Real time power estimation and thread scheduling via performance counters

By Karan Singh, Major Bhadauria, Sally A. McKee. CARN, 2009

329 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Hardware and Architecture

View Paper: https://www.semanticscholar.org/paper/a9c1fa73f2c830f1f1d8526042a357a697dcfab4

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Eva García-Martín
2. Hojung Cha
3. Håkan Grahn
4. Crefeda Faviola Rodrigues
5. Ryan D. Riley
6. Graham D. Riley

In Hardware and Architecture

1. Eva García-Martín
2. Håkan Grahn
3. Dmitry Evtyushkin
4. Crefeda Faviola Rodrigues
5. Ryan D. Riley
6. Graham D. Riley

Citations in the corpus

• Estimation of energy consumption in machine learning (2019, methodology): “[76] Instruction Current Analytical Energy Profiling CPU Model Yes [10] Hardware Analytical Analytical Power, Energy, Perf Simu data CPU Theoretical Yes [80] Hardware Analytical Transition-sensitive Energy/Power Simulation CPU, f units Theoretical – [44] Hardware Real Heuristics Power PMC, sim data ”
• On the Detection of Kernel-Level Rootkits Using Hardware Performance Counters (2017, methodology): “HPCs are also used for other purposes; for example, they can be used for power [25] and temperature [12] analysis.”
• AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring (2012, methodology): “Usage estimation for hardware components has been previously completed using hardware performance counter (HPC) [5, 10-13], software performance counter (SPC) such as the Linux procfs/sysfs [7, 14-16], or BatteryStats, which is provided by Android [8, 17].”