Modern high performance computing subsystems (HPC) - including processor,
network, memory, and IO - are provided with power management mechanisms.
These include dynamic speed scaling and dynamic resource sleeping.
Understanding the behavioral patterns of high performance computing
systems at runtime can lead to a multitude of optimization opportunities
including controlling and limiting their energy usage. In this paper,
we present a general purpose methodology for optimizing energy performance
of HPC systems considering processor, disk and network. We rely on
the concept of execution vector along with a partial phase recognition
technique for on-the-fly dynamic management without any a priori
knowledge of the workload. We demonstrate the effectiveness of our
management policy under two real-life workloads. Experimental results
show that our management policy in comparison with baseline unmanaged
execution saves up to 24% of energy with less than 4% performance
overhead for our real-life workloads.
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