Name
Title | ||
|---|---|---|
Applying EMD/HHT analysis to power traces of applications executed on systems with Intel Xeon Phi |
Abstract | ||
|---|---|---|
AbstractPower draw is a complex physical response to the workload of a given application on the hardware, which is difficult to model, in part, due to its variability. The empirical mode decomposition and Hilbert–Huang transform (EMD/HHT) is a method commonly applied to physical systems varying with time to analyze their complex behavior. In authors’ work, the EMD/HHT is considered for the first time to study power usage of high-performance applications. Here, this method is applied to the power measurement sequences (called here power traces) collected on three different computing platforms featuring two generations of Intel Xeon Phi, which are an attractive solution under the power budget constraints. The high-performance applications explored in this work are codesign molecular synamics and general atomic and molecular electronic structure system—which exhibit different power draw characteristics—to showcase strengths and limitations of the EMD/HHT analysis. Specifically, EMD/HHT measures intensity of an execution, which shows the concentration of power draw with respect to execution time and provides insights into performance bottlenecks. This article compares intensity among executions, noting on a relationship between intensity and execution characteristics, such as computation amount and data movement. In general, this article concludes that the EMD/HHT method is a viable tool to compare application power usage and performance over the entire execution and that it has much potential in selecting most appropriate execution configurations. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1177/1094342017731612 | Periodicals |
Keywords | Field | DocType |
Energy, power, performance, Intel Xeon Phi, empirical mode decomposition, Hilbert-Huang transform, Sandia PowerAPI, CoMD, GAMESS | Xeon Phi,Computer science,Parallel computing,Operating system | Journal |
Volume | Issue | ISSN |
34 | 2 | 1094-3420 |
Citations | PageRank | References |
0 | 0.34 | 21 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gary Lawson | 1 | 32 | 4.14 |
| Masha Sosonkina | 2 | 272 | 45.62 |
| Tal Ezer | 3 | 2 | 0.75 |
| Yuzhong Shen | 4 | 184 | 21.96 |