In-situ data analytics for highly scalable cloud modelling on Cray machines

TL;DR

This paper presents an in-situ data analytics approach for highly scalable cloud modeling on Cray machines, enabling continuous analysis without halting simulations, demonstrated on up to 32768 cores with minimal performance impact.

Contribution

It introduces a highly asynchronous in-situ analytics framework integrated into MONC, optimizing performance and scalability on large-scale Cray supercomputers.

Findings

Minimal performance impact on runtime with analytics enabled

Effective asynchronous data transfer and analysis workflow

Scalability demonstrated on up to 32768 cores

Abstract

MONC is a highly scalable modelling tool for the investigation of atmospheric flows, turbulence and cloud microphysics. Typical simulations produce very large amounts of raw data which must then be analysed for scientific investigation. For performance and scalability reasons this analysis and subsequent writing to disk should be performed in-situ on the data as it is generated however one does not wish to pause the computation whilst analysis is carried out. In this paper we present the analytics approach of MONC, where cores of a node are shared between computation and data analytics. By asynchronously sending their data to an analytics core, the computational cores can run continuously without having to pause for data writing or analysis. We describe our IO server framework and analytics workflow, which is highly asynchronous, along with solutions to challenges that this approach raises and the performance implications of some common configuration choices. The result of this work is a highly scalable analytics approach and we illustrate on up to 32768 computational cores of a Cray XC30 that there is minimal performance impact on the runtime when enabling data analytics in MONC and also investigate the performance and suitability of our approach on the KNL.