Title :
Simple Power-Aware Scheduler to Limit Power Consumption by HPC System within a Budget
Author :
Bodas, Deva ; Song, Jian ; Rajappa, Murali ; Hoffman, Andy
Abstract :
Future Exascale systems are projected to require tens of megawatts. While facilities must provision sufficient power to realize peak performance, limited power availability will require power capping. Current approaches for power capping limit CPU power state and are agnostic to workload characteristics. Injudicious use of such mechanisms in HPC system can impose a devastating impact on performance. We propose integrating power limiting into a job scheduler. We will describe a power-aware scheduler that monitors power consumption, distributes the power budget to each job, and implements a "uniform frequency" mechanism to limit power. We will compare three implementations of uniform frequency. We will show that power monitoring improves the probability of launching a job earlier, allows a job to run faster, and reduces stranded power. Our data shows that "auto mode" for uniform frequency operates at 40% higher frequency than a fixed frequency mode.
Keywords :
parallel processing; power aware computing; power consumption; scheduling; HPC system; power budget; power consumption; power limiting; power monitoring; power-aware scheduler; uniform frequency mechanism; Calibration; Energy efficiency; Job shop scheduling; Materials requirements planning; Monitoring; Power demand; Resource management; Resource manager; scheduler; energy efficiency; power manager; power limiting; HPC; Exascale; IPMI;
Conference_Titel :
Energy Efficient Supercomputing Workshop (E2SC), 2014
Conference_Location :
New Orleans, LA
DOI :
10.1109/E2SC.2014.8