Name
Abstract | ||
|---|---|---|
Time-stepping simulation methods offer potential for self-adaptivity, since the first time steps of the simulation can be used to explore the hardware characteristics and measure which of several available implementation variants leads to a good performance and energy consumption on the given hardware platform. The version with the best performance or the smallest energy consumption can then be used for the remaining time steps. However, the number of variants to test may be quite large and different simulation methods may require different approaches for self-adaptivity. In this article, we explore the potential for self-adaptivity of several methods from scientific computing. In particular, we consider particle simulation methods, solution methods for differential equations, as well as sparse matrix computations and explore the potential for self-adaptivity of these methods, considering both performance and energy consumption as target function. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/CAHPC.2018.8645887 | 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD) |
Keywords | Field | DocType |
autotuning,performance and energy analysis,time-stepping methods | Differential equation,Sparse matrix computations,Particle simulation,Computer science,Parallel computing,Computer engineering,Energy consumption,Self adaptivity | Conference |
ISSN | ISBN | Citations |
1550-6533 | 978-1-5386-7769-8 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Natalia Kalinnik | 1 | 20 | 5.04 |
| Robert Kiesel | 2 | 0 | 1.69 |
| Thomas Rauber | 3 | 415 | 64.60 |
| Marcel Richter | 4 | 0 | 1.01 |
| Gudula Rünger | 5 | 608 | 90.35 |