  The evolution of high-performance computing is associated with the growth of
energy consumption. Performance of cluster computes (is increased via rising in
performance and the number of used processors, GPUs, and coprocessors. An
increment in the number of computing elements results in significant growth of
energy consumption. Power grids limits for supercomputer centers (SCC) are
driving the transition to more energy-efficient solutions. Often upgrade of
computing resources is done step-by-step, i.e. parts of older supercomputers
are removed from service and replaced with newer ones. A single SCC at any time
can operate several computing systems with different performance and power
consumption. That is why the problem of scheduling parallel programs execution
on SCC resources to optimize energy consumption and minimize the increase in
execution time (energy-efficient scheduling) is important. The goal of the
presented work was the development of a new energy-efficient algorithm for
scheduling computing resources at SCC. To reach the goal the authors analyzed
methods of scheduling computing resources in a shared facility, including
energy consumption minimizing methods. The study made it possible to formulate
the problem of energy-efficient scheduling for a set of CCs and propose an
algorithm for its solution. Experiments on NPB benchmarks allowed achieving
significant reduction in energy consumption with a minor increase of runtime.
