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Träff, J. L. (2025). Communication Round and Computation Efficient Exclusive Prefix-Sums Algorithms (for MPI_Exscan). arXiv. https://doi.org/10.34726/10821

Salimi Beni, M., Laso, R., Cosenza, B., Benkner, S., & Hunold, S. (2025). Optimizing Distributed Deep Learning Training by Tuning NCCL. In ASHPC25 : Austrian-Slovenian HPC Meeting 2025 : Rimske Terme, Slovenia : 19-22 May 2025 (pp. 38–38). https://doi.org/10.34726/10424

Vardas, I., Laso Rodriguez, R., & Salimi Beni, M. (2025). ncclsee: A Lightweight Profiling Tool for NCCL. In ASHPC25 : Austrian-Slovenian HPC Meeting 2025 : Rimske Terme, Slovenia : 19-22 May 2025 (pp. 39–39). https://doi.org/10.34726/10426

Träff, J. L. (2025). Optimal, Non-pipelined Reduce-scatter and Allreduce Algorithms. arXiv. https://doi.org/10.34726/10760

Carpentieri, L., De Caro, A., Salimibeni, M., Fan, K., & Cosenza, B. (2025). Phase-Based Frequency Scaling for Energy-Efficient Heterogeneous Computing. In 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS) (pp. 824–836). IEEE. https://doi.org/10.1109/IPDPS64566.2025.00078

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

Vardas, I., Hunold, S., Swartvagher, P., & Träff, J. L. (2024). Exploring Mapping Strategies for Co-allocated HPC Applications. In Demetris Zeinalipour, D. Blanco Heras, G. Pallis, H. Herodotou, D. Trihinas, D. Balouek, P. Diehl, T. Cojean, K. Fürlinger, M. H. Kirkeby, M. Nardelli, & P. Di Sanzo (Eds.), Euro-Par 2023: Parallel Processing Workshops : Euro-Par 2023 International Workshops, Limassol, Cyprus, August 28 – September 1, 2023, Revised Selected Papers, Part II (pp. 271–276). Springer Nature. https://doi.org/10.1007/978-3-031-48803-0_41

Träff, J. L. (2024). Lectures on Parallel Computing. arXiv. https://doi.org/10.34726/10819

Träff, J. L. (2024). Optimal Broadcast Schedules in Logarithmic Time with Applications to Broadcast, All-Broadcast, Reduction and All-Reduction. arXiv. https://doi.org/10.34726/10820

Laso Rodriguez, R., Krupitza, D., & Hunold, S. (2024). pSTL-Bench: A Micro-Benchmark Suite for Assessing Scalability of C++ Parallel STL Implementations. arXiv. https://doi.org/10.48550/arXiv.2402.06384

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

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

Salimi Beni, M., Hunold, S., & Cosenza, B. (2024). Analysis and prediction of performance variability in large-scale computing systems. Journal of Supercomputing, 80(10), 14978–15005. https://doi.org/10.1007/s11227-024-06040-w

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

Träff, J. L. (2023). Round-optimal 𝑛-Block Broadcast Schedules in Logarithmic Time. arXiv. https://doi.org/10.34726/7320

Hunold, S. (2023, December 8). Unveiling the Complexities of Performance Analysis and Optimization in HPC Systems. Universität Münster, Münster, Germany.

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

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

Laso Rodriguez, R., & Casado, F. E. (2023, November 3). The research career after the PhD. CiTIUS (USC), Santiago de Compostela, Spain.

Träff, J. L., & Vardas, I. (2023). Library Development with MPI: Attributes, Request Objects, Group Communicator Creation, Local Reductions, and Datatypes. In Proceedings of the 30th European MPI Users’ Group Meeting (EUROMPI 23). 30th European MPI Users’ Group Meeting (EuroMPI 2023), Bristol, United Kingdom of Great Britain and Northern Ireland (the). ACM. https://doi.org/10.1145/3615318.3615323

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

Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2023). Realizing multioperations and multiprefixes in Thick Control Flow processors. Microprocessors and Microsystems, 98, Article 104807. https://doi.org/10.1016/j.micpro.2023.104807

Forsell, M., Roivainen, J., Leppänen, V., & Träff, J. L. (2023). Preliminary Performance and Memory Access Scalability Study of Thick Control Flow Processors. In J. Nurmi, M. Shen, P. Ellervee, P. Koch, & F. Moradi (Eds.), Proceedings 2023 IEEE Nordic Circuits and Systems Conference (NorCAS) (pp. 1–7). IEEE. https://doi.org/10.1109/NorCAS58970.2023.10305463

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

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

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

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

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

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

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

Träff, J. L. (2022). Brief Announcement: Fast(er) Construction of Round-optimal n-Block Broadcast Schedules. In K. Agrawal & I.-T. A. Lee (Eds.), Proceedings of the 34th ACM Symposium on Parallelism in Algorithms and Architectures (SPAA 2022) (pp. 143–146). ACM. https://doi.org/10.1145/3490148.3538560

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

Hunold, S., & Przybylski, B. (2022, May 18). Scheduling.jl - Collaborative and Reproducible Scheduling Research with Julia. New Challenges in Scheduling Theory (Centre CNRS “Paul-Langevin”, Aussois, France), Aussois, France.

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.

Forsell, M., Nikula, S., Roivainen, J., Leppänen, V., & Träff, J. L. (2022). Performance and programmability comparison of the thick control flow architecture and current multicore processors. The Journal of Supercomputing, 78(3), 3152–3183. https://doi.org/10.1007/s11227-021-03985-0

Hunold, S. (2022). Performance Tuning of MPI Collectives - Status Quo and Open Problems. CaSToRC HPC National Competence Center Fall Seminar Series 2022, Unknown.

Träff, J. L. (2022). (Poly)Logarithmic Time Construction of Round-optimal n-Block Broadcast Schedules for Broadcast and irregular Allgather in MPI. arXiv. https://doi.org/10.48550/arXiv.2205.10072

Träff, J. L. (2022). Fast(er) Construction of Round-optimal n-Block Broadcast Schedules. In Proceedings IEEE International Conference on Cluster Computing (CLUSTER 2022) (pp. 142–151). IEEE. https://doi.org/10.1109/CLUSTER51413.2022.00028

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

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.

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

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

Träff, J. L. (2021). A Doubly-pipelined, Dual-root Reduction-to-all Algorithm and Implementation. arXiv. https://doi.org/10.48550/arXiv.2109.12626

Träff, J. L., & Pöter, M. (2021). A more pragmatic implementation of the lock-free, ordered, linked list. In J. Lee & E. Petrank (Eds.), Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. ACM. https://doi.org/10.1145/3437801.3441579

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

Faraj, M. F., van der Grinten, A., Meyerhenke, H., Träff, J. L., & Schulz, C. (2020). High-Quality Hierarchical Process Mapping. arXiv. https://doi.org/10.48550/arXiv.2001.07134

Faraj, M. F., van der Grinten, A., Meyerhenke, H., Träff, J. L., & Schulz, C. (2020). High-Quality Hierarchical Process Mapping. In S. Faro & D. Cantone (Eds.), 18th International Symposium on Experimental Algorithms, SEA 2020 (pp. 4:1-4:15). Schloss Dagstuhl - Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.SEA.2020.4

Forsell, M., Roivainen, J., & Träff, J. L. (2020). Optimizing Memory Access in TCF Processors with Compute-Update Operations. In 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW). 22nd Workshop on Advances in Parallel and Distributed Computational Models (APDCM 2020) in conjunction with IPDPS 2020 - Online Conference, New Orleans, United States of America (the). IEEE. https://doi.org/10.1109/ipdpsw50202.2020.00100

Hunold, S., & Przybylski, B. (2020). Scheduling.jl - Collaborative and Reproducible Scheduling Research with Julia. arXiv. https://doi.org/10.48550/arXiv.2003.05217

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. IEEE. https://doi.org/10.1109/cluster49012.2020.00036

Hunold, S., von Kirchbach, K., Lehr, M., Schulz, C., & Träff, J. L. (2020). Efficient Process-to-Node Mapping Algorithms for Stencil Computations. arXiv. https://doi.org/10.48550/arXiv.2005.09521

Kirchbach, K. V., Schulz, C., & Träff, J. L. (2020). Better Process Mapping and Sparse Quadratic Assignment. ACM Journal on Experimental Algorithmics, 25, 1–19. https://doi.org/10.1145/3409667

Lehr, M., & von Kirchbach, K. (2020). Improved Cartesian Topology Mapping in MPI. In A. Schlögl, J. Kiss, & S. Elefante (Eds.), Austrian High-Performance-Computing Meeting (AHPC 2020) (p. 27). IST Austria. https://doi.org/10.15479/AT:ISTA:7474

Pachajoa, C., Levonyak, M., Pacher, C., Träff, J. L., & Gansterer, W. (2020). Classical and pipelined preconditioned conjugate gradient methods with node-failure resilience. In A. Schlögl, J. Kiss, & S. Elefante (Eds.), Austrian High-Performance-Computing Meeting (AHPC 2020) (p. 13). IST Austria. https://doi.org/10.15479/AT:ISTA:7474

Träff, J. L. (2020). Decomposing MPI Collectives for Exploiting Multi-lane Communication. SPCL_Bcast, ETH Zürich, Zürich, Switzerland.

Träff, J. L. (2020). k-ported vs. k-lane Broadcast, Scatter, and Alltoall Algorithms. arXiv. https://doi.org/10.48550/arXiv.2008.12144

Träff, J. L. (2020). Exploiting Multi-lane Communication in MPI Collectives. In A. Schlögl, J. Kiss, & S. Elefante (Eds.), Austrian High-Performance-Computing Meeting (AHPC 2020) (p. 30). IST Austria. https://doi.org/10.15479/AT:ISTA:7474

Träff, J. L. (2020). Signature Datatypes for Type Correct Collective Operations, Revisited. In 27th European MPI Users’ Group Meeting. 27th European MPI Users’ Group Meeting (EuroMPI/USA 2020) - Online Conference, Austin, United States of America (the). IEEE. https://doi.org/10.1145/3416315.3416324

Träff, J. L., & Hoefler, T. (2020). Special issue: Selected papers from EuroMPI 2019. Parallel Computing, 99, Article 102695. https://doi.org/10.1016/j.parco.2020.102695

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. IEEE. https://doi.org/10.1109/cluster49012.2020.00037

Träff, J. L., & Pöter, M. (2020). A more Pragmatic Implementation of the Lock-free, Ordered, Linked List. arXiv. https://doi.org/10.48550/arXiv.2010.15755

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, United States of America (the). IEEE. https://doi.org/10.1145/3416315.3416319

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. IEEE. https://doi.org/10.1109/cluster49012.2020.00011