Wissenschaftliche Artikel

Träff, J. L., Hunold, S., Mercier, G., & Holmes, D. J. (2021). MPI collective communication through a single set of interfaces: A case for orthogonality. Parallel Computing: Systems & Applications, 107(102826), 102826. https://doi.org/10.1016/j.parco.2021.102826 ( reposiTUm)
Kainrad, T., Hunold, S., Seidel, T., & Langer, T. (2019). LigandScout Remote: A New User-Friendly Interface for HPC and Cloud Resources. Journal of Chemical Information and Modeling, 59(1), 31–37. https://doi.org/10.1021/acs.jcim.8b00716 ( reposiTUm)
Bleuse, R., Hunold, S., Kedad-Sidhoum, S., Monna, F., Mounie, G., & Trystram, D. (2017). Scheduling Independent Moldable Tasks on Multi-Cores with GPUs. IEEE Transactions on Parallel and Distributed Systems, 28(9), 2689–2702. https://doi.org/10.1109/tpds.2017.2675891 ( reposiTUm)
Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2017). On expected and observed communication performance with MPI derived datatypes. Parallel Computing: Systems & Applications, 69, 98–117. https://doi.org/10.1016/j.parco.2017.08.006 ( reposiTUm)
Hunold, S. (2015). One Step towards Bridging the Gap between Theory and Practice in Moldable Task Scheduling with Precedence Constraints. Concurrency and Computation: Practice and Experience, 27(4), 1010–1026. http://hdl.handle.net/20.500.12708/150641 ( reposiTUm)
Bertin, R., Hunold, S., Legrand, A., & Touati, C. (2013). Fair scheduling of bag-of-tasks applications using distributed Lagrangian optimization. Journal of Parallel and Distributed Computing, 74(1), 1914–1929. https://doi.org/10.1016/j.jpdc.2013.08.011 ( reposiTUm)

Beiträge in Tagungsbänden

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)
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)
Träff, J. L., Hunold, S., Vardas, I., & Funk, N. M. (2023). Uniform Algorithms for Reduce-scatter and (most) other Collectives for MPI. In 2023 IEEE International Conference on Cluster Computing (CLUSTER) (pp. 284–294). IEEE. https://doi.org/10.1109/CLUSTER52292.2023.00031 ( 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)
Hunold, S., Vardas, I., Ibis, G., & Langer, T. (2023). Massively Scaling Molecular Screening Workloads on EuroHPC Supercomputers. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2023 - ASHPC23 (pp. 51–51). EuroCC Austria. https://doi.org/10.34726/5366 ( 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)
Swartvagher, P., Vardas, I., Hunold, S., & Träff, J. L. (2023). Rank Reordering within MPI Communicators to Exploit Deep Hierarchal Architectures of Supercomputers. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2023 - ASHPC23 (pp. 61–61). EuroCC Austria. https://doi.org/10.34726/5368 ( reposiTUm)
Swartvagher, P., Hunold, S., Träff, J. L., & Vardas, I. (2023). Using Mixed-Radix Decomposition to Enumerate Computational Resources of Deeply Hierarchical Architectures. In Proceedings of 2023 SC23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analysis (SC 2023 Workshops) (pp. 405–415). ACM. https://doi.org/10.1145/3624062.3624109 ( reposiTUm)
Vardas, I., Hunold, S., Swartvagher, P., & Träff, J. L. (2023). Effects of Mapping Strategies on Average Duration and Throughput of Colocated HPC Applications. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2023 - ASHPC23 (pp. 10–10). EuroCC Austria. https://doi.org/10.34726/5330 ( 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)
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)
Ajanohoun, J. I., Vardas, I., Träff, J. L., & Hunold, S. (2022). MPI Performance Tools under the Microscope: A Thorough Overhead Analysis. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2022 - ASHPC22 (p. 16). EuroCC Austria. http://hdl.handle.net/20.500.12708/55697 ( reposiTUm)
Vardas, I., Hunold, S., Ajanohoun, J. I., & Träff, J. L. (2022). mpisee: MPI Profiling for Communication and Communicator Structure. In E. Reiter (Ed.), Austrian-Slovenian HPC Meeting 2022 - ASHPC22 (p. 15). EuroCC Austria. http://hdl.handle.net/20.500.12708/55696 ( 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., & Przybylski, B. (2021). Teaching Complex Scheduling Algorithms. In 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 11th NSF/TCPP Workshop on Parallel and Distributed Computing Education (EduPar 2021) in conjunction with 35th IEEE IPDPS 2021 - Online Conference, Portland, Oregon, USA, United States of America (the). IEEE. https://doi.org/10.1109/ipdpsw52791.2021.00058 ( 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)
Träff, J. L., Hunold, S., Mercier, G., & Holmes, D. J. (2020). Collectives and Communicators: A Case for Orthogonality. In 27th European MPI Users’ Group Meeting. 27th European MPI Users’ Group Meeting (EuroMPI/USA 2020) - Online Conference, Austin, Texas, USA, Non-EU. IEEE. https://doi.org/10.1145/3416315.3416319 ( reposiTUm)
Hunold, S., Bhatele, A., Bosilca, G., & Knees, P. (2020). Predicting MPI Collective Communication Performance Using Machine Learning. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (IEEE Cluster 2020) - Online Conference, Kobe, Japan, Non-EU. IEEE. https://doi.org/10.1109/cluster49012.2020.00036 ( reposiTUm)
Träff, J. L., & Hunold, S. (2020). Decomposing MPI Collectives for Exploiting Multi-lane Communication. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (IEEE Cluster 2020) - Online Conference, Kobe, Japan, Non-EU. IEEE. https://doi.org/10.1109/cluster49012.2020.00037 ( reposiTUm)
von Kirchbach, K., Lehr, M., Hunold, S., Schulz, C., & Träff, J. L. (2020). Efficient Process-to-Node Mapping Algorithms for Stencil Computations. In 2020 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (IEEE Cluster 2020) - Online Conference, Kobe, Japan, Non-EU. IEEE. https://doi.org/10.1109/cluster49012.2020.00011 ( reposiTUm)
Träff, J. L., & Hunold, S. (2019). Cartesian Collective Communication. In Proceedings of the 48th International Conference on Parallel Processing. 48th International Conference on Parallel Processing (ICPP 2019), Kyoto, Japan, Non-EU. ACM. https://doi.org/10.1145/3337821.3337848 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2019). On the Importance of Data Quality when Tuning MPI Libraries. In G. Haase (Ed.), Austrian HPC Meeting 2019 - AHPC19 (AHPC19 booklet of abstracts) (p. 15). Institut für Mathematik und wissenschaftliches Rechnen der Universität Graz. http://hdl.handle.net/20.500.12708/57798 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2018). Autotuning MPI Collectives using Performance Guidelines. In Proceedings of the International Conference on High Performance Computing in Asia-Pacific Region. International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2018), Tokyo, Japan, Non-EU. ACM. https://doi.org/10.1145/3149457.3149461 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2018). Hierarchical Clock Synchronization in MPI. In 2018 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing, CLUSTER 2018, Belfast, United Kingdom, EU. IEEE. https://doi.org/10.1109/cluster.2018.00050 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2018). Algorithm Selection of MPI Collectives Using Machine Learning Techniques. In 2018 IEEE/ACM Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS). 9th IEEE International Workshop on Performance Modeling, Benchmarking and Simulation of High Performance Computer Systems (PMBS 2018) in conjunction with SC 2018, Dallas, Texas, USA, Non-EU. IEEE. https://doi.org/10.1109/pmbs.2018.8641622 ( reposiTUm)
Hunold, S., Legrand, A., & Nussbaum, L. (2017). Introduction to REPPAR Workshop. In 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). IEEE. https://doi.org/10.1109/ipdpsw.2017.221 ( reposiTUm)
Heinrich, F. C., Cornebize, T., Degomme, A., Legrand, A., Carpen-Amarie, A., Hunold, S., Orgerie, A.-C., & Quinson, M. (2017). Predicting the Energy-Consumption of MPI Applications at Scale Using Only a Single Node. In 2017 IEEE International Conference on Cluster Computing (CLUSTER). IEEE International Conference on Cluster Computing (CLUSTER 2017), Honolulu, Hawaii, USA, Non-EU. IEEE. https://doi.org/10.1109/cluster.2017.66 ( reposiTUm)
Hunold, S., Carpen-Amarie, A., Lübbe, F. D., & Träff, J. L. (2016). Automatic Verification of Self-consistent MPI Performance Guidelines. In P.-F. Dutot & D. Trystram (Eds.), Euro-Par 2016: Parallel Processing (pp. 433–446). Springer International Publishing. https://doi.org/10.1007/978-3-319-43659-3_32 ( reposiTUm)
Hunold, S., Carpen-Amarie, A., & Träff, J. L. (2016). The art of benchmarking MPI libraries. In I. Reichl, C. Blaas-Schenner, & J. Zabloudil (Eds.), Austrian HPC Meeting 2016 - AHPC 2016 (p. 45). Vienna Scientific Cluster (VSC). http://hdl.handle.net/20.500.12708/56921 ( reposiTUm)
Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2016). On the Expected and Observed Communication Performance with MPI Derived Datatypes. In D. Holmes, A. Collis, J. L. Träff, & L. Smith (Eds.), Proceedings of the 23rd European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2966884.2966905 ( reposiTUm)
Träff, J. L., Lübbe, F. D., Rougier, A., & Hunold, S. (2015). Isomorphic, Sparse MPI-like Collective Communication Operations for Parallel Stencil Computations. In J. Dongarra, A. Denis, B. Goglin, E. Jeannot, & G. Mercier (Eds.), Proceedings of the 22nd European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2802658.2802663 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2015). On the Impact of Synchronizing Clocks and Processes on Benchmarking MPI Collectives. In J. Dongarra, A. Denis, B. Goglin, E. Jeannot, & G. Mercier (Eds.), Proceedings of the 22nd European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2802658.2802662 ( reposiTUm)
Hunold, S., Carpen-Amarie, A., & Träff, J. L. (2014). Reproducible MPI Micro-Benchmarking Isn’t As Easy As You Think. In J. Dongarra, Y. Ishikawa, & A. Hori (Eds.), Proceedings of the 21st European MPI Users’ Group Meeting. ACM. https://doi.org/10.1145/2642769.2642785 ( reposiTUm)
Hunold, S. (2014). Scheduling Moldable Tasks with Precedence Constraints and Arbitrary Speedup Functions on Multiprocessors. In R. Wyrzykowski, J. Dongarra, K. Karczewski, & J. Wasniewski (Eds.), Parallel Processing and Applied Mathematics (pp. 13–25). Springer. https://doi.org/10.1007/978-3-642-55195-6_2 ( reposiTUm)
Träff, J. L., Rougier, A., & Hunold, S. (2014). Implementing a classic. In M. Gerndt, P. Stenström, L. Rauchwerger, B. Miller, & M. Schulz (Eds.), Proceedings of the 28th ACM international conference on Supercomputing - ICS ’14. ACM. https://doi.org/10.1145/2597652.2597662 ( reposiTUm)

Tagungsbände

Hunold, S., Xie, B., & Shu, K. (Eds.). (2024). Benchmarking, Measuring, and Optimizing : 15th BenchCouncil International Symposium, Bench 2023, Revised Selected Papers (Vol. 14521). Springer Singapore. https://doi.org/10.1007/978-981-97-0316-6 ( reposiTUm)
Desprez, F., Dutot, P.-F., Kaklamanis, C., Marchal, L., Molitorisz, K., Ricci, L., Scarano, V., Vega-Rodriguez, M. A., Varbanescu, A. L., Hunold, S., Scott, S. L., Lankes, S., & Weidendorfer, J. (Eds.). (2017). Euro-Par 2016: Parallel Processing Workshops. Springer Nature Switzerland AG 2021. https://doi.org/10.1007/978-3-319-58943-5 ( reposiTUm)
Euro-Par 2015: Parallel Processing. (2015). In J. L. Träff, S. Hunold, & F. Versaci (Eds.), Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-662-48096-0 ( reposiTUm)
Euro-Par 2015: Parallel Processing Workshops. (2015). In S. Hunold, A. Costan, D. Gimenez, A. Iosup, L. Ricci, M. E. Gomez Requena, V. Scarano, A. L. Varbanescu, S. L. Scott, S. Lankes, J. Weidendorfer, & M. Alexander (Eds.), Lecture Notes in Computer Science. Springer International Publishing. https://doi.org/10.1007/978-3-319-27308-2 ( reposiTUm)
Lopes, L., Zilinskas, J., Costan, A., Cascella, R. G., Kecskemeti, G., Jeannot, E., Cannataro, M., Ricci, L., Benkner, S., Petit, S., Scarano, V., Gracia, J., Hunold, S., Scott, S. L., Lankes, S., Lengauer, C., Carretero, J., Breitbart, J., & Alexander, M. (Eds.). (2014). Euro-Par 2014: Parallel Processing Workshops. Springer. https://doi.org/10.1007/978-3-319-14313-2 ( reposiTUm)
Lopes, L., Zilinskas, J., Costan, A., Cascella, R. G., Kecskemeti, G., Jeannot, E., Cannataro, M., Ricci, L., Benkner, S., Petit, S., Scarano, V., Gracia, J., Hunold, S., Scott, S. L., Lankes, S., Lengauer, C., Carretero, J., Breitbart, J., & Alexander, M. (Eds.). (2014). Euro-Par 2014: Parallel Processing Workshops. Springer. https://doi.org/10.1007/978-3-319-14325-5 ( reposiTUm)

Präsentationen

Hunold, S. (2023, December 8). Unveiling the Complexities of Performance Analysis and Optimization in HPC Systems [Presentation]. Universität Münster, Münster, Germany. ( reposiTUm)
Hunold, S., & Przybylski, B. (2022, May 18). Scheduling.jl - Collaborative and Reproducible Scheduling Research with Julia [Conference Presentation]. New Challenges in Scheduling Theory (Centre CNRS “Paul-Langevin”, Aussois, France), Aussois, France. http://hdl.handle.net/20.500.12708/153814 ( 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)
Hunold, S., & Carpen-Amarie, A. (2017). Autotuning MPI Collectives using Performance Guidelines. LIG - Bâtiment IMAG, St Martin d’Hères, France, EU. http://hdl.handle.net/20.500.12708/86599 ( reposiTUm)
Hunold, S. (2016). The art of benchmarking MPI libraries. Austrian HPC Meeting 2016 - AHPC16, Grundlsee, Austria, Austria. http://hdl.handle.net/20.500.12708/86269 ( reposiTUm)
Hunold, S. (2016). Clock Synchronization Algorithms and SimGrid. SimGrid User Days, CNRS center Villa Clythia, Fréjus, France, EU. http://hdl.handle.net/20.500.12708/86260 ( reposiTUm)
Hunold, S. (2016). The Art of MPI Benchmarking. 45th SPEEDUP Workshop on High-Performance Computing, Basel, Switzerland, Non-EU. http://hdl.handle.net/20.500.12708/86310 ( reposiTUm)
Hunold, S. (2016). The Art of MPI Benchmarking. Lunchtime Seminar, Department of Computer Science, University of Innsbruck, Innsbruck, Austria, Austria. http://hdl.handle.net/20.500.12708/86282 ( reposiTUm)
Hunold, S. (2015). Reproducibility in Parallel Computing. Session: Performance Reproducibility in HPC - Challenges and State-of-the-Art at the 27th International Conference for High Performance Computing, Networking, Storage and Analysis (SC 2015), Austin, Texas, Non-EU. http://hdl.handle.net/20.500.12708/86091 ( reposiTUm)
Hunold, S. (2015). One Step towards Bridging the Gap between Theory and Practice in Moldable Task Scheduling with Precedence Constraints. Wirtschaftswissenschaftliche Fakultät, Universität Augsburg, Augsburg, Deutschland, EU. http://hdl.handle.net/20.500.12708/86038 ( reposiTUm)
Hunold, S. (2015). Accurately Measuring MPI Collectives with Synchronized Clocks. Dagstuhl Seminar 15281: Algorithms and Scheduling Techniques to Manage Resilience and Power Consumption in Distributed Systems, Schloss Dagstuhl, Wadern, Germany, EU. http://hdl.handle.net/20.500.12708/86057 ( reposiTUm)
Hunold, S. (2014). Reproducibility of Experiments: It’s about the WHO and less the HOW. Panel on reproducible research methodologies and new publication models, 4th International Workshop on Adaptive Self-tuning Computing Systems (ADAPT 2014) co-located with HiPEAC 2014, Vienna, Austria, Austria. http://hdl.handle.net/20.500.12708/85814 ( reposiTUm)
Hunold, S. (2014). Moldable Task Scheduling: Theory and Practice. Workshop on New Challenges in Scheduling Theory, Aussois, France, EU. http://hdl.handle.net/20.500.12708/85817 ( reposiTUm)
Hunold, S. (2014). One Step towards Bridging the Gap between Theory and Practice in Moldable Task Scheduling with Precedence Constraints. 9th Scheduling for Large Scale Systems Workshop, Lyon, France, EU. http://hdl.handle.net/20.500.12708/85812 ( reposiTUm)
Hunold, S., Carpen-Amarie, A., & Träff, J. L. (2014). Reproducible MPI Micro-Benchmarking Isn’t As Easy As You Think. Research Group Theory and Applications of Algorithms, University of Vienna, Vienna, Austria, Austria. http://hdl.handle.net/20.500.12708/85872 ( reposiTUm)
Hunold, S. (2014). One Step towards Bridging the Gap between Theory and Practice in Moldable Task Scheduling with Precedence Constraints. AIT Austrian Institute of Technology, Seibersdorf, Austria, Austria. http://hdl.handle.net/20.500.12708/85871 ( reposiTUm)
Hunold, S. (2013). On the Scalability of Moldable Task Scheduling Algorithms. Dagstuhl Seminar 13381: Algorithms and Scheduling Techniques for Exascale Systems, Schloss Dagstuhl, Wadern, Germany, EU. http://hdl.handle.net/20.500.12708/85623 ( reposiTUm)
Hunold, S. (2013). Can I repeat your parallel computing experiment? Yes, you can’t. Technische Universität Dresden, Zentrale für Informationsdienste und Hochleistungsrechnen (ZIH), Dresden, Deutschland, EU. http://hdl.handle.net/20.500.12708/85620 ( reposiTUm)
Hunold, S., & Lepping, J. (2012). Evolutionary Scheduling of Parallel Tasks Graphs onto Homogeneous Clusters. New Challenges in Scheduling Theory, Centre CNRS, Frejus, France, EU. http://hdl.handle.net/20.500.12708/85392 ( reposiTUm)
Hunold, S. (2012). Reproducibility and Data Provenance with VisTrails. WP8 meeting, ANR SONGS project, INRIA, Paris, France, EU. http://hdl.handle.net/20.500.12708/85431 ( reposiTUm)

Berichte

Hunold, S., von Kirchbach, K., Lehr, M., Schulz, C., & Träff, J. L. (2020). Efficient Process-to-Node Mapping Algorithms for Stencil Computations (2005.09521). arXiv. https://doi.org/10.48550/arXiv.2005.09521 ( reposiTUm)
Träff, J. L., Carpen-Amarie, A., Hunold, S., & Rougier, A. (2016). Message-Combining Algorithms for Isomorphic, Sparse Collective Communication (1606.07676). arXiv. https://doi.org/10.48550/arXiv.1606.07676 ( reposiTUm)
Hunold, S., Carpen-Amarie, A., Lübbe, F. D., & Träff, J. L. (2016). PGMPI: Automatically Verifying Self-Consistent MPI Performance Guidelines (1606.00215). arXiv. https://doi.org/10.48550/arXiv.1606.00215 ( reposiTUm)
Carpen-Amarie, A., Hunold, S., & Träff, J. L. (2016). MPI Derived Datatypes: Performance Expectations and Status Quo (1607.00178). arXiv. https://doi.org/10.48550/arXiv.1607.00178 ( reposiTUm)
Hunold, S., & Träff, J. L. (2013). On the State and Importance of Reproducible Experimental Research in Parallel Computing (1308.3648). arXiv. https://doi.org/10.48550/arXiv.1308.3648 ( reposiTUm)

Preprints

Hunold, S., & Przybylski, B. (2020). Scheduling.jl - Collaborative and Reproducible Scheduling Research with Julia. arXiv. https://doi.org/10.48550/arXiv.2003.05217 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2017). Tuning MPI Collectives by Verifying Performance Guidelines. arXiv. https://doi.org/10.48550/arXiv.1707.09965 ( reposiTUm)
Hunold, S., & Carpen-Amarie, A. (2015). MPI Benchmarking Revisited: Experimental Design and Reproducibility. arXiv. https://doi.org/10.48550/arXiv.1505.07734 ( reposiTUm)
Hunold, S. (2015). A Survey on Reproducibility in Parallel Computing. arXiv. https://doi.org/10.48550/arXiv.1511.04217 ( reposiTUm)

Hochschulschriften

Hunold, S. (2019). Benchmarking and scheduling on parallel machines [Professorial Dissertation, Technische Universität Wien]. reposiTUm. http://hdl.handle.net/20.500.12708/159450 ( reposiTUm)