Achieving Exascale Capabilities through Heterogeneous Computing

TLDR

Exascale computing demands high performance within tight power budgets, and energy‑efficient specialized hardware—particularly high‑volume GPUs—offers a cost‑effective alternative to custom components. The article outlines AMD’s vision that exascale systems will rely on heterogeneous nodes combining integrated CPUs and GPUs, supported by hardware and software to enable scientists to run experiments at exascale. AMD proposes building exascale nodes that integrate CPUs and GPUs—accelerated processing units—alongside hardware and software support to harness their full capabilities. The authors identify key hardware and software challenges and highlight ongoing AMD research aimed at realizing this heterogeneous exascale vision.

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.