Volume 6 (2), December 2023, Pages 203-208

Elviz Ismayilov

Azerbaijan State Oil and Industry University, Baku, Azerbaijan, This email address is being protected from spambots. You need JavaScript enabled to view it.


The rapidly developing field of high-performance computing (HPC) requires efficient and scalable solutions to manage extensive computing loads. Although they use different approaches and architectures, OpenHPC and HTCondor are well-known platforms that meet these needs. This article thoroughly analyzes OpenHPC and HTCondor to identify their fundamental differences and work paradigms. OpenHPC is a comprehensive modular structure designed to facilitate the deployment, management, and maintenance of HPC clusters, offering a rich set of pre-integrated HPC software components. Conversely, HTCondor specializes in efficiently planning and managing resource-intensive tasks, using a unique partner selection system for dynamic resource allocation based on job requirements and resource availability. By examining aspects such as system architecture, resource management efficiency, scalability, flexibility, and the user ecosystem, this analysis sheds light on the strengths and weaknesses of each structure. The research aims to provide stakeholders in high-performance computing with the knowledge necessary to make informed decisions regarding the selection and implementation of high-performance computing management systems, ultimately aimed at optimizing the use of computing resources and optimizing research and development workflows.


OpenHPC, HTCondor, HPC Clusters, High Performance Computing.

DOI: https://doi.org/10.32010/26166127.2023.





Schulz, K. W., et al. (2016, November). Cluster computing with OpenHPC. In Inaugural HPC Systems Professionals Workshop, HPCSYSPROS16 (pp. 1-6).

Simmons, C., Schulz, K., & Simmel, D. (2020, July). Customizing OpenHPC: integrating additional software and provisioning new services, including open on-demand. In Proceedings of the Conference on Practice and Experience in Advanced Research Computing (p. 1).

Orejuela, V., Ramirez, Á. S., Toro, A. F., Gonzalez, A. F., & Briñez, D. (2018). Application for computational cluster performance tests configured in HTCONDOR. In MATEC Web of Conferences (Vol. 210, p. 04029). EDP Sciences.

Kalayci, S., Dasgupta, G., Fong, L., Ezenwoye, O., & Sadjadi, S. M. (2010, December). Distributed and Adaptive Execution of Condor DAGMan Workflows. In SEKE (pp. 587-590).

Hollowell, C., Barnett, J., Caramarcu, C., Strecker-Kellogg, W., Wong, A., & Zaytsev, A. (2017, October). Mixing HTC and HPC workloads with HTCondor and slurm. In Journal of Physics: Conference Series (Vol. 898, No. 8, p. 082014). IOP Publishing.

Gavrilovska, A., et al. (2007, March). High-performance hypervisor architectures: Virtualization in hpc systems. In Workshop on system-level virtualization for HPC (HPCVirt).

Diwan, S. M. (1999). Open HPC++: An open programming environment for high-performance distributed applications. Indiana University.

Bockelman, B., Livny, M., Lin, B., & Prelz, F. (2021). Principles, technologies, and time: The translational journey of the HTCondor-CE. Journal of Computational Science, 52, 101213.

Yang, Y. (2007). A fault tolerance protocol for stateless parallel processing. Temple University. [Ph.D thesis, AAI3293270].