Testing is one of the most important steps in software development–it ensures the quality of software. Continuous Integration (CI) is a widely used testing standard that can report software quality to the developer in a timely manner during development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. There are many existing profiling and performance tools for HPC applications, but none of these are integrated into CI tools. In this work, we propose BeeSwarm, an HPC container based parallel scaling performance system that can be easily applied to the current CI test environments. BeeSwarm is mainly designed for HPC application developers who need to monitor how their applications can scale on different compute resources. We demonstrate BeeSwarm using three different HPC applications: CoMD, LULESH and NWChem. We utilize GitHub Actions and provision resources from Google Compute Engine. Our results show that BeeSwarm can be used for scalability and performance testing of a variety of HPC applications, allowing developers to monitor application performance over time.

In this paper, we propose a workflow orchestration system that is able to run workflows on both HPC systems and in the cloud using HPC containers. Most existing workflow orchestration systems are only able to run workflows on one system at a time, and thus may be unable to run workflows that require more resources than what some platforms provide, and may also be unable to handle validation and fault tolerance requirements. Users may have access to a number of different systems, perhaps a mix of HPC systems and private and public clouds, but currently are only able to utilize one system at a time for running complex workflows. Utilizing HPC containers, such as Charliecloud, and a subset of the Common Workflow Language (CWL) for representing workflows, we extend the BEE Orchestration System to allow for possible scheduling of complex workflows across resources. We design and implement a scheduling component and a component for interacting with OpenStack-based HPC clusters and Google Compute Engine clouds to allow for communication between any combination of Cloud and HPC components. We demonstrate how BEE orchestrates workflows across systems, making it possible to run complex scientific applications across all systems that are available to a user. These results also show that BEE will become a viable alternative to other workflow orchestration systems.

Testing is one of the most important steps in software development–it ensures the quality of software. Continuous Integration (CI) is a widely used testing standard that can report software quality to the developer in a timely manner during development progress. Performance, especially scalability, is another key factor for High Performance Computing (HPC) applications. There are many existing profiling and performance tools for HPC applications, but none of these are integrated into CI tools. In this work, we propose BeeSwarm, an HPC container based parallel scaling performance system that can be easily applied to the current CI test environments. BeeSwarm is mainly designed for HPC application developers who need to monitor how their applications can scale on different compute resources. We demonstrate BeeSwarm using a multi-physics HPC application with Travis CI, GitLab CI and GitHub Actions while using ChameleonCloud and Google Compute Engine as the compute backends. Our results show that BeeSwarm can be used for scalability and performance testing of HPC applications.

This poster presents an HPC application workflow system whose goal is to provide verifiably-reproducible HPC application performance. This system combines existing container, experiment, and data management techniques with HPC performance models, allowing it to both maximize performance reproducibility and inform users when application performance deviates from what should be expected even when running at scales or for lengths of time at which the application had never run.