Achieving Exascale Capabilities through Heterogeneous Computing

This article provides an overview for AMD's vision for exascale computing, and in particular how heterogeneity will play a central role in realizing this vision. Exascale computing requires very high levels of performance capabilities while staying within very stringent power budgets. 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 to not have to pay for custom components designed only for high-end HPC systems, and therefore high-volume GPU technology becomes a natural choice for energy-efficient data-parallel computing. To make fully realize the capabilities of the GPU, we envision exascale compute nodes comprised of integrated CPUs and GPUs (i.e., accelerated processing units or APUs) along with the hardware and software support to enable scientists to effectively run their scientific experiments on an exascale system. We discuss the hardware and software challenges in building a heterogeneous exascale system, and we describe on-going research efforts at AMD to realize our exascale vision.