PREDICTING THE PERFORMANCE OF CONTAINERIZED HIGH-PERFORMANCE COMPUTING APPLICATIONS UNDER DIFFERENT INTERFERENCE CONDITIONS

Abstract

HPC services provide users with a variety of configurations. Differences between these configurations usually depend on the amount of memory and cores or the type of hardware that is provided to the user. It is up to the HPC user to decide what configuration to choose before running their application. This creates a problem especially when users have little knowledge of their applications’ needs or limited information about how their application would perform on different configurations. To address this issue, we propose a tool that predicts the performance of HPC applications on a variety of resource configurations. Our tool profiles a given application on a select number of configurations and uses the profiling information to predict the performance of the application for other configurations. Moreover, our tool accounts for the effect of interference from other instances/applications on the performance of the application in question. In this work, we focus on HPC applications and use containers for configuring resource utilization, however, our methods may be generalized to other contexts. We aim to show that the performance of HPC applications across various resource configurations in the presence of interference can be predicted with reasonable accuracy.