Achieving Exascale Capabilities through Heterogeneous Computing

Abstract

This article provides an overview of AMD's vision for exascale computing, and in particular, how heterogeneity will play a central role in realizing this vision. Exascale computing requires high levels of performance capabilities while staying within stringent power budgets. Using hardware optimized for specific functions is much more energy efficient than implementing those functions with general-purpose cores. However, there is a strong desire for supercomputer customers not to have to pay for custom components designed only for high-end high-performance computing systems. Therefore, high-volume GPU technology becomes a natural choice for energy-efficient data-parallel computing. To fully realize the GPU's capabilities, the authors envision exascale computing nodes that compose integrated CPUs and GPUs (that is, accelerated processing units), along with the hardware and software support to enable scientists to effectively run their scientific experiments on an exascale system. The authors discuss the hardware and software challenges in building a heterogeneous exascale system and describe ongoing research efforts at AMD to realize their exascale vision.