2_27 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
Cache design,Machine learning cache,Cache tuning,Cache prediction,Cache recommendation | Suite,Active learning (machine learning),Computer science,Cache,Quality of service,Cache algorithms,Software,Artificial intelligence,Energy consumption,Machine learning,Control reconfiguration | Conference |
Volume | ISSN | Citations |
10216 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Osvaldo Navarro | 1 | 3 | 1.79 |
| Jones Yudi Mori | 2 | 20 | 4.96 |
| Javier Hoffmann | 3 | 1 | 0.37 |
| Fabian Stuckmann | 4 | 1 | 0.37 |
| Hubner, Michael | 5 | 390 | 47.98 |