Publikationen

2025

Konferenzen & Workshops

  • Efficient Multi-GPU Programming in Python: Reducing Synchronization and Access Overheads L. Oden, K. Nölp 2025 IEEE International Conference on Cluster Computing (CLUSTER) DOI: 10.1109/CLUSTER59342.2025.11186485
  • nbshmem: Enabling GPU-Initiated Multi-GPU Communication in Python C. Bombis, L. Oden 2025 IEEE International Parallel and Distributed Processing Symposium (IPDPS) [DOI: 10.1109/IPDPS.2025.00000] (DOI via IEEE Xplore zu prüfen)
  • Hybrid GPU Programming Education with Python and C++: Preferences, Performance and Common Python Pitfalls L. Oden, K. Nölp EduHPC-25: Workshop on Education for High Performance Computing (SC25 Workshops) Link: EduHPC Program (Proceedings erscheinen via IEEE)
  • Comparing different approaches for utilizing NVIDIA GPUs in Python B. Uriel, J. Schmitt, L. Oden 31. PARS-Workshop 2025 Link: GI Digital Library / PARS (Link folgt nach Veröffentlichung)
  • Accelerating Gzip Decompression on Aarch64 by Vectorizing CRC32 M. Vogel, L. Oden 31. PARS-Workshop 2025 Link: GI Digital Library / PARS (Link folgt nach Veröffentlichung)

Journals

Preparing MPICH for exascale Y. Guo, K. Raffenetti, H. Zhou, P. Balaji, M. Si, A. Amer, S. Iwasaki, S. Seo, et al. The International Journal of High Performance Computing Applications 39 (2) DOI: 10.1177/10943420241311608

2024

Konferenzen & Workshops

  • Integrating interactive performance analysis in Jupyter notebooks for parallel programming education L. Oden, K. Nölp, P. Brauner 2024 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) DOI: 10.1109/IPDPSW63119.2024.00084

Journals

  • The coming decade of digital brain research: A vision for neuroscience at the intersection of technology and computing K. Amunts, M. Axer, S. Banerjee, L. Bitsch, J.G. Bjaalie, P. Brauner, A. Brovelli, et al. Imaging Neuroscience 2, 1-35 DOI: 10.1162/imag_a_00137

Ausgewählte Publikationen 2017-2023

  • Simplifying non-contiguous data transfer with MPI for Python K. Nölp, L. Oden The Journal of Supercomputing 79 (17) https://link.springer.com/article/10.1007/s11227-023-05398-7
  • Evaluation of GPU-Compression Algorithms for CUDA-Aware MPI M. Vogel, L.Oden 30. PARS-Workshop 2023 (PARS-Mitteilungen Vol. 36) https://dl.gi.de/items/93ac972f-1ea6-4c34-b6db-89e4e3797712
  • Improving cryptanalytic applications with stochastic runtimes on GPUs (2021) L. Oden, J. Keller 2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) DOI: 10.1109/IPDPSW52791.2021.00014
  • Lessons learned from comparing C-CUDA and Python-Numba for GPU-Computing (2020) L. Oden 2020 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) DOI: 10.1109/PDP50117.2020.00049
  • Fall-detection on a wearable micro controller using machine learning algorithms (2020) L. Oden, T. Witt 2020 IEEE International Conference on Smart Computing (SMARTCOMP) DOI: 10.1109/SMARTCOMP50058.2020.00067
  • IO challenges for human brain atlasing using deep learning methods: an in-depth analysis (2019) L. Oden, C. Schiffer, H. Spitzer, T. Dickscheid, D. Pleiter 2019 27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP) DOI: 10.1109/EMPDP.2019.8671630
  • InfiniBand Verbs on GPU: a case study of controlling an InfiniBand network device from the GPU (2017) L. Oden, H. Fröning The International Journal of High Performance Computing Applications 31 (4) DOI: 10.1177/1094342015618017
  • Why is MPI so slow? Analyzing the fundamental limits in implementing MPI-3.1 (2017) K. Raffenetti, A. Amer, L. Oden, C. Archer, W. Bland, H. Fujita, Y. Guo, et al. Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC) DOI: 10.1145/3126908.3126963
  • Pol Santamaria, Lena Oden, Yolanda Becerra, Eloy Gil, Raül Sirvent, Philipp Glock and Jordi Torres
    “Evaluating the benefits of Key-Value databases for scientific applications”, International Conference on Computational Science (ICCS), Faro, 2019
  • Nikela Papadopoulou, Lena Oden and Pavan Balaji,
    ”A Performance Study of UCX over InfiniBand”, International Symposium on Cluster, Cloud and Grid Computing, (CCGrid), 2017
  • Lena Oden and Pavan Balaji,
    ”Hexe: A Toolkit for Heterogeneous Memory Management”, 2017 IEEE 23rd International Conference on Parallel and Distributed Systems (ICPADS), Shenzhen, 2017
  • Lena Oden, Benjamin Klenk, Holger Fröning,
    ”Analyzing GPU-controlled communication with dynamic parallelism in terms of performance and energy”, Parallel Computing, Volume 57, September 2016, Pages 125-134
  • Lena Oden and Holger Fröning,
    ”InfiniBand Verbs on GPU: a case study of controlling an InfiniBand network device from the GPU”, , The International Journal of High Performance Computing Applications, Online since 2015
  • Benjamin Klenk, Lena Oden and Holger Fröning,
    ”Analyzing communication models for distributed thread collaborative processors in terms of energy and time”, International Symposium on Performance Analysis of Systems and Software (ISPASS), 2015 IEEE
  • Lena Oden, Benjamin Klenk, Holger Fröning,
    ”Energy-Efficient Collective Reduce and Allreduce Operations on Distributed GPUs”, IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2014)
  • Lena Oden, Holger Fröning, Franz-Joseph Pfreundt,
    ”Infiniband-Verbs on GPU: A case study of controlling an Infiniband network device from the GPU”, Workshop on Accelerators and Hybrid Exascale Systems (Ashes), at IDPDS 2014
  • Benjamin Klenk, Lena Oden and Holger Frönig,
    ”Analyzing Put/Get APIs for Threadcollaborative Processors”, Workshop on Heterogeneous and Unconventional Cluster Architectures and Applications (HUCAA), at ICPP 2014
  • Benjamin Klenk, Lena Oden , Holger Fröning,
    ”GPU-centric communication for improved efficiency”, Workshop on Green Programming, Computing and Data Processing (GPCDP) in conjunction with International Green Computing Conference, 2014
  • Lena Oden , Benjamin Klenk, Holger Fröning,
    ”Energy-Efficient Stencil Computations on Distributed GPUs Using Dynamic Parallelism and GPU-Controlled Communication”, Energy Efficient Supercomputing Workshop, 2014
  • Alrutz T. et al.
    „GASPI – A Partitioned Global Address Space Programming Interface“, Facing the Multicore-Challenge III. Lecture Notes in Computer Science, vol 7686. Springer, Berlin, Heidelberg, 2013
  • Lena Oden,
    ”GPI2 for GPUs: A PGAS framework for efficient communication on hybrid clusters”, International Conference on Parallel Computing (ParCo 2013)
  • Lena Oden and Holger Fröning,
    ”GGAS: Global GPU address spaces for efficient communication in heterogeneous clusters”, IEEE International Conference Cluster Computing (Cluster 2013)
Technische Informatik | 19.12.2025