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Towards exascale computing with heterogeneous architectures

The goal of reaching exascale computing is made especially challenging by the highly heterogeneous nature of modern platforms and the energy they consume. As compute nodes typically utilize multiple multi-core CPU and are increasingly equipped with PCIe based accelerators, both are contributing to an ever more dynamic power consumption. In our study we evaluate our target application on a variety of heterogeneous platforms, including high end FPGA, GPU, and Xeon Phi accelerators, with respect to energy efficiency at a node and cluster level. We compare multiple implementations of our application, each built with a different modern parallel programming framework, with respect to execution performance, code complexity and energy efficiency. Later we extrapolate based on our findings, the implications of scaling this application towards exascale, with projections of computation achievable within the exascale power budget for our three architectures.