Characterizing Containerized HPC Applications Performance at Petascale on CPU and GPU Architectures

Abstract

Containerization technologies provide a mechanism to encapsulate applications and many of their dependencies, facilitating software portability and reproducibility on HPC systems. However, in order to access many of the architectural features that enable HPC system performance, compatibility between certain components of the container and host is required, resulting in a trade-off between portability and performance. In this work, we discuss our experiences running three state-of-the-art containerization technologies on five leading petascale systems. We present how we build the containers to ensure performance and security and their performance at scale. We ran microbenchmarks at a scale of 6,144 nodes containing 0.35 M MPI processes and baseline the performance of container technologies. We establish the near-native performance and minimal memory overheads by the containerized environments using MILC - a lattice quantum chromodynamics code at 139,968 processes and using VPIC - a 3d electromagnetic relativistic Vector Particle-In-Cell code for modeling kinetic plasmas at 32,768 processes. We demonstrate an on-par performance trend at a large scale on Intel, AMD, and three NVIDIA architectures for both HPC applications.KeywordsPetascaleHPCContainerizationCloud computingSingularityCharliecloudPodmanMILCVPIC