Beyond Exascale: Dataflow Domain Translation on a Cerebras Cluster

TL;DR

This paper presents the Domain Translation algorithm that significantly enhances simulation performance on Cerebras clusters, achieving unprecedented throughput and efficiency for physical system modeling at exascale levels.

Contribution

The paper introduces a novel Domain Translation algorithm that improves simulation rates and scaling efficiency on Cerebras clusters, surpassing traditional domain decomposition methods.

Findings

Simulations exceeding 1.6 million time steps per second

Perfect weak scaling at 88% of peak performance

112 PFLOP/s in a power-unconstrained environment

Abstract

Simulation of physical systems is essential across scientific and engineering domains. Commonly used domain decomposition methods are unable to simultaneously deliver both high simulation rate and high utilization in network computing environments. In particular, Exascale systems deliver only a small fraction their peak performance for these workloads. This paper introduces the novel Domain Translation algorithm, designed to overcome these limitations. On a cluster of 64 Cerebras CS-3 systems, we use this method to demonstrate unprecedented cluster performance across a range of metrics: we show simulations running in excess of 1.6 million time steps per second; we also demonstrate perfect weak scaling at 88% of peak performance. At this cluster scale, our implementation provides 112 PFLOP/s in a power-unconstrained environment, and 57 GFLOP/J in a power-limited environment. We illustrate the method by applying the shallow-water equations to model a tsunami following an asteroid impact at 460m-resolution on a planetary scale.