  Power consumption is a critical consideration in high performance computing
systems and it is becoming the limiting factor to build and operate Petascale
and Exascale systems. When studying the power consumption of existing systems
running HPC workloads, we find that power, energy and performance are closely
related which leads to the possibility to optimize energy consumption without
sacrificing (much or at all) the performance. In this paper, we propose a HPC
system running with a GNU/Linux OS and a Real Time Resource Manager (RTRM) that
is aware and monitors the healthy of the platform. On the system, an
application for disaster management runs. The application can run with
different QoS depending on the situation. We defined two main situations.
Normal execution, when there is no risk of a disaster, even though we still
have to run the system to look ahead in the near future if the situation
changes suddenly. In the second scenario, the possibilities for a disaster are
very high. Then the allocation of more resources for improving the precision
and the human decision has to be taken into account. The paper shows that at
design time, it is possible to describe different optimal points that are going
to be used at runtime by the RTOS with the application. This environment helps
to the system that must run 24/7 in saving energy with the trade-off of losing
precision. The paper shows a model execution which can improve the precision of
results by 65% in average by increasing the number of iterations from 1e3 to
1e4. This also produces one order of magnitude longer execution time which
leads to the need to use a multi-node solution. The optimal trade-off between
precision vs. execution time is computed by the RTOS with the time overhead
less than 10% against a native execution.
