Containers in HPC: A Scalability and Portability Study in Production Biological Simulations

Abstract

Since the appearance of Docker in 2013, container technologies for computers have evolved and gained importance in cloud data centers. However, adoption of containers in High-Performance Computing (HPC) centers is still under discussion: on one hand, the ease in portability is very well accepted; on the other hand, the performance penalties and security issues introduced by the added software layers are often under scrutiny. Since very little evaluation of large production HPC codes running in containers is available, we provide in this paper a comparative study using a production simulation of a biological system. The simulation is performed using Alya, which is a computational fluid dynamics (CFD) code optimized for HPC environments and enabled to run multiphysics problems. In the paper, we analyze the productivity advantages of adopting containers for large HPC codes, and we quantify performance overhead induced by the use of three different container technologies (Docker, Singularity and Shifter) comparing it to native execution. Given the results of these tests, we selected Singularity as best technology, based on performance and portability. We show scalability results of Alya using singularity up to 256 computational nodes (up to 12k cores) of MareNostrum4 and present a study of performance and portability on three different HPC architectures (Intel Skylake, IBM Power9, and Arm-v8).