Resource management strategies with energy profiles for stencil computing

The performance of high-end supercomputers will reach the exascale through the advent of core counts in the billions. However, in the upcoming exascale computing era it is important not only to focus on the performance, but also on scalability of fine-grained parallel applications, data locality and energy aware scheduling within the parallel code. In fact, parallel applications need to change even now by redesigning algorithms and data structures respectively to take advantage of the recent improvements in energy efficiency of heterogeneous computing hardware, including multicore processors and GPU accelerators. Over the next few years one of the biggest challenges for exascale will be the ability of parallel applications to fully exploit locality which will, in turn, be required to achieve expected performance and energy efficiency. Future highly parallel applications will have to deal with deep memory hierarchies taking into account energy cost in moving data off-chip. Therefore, they will have to apply new coordinated scheduling approaches to balance energy aware resource utilization and minimize work starvation during runtime. As new constraints and limits on memory bandwidth and energy will play a key role in High Performance Computing (HPC) in the future, more sophisticated and dynamic scheduling techniques will be needed and applied within the parallel code. In this paper we focus on analysis of execution and performance models for a relevant class of stencil computations to explore the relationship between task scheduling algorithms and energy constraints.