ABSTRACT
Containers are seeing widespread use in the world of High Performance Computing, with many HPC Centers either providing their own containerization solution or adopting existing ones like Singularity and Apptainer. The demand for containerization options come from users who want to take advantage of the portability and reproducibility containers can provide, as well as being able to build and use applications that are only distributed in container form or are otherwise unsuited to natively run in an HPC environment. The users served by the Oak Ridge Leadership Computing Facility are no exception. We go over the past and current containerization offerings at the Oak Ridge Leadership Computing Facility, mainly focusing on the Summit supercomputer. We arrive at using a combination of Podman and Singularity to allow users to build and run containers directly on Summit, without requiring external resources or hardware for any step of the process. We look at a couple of projects running on Summit that greatly benefited from being able to use containers on Summit. And we compare benchmarks running natively and in containers on Summit at different scales, observing minimal performance difference and consistent behavior across all tests.
- [n. d.]. Containers on Summit — OLCF User Documentation. https://docs.olcf.ornl.gov/software/containers_on_summit.htmlGoogle Scholar
- [n. d.]. Fakeroot feature — SingularityCE User Guide 3.11 documentation. https://docs.sylabs.io/guides/3.11/user-guide/fakeroot.htmlGoogle Scholar
- [n. d.]. November 2022 | TOP500. https://top500.org/lists/top500/2022/11/Google Scholar
- [n. d.]. Overlay Filesystem — The Linux Kernel documentation. https://www.kernel.org/doc/html/latest/filesystems/overlayfs.htmlGoogle Scholar
- [n. d.]. Overview — NVIDIA Cloud Native documentation. https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/overview.htmlGoogle Scholar
- [n. d.]. SingularityCE and MPI applications — SingularityCE User Guide 3.11 documentation. https://docs.sylabs.io/guides/3.11/user-guide/mpi.htmlGoogle Scholar
- 2023. AlphaFold. https://github.com/deepmind/alphafold original-date: 2021-06-17T14:06:06Z.Google Scholar
- 2023. olcfcontainers / olcfbaseimages · GitLab. https://code.ornl.gov/olcfcontainers/olcfbaseimagesGoogle Scholar
- 2023. open-ce/open-ce. https://github.com/open-ce/open-ce original-date: 2020-09-03T20:23:41Z.Google Scholar
- 2023. Raptor Computing Systems::Talos™ II Secure Workstation. https://www.raptorcs.com/TALOSII/Google Scholar
- M. Gao, M. Coletti, R.B. Davidson, R. Prout, S. Abraham, B. Hernandez, and A. Sedova. 2022. Proteome-scale Deployment of Protein Structure Prediction Workflows on the Summit Supercomputer. 206–215. https://doi.org/10.1109/IPDPSW55747.2022.00045Google Scholar
- M. Gao, P. Lund-Andersen, A. Morehead, S. Mahmud, C. Chen, X. Chen, N. Giri, R.S. Roy, F. Quadir, T.C. Effler, R. Prout, S. Abraham, W. Elwasif, N.Q. Haas, J. Skolnick, J. Cheng, and A. Sedova. 2021. High-Performance Deep Learning Toolbox for Genome-Scale Prediction of Protein Structure and Function. 46–57. https://doi.org/10.1109/MLHPC54614.2021.00010Google Scholar
- John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin Žídek, Anna Potapenko, Alex Bridgland, Clemens Meyer, Simon A. A. Kohl, Andrew J. Ballard, Andrew Cowie, Bernardino Romera-Paredes, Stanislav Nikolov, Rishub Jain, Jonas Adler, Trevor Back, Stig Petersen, David Reiman, Ellen Clancy, Michal Zielinski, Martin Steinegger, Michalina Pacholska, Tamas Berghammer, Sebastian Bodenstein, David Silver, Oriol Vinyals, Andrew W. Senior, Koray Kavukcuoglu, Pushmeet Kohli, and Demis Hassabis. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596, 7873 (Aug. 2021), 583–589. https://doi.org/10.1038/s41586-021-03819-2 Number: 7873 Publisher: Nature Publishing Group.Google Scholar
- Gregory M Kurtzer, Vanessa Sochat, and Michael W Bauer. 2017. Singularity: Scientific containers for mobility of compute. PloS one 12, 5 (2017), e0177459.Google ScholarCross Ref
- Oleksandr Rudyy, Marta Garcia-Gasulla, Filippo Mantovani, Alfonso Santiago, Raül Sirvent, and Mariano Vázquez. 2019. Containers in HPC: A Scalability and Portability Study in Production Biological Simulations. In Proceedings of 2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS).Google ScholarCross Ref
- Adam Simpson, Jack Morrison, Adam Carlyle, and Matt Belhorn. 2023. olcf / container-builder (GitLab). https://code.ornl.gov/olcf/container-builderGoogle Scholar
- A. Torrez, T. Randles, and R. Priedhorsky. 2019. HPC Container Runtimes have Minimal or No Performance Impact. In 2019 IEEE/ACM International Workshop on Containers and New Orchestration Paradigms for Isolated Environments in HPC (CANOPIE-HPC). 37–42.Google Scholar
- Han Wang, Linfeng Zhang, Jiequn Han, and Weinan E. 2018. DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics. https://doi.org/10.1016/j.cpc.2018.03.016 Pages: 178–184 Publication Title: Computer Physics Communications Volume: 228 original-date: 2017-12-12T15:23:44Z.Google Scholar
- Andrew J Younge, Kevin Pedretti, Ryan E Grant, and Ron Brightwell. 2017. A Tale of Two Systems: Using Containers to Deploy HPC Applications on Supercomputers and Clouds. In Proceedings of the 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom).Google ScholarCross Ref
Index Terms
- The HPC Container Experience on the Summit Supercomputer
Recommendations
Traditional High-Performance Computing with Container Technology (THPC): HPC using Container Technology, EasyBuild, Spack, and IBM LSF scheduler
PEARC '23: Practice and Experience in Advanced Research ComputingHigh-performance Computing (HPC) has been around for decades but maintaining software for HPC with heterogeneous compute nodes remains a challenging task for system administrators. Many software package frameworks have been developed over the years to ...
Survey of adaptive containerization architectures for HPC
SC-W '23: Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, Network, Storage, and AnalysisContainers offer an array of advantages that benefit research reproducibility and portability. As container tools mature, container security improves, and high-performance computing (HPC) and cloud system tools converge, supercomputing centers are ...
New capabilities in qoscosgrid middleware for advanced job management, advance reservation and co-allocation of computing resources --- quantum chemistry application use case
Building a National Distributed e-Infrastructure - PL-GridIn this chapter we present the new capabilities of QosCosGrid (QCG) middleware for advanced job and resource management in the grid environment. By connecting many computing clusters together, QosCosGrid offers easy-to-use mapping, execution and ...
Comments