Cooling-aware node-level task allocation for next-generation green HPC systems

Energy-efficiency is of primary interest in future HPC systems as their computational growth is limited by the supercomputer peak power consumption. A significant part of the power consumed by a supercomputer machine is caused by the cooling infrastructure. Todays thermal design is based on coarse grain models which consider the silicon die of the processing elements as an isothermal surface. Similarly feedback control loops uses the same assumption to modulate the cooling effort with the goal of reducing cooling cost and maintaining the silicon temperature in a safe working range. Recent processors development has brought into the market CPUs that integrate a large number of complex cores. Differently from massively parallel CPUs for which the area and power consumption of each core is very limited, the cores of these processors can consume tens of watts and thus, under heterogeneous workloads, creating significant thermal gradients. In this paper we first characterize the power and thermal characteristics of new server-class Intel Xeon computing node based on Haswell v3 architecture considering both the computational and the cooling components. We show that these systems are characterized by significant on-die thermal gradients and that the current O.S. Task allocation strategy is not capable of taking advantage of that, leading to max CPU temperature and extra cooling activity. To solve this issue we propose a novel task allocation strategy that reduces the cooling power while matching the HPC performance requirements.