Vardas, I., Hunold, S., SWARTVAGHER, P., & Träff, J. L. (2024). Improved Parallel Application Performance and Makespan by Colocation and Topology-aware Process Mapping. In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 119–124). IEEE. https://doi.org/10.1109/CCGrid59990.2024.00023 ( reposiTUm)

Laso Rodriguez, R., Krupitza, D., & Hunold, S. (2024). Exploring Scalability in C++ Parallel STL Implementations. In ICPP ’24: Proceedings of the 53rd International Conference on Parallel Processing (pp. 284–293). ACM. https://doi.org/10.1145/3673038.3673065 ( reposiTUm)

Salimibeni, M., Cosenza, B., & Hunold, S. (2024). MPI Collective Algorithm Selection in the Presence of Process Arrival Patterns. In Proceedings : 2024 IEEE International Conference  on Cluster Computing : 24 – 27 September 2024  Kobe, Japan (pp. 108–119). https://doi.org/10.1109/CLUSTER59578.2024.00017 ( reposiTUm)

Hunold, S. (2023). Verifying Performance Guidelines for MPI Collectives at Scale. In Proceedings of 2023 SC23 Workshops of The International Conference on High Performance Computing, Network, Storage, and Analysis (SC23 Workshops) (pp. 1264–1268). ACM. https://doi.org/10.1145/3624062.3625532 ( reposiTUm)

Hunold, S., & Steiner, S. (2023). OMPICollTune: Autotuning MPI Collectives by Incremental Online Learning. In Proceedings of PMBS 2022: performance modeling, benchmarking and simulation of high performance computer systems (pp. 123–128). IEEE. https://doi.org/10.1109/PMBS56514.2022.00016 ( reposiTUm)

Hunold, S., & Kraßnitzer, K. D. V. (2023). A Quantitative Analysis of OpenMP Task Runtime Systems. In A. Gainaru, C. Zhang, & C. Luo (Eds.), Benchmarking, Measuring, and Optimizing : 14th BenchCouncil International Symposium, Bench 2022, Virtual Event, November 7-9, 2022, Revised Selected Papers (pp. 3–18). Springer. https://doi.org/10.1007/978-3-031-31180-2_1 ( reposiTUm)

Schuchart, J., Hunold, S., & Bosilca, G. (2023). Synchronizing MPI Processes in Space and Time. In EuroMPI “23: Proceedings of the 30th European MPI Users” Group Meeting (pp. 1–11). ACM. https://doi.org/10.1145/3615318.3615325 ( reposiTUm)

Hunold, S., & Hagn, M. (2023). MPI is Good, Control is Better: Checking Performance Guidelines of Collectives. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2023 - ASHPC23 (pp. 60–60). EuroCC Austria. https://doi.org/10.34726/5367 ( reposiTUm)

Vardas, I., Hunold, S., Ajanohoun, J. I., & Traff, J. L. (2022). mpisee: MPI Profiling for Communication and Communicator Structure. In 2022 IEEE 36th International Parallel and Distributed Processing Symposium Workshops (IPDPSW 2022) (pp. 520–529). IEEE. https://doi.org/10.1109/IPDPSW55747.2022.00092 ( reposiTUm)

Hunold, S., Ajanohoun, J. I., Vardas, I., & Träff, J. L. (2022). An Overhead Analysis of MPI Profiling and Tracing Tools. In C. Scully-Allison, R. Liem, & A. V. Solorzano (Eds.), PERMAVOST 2022: Proceedings of the 2nd Workshop on Performance Engineering, Modelling, Analysis, and Visualization Strategy (pp. 5–13). Association for Computing Machinery (ACM). https://doi.org/10.1145/3526063.3535353 ( reposiTUm)

Hunold, S., Ajanohoun, J. I., & Carpen-Amarie, A. (2021). MicroBench Maker: Reproduce, Reuse, Improve. In 2021 International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). 12th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS 2021) in conjunction with SC 2021, St. Louis, Missouri, United States of America (the). IEEE. https://doi.org/10.1109/pmbs54543.2021.00013 ( reposiTUm)

Hunold, S. (2022). Performance Tuning of MPI Collectives - Status Quo and Open Problems [Presentation]. CaSToRC HPC National Competence Center Fall Seminar Series 2022, Unknown. http://hdl.handle.net/20.500.12708/153709 ( reposiTUm)