Now showing 1 - 10 of 48
  • Publication
    Open Access
    hpc.bw benchmark report 2022–2024
    In the scope of the dtec.bw project hpc.bw, innovative HPC hardware resources were procured to investigate their performance for HSU-relevant compute-intensive software. Benchmarks for different software packages were conducted, and respective results are reported and documented in the following, considering the Intel Xeon architecture used in the HPC cluster HSUper, AMD EPYC 7763 and ARM FX700.
  • Publication
    Open Access
    xbat: a continuous benchmarking tool for HPC software
    (UB HSU, 2024-12-20)
    Tippmann, Nico
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    Auweter, Axel
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    ; ; ;
    Benchmarking the performance of one’s application in high performance computing (HPC) systems is critically important for reducing runtime and energy costs. Yet, accessing the plethora of relevant metrics that impact performance is often challenging, particularly for users without hardware experience. In this paper, we introduce the novel benchmarking tool xbat developed by MEGWARE GmbH. xbat requires no setup from the user side, and it allows the user to run, monitor and evaluate their application from the tool’s web interface, consolidating the entire benchmarking process in an approachable, intuitive workflow. We demonstrate the capabilities of the tool using benchmark applications of varying complexity and show that it can manage all aspects of the benchmarking workflow in a seamless manner. In particular, we focus on the open-source molecular dynamics research software ls1 mardyn, and the closed-source optimisation package Gurobi. Both packages present unique challenges. Mixed-integer programming solvers, such as those integrated in the Gurobi software, exhibit significant performance variability, so that seemingly innocuous parameter changes and machine characteristics can affect the runtime drastically, and ls1 mardyn comes with an auto-tuning library AutoPas, that enables the selection of various node-level algorithms to compute molecular trajectories. Focusing on these two packages, we showcase the practicality, versatility and utility of xbat, and share its current and future developments.
  • Publication
    Open Access
    hpc.bw: an evaluation of short-term performance engineering projects
    Increasing amounts of data and simulations in scientific areas enforce the need of improved software performance. The maintaining scientific staff is often not primarily trained for this purpose or lacks personnel and time to address software performance issues. A particular aim of the dtec.bw-funded project hpc.bw is to tackle some of these shortcomings. A pillar of the hpc.bw agenda is the offer of a low-threshold consultancy and development support focused on performance engineering. This paper provides an insight on our related activities. We illustrate the structure of our annual calls for short-term performance engineering projects, we outline our results at the example of the performance engineering project “benEFIT - Numerical simulation of non-destructive testing in concrete”, and we draw a first conclusion on the current procedure.
  • Publication
    Open Access
  • Publication
    Open Access
  • Publication
    Metadata only
  • Publication
    Open Access
    Methods for the integrated classification of ependymomas using computational pathology and omics data
    (Universitätsbibliothek der HSU/UniBw H, 2024-10-02) ; ;
    Helmut-Schmidt-Universität / Universität der Bundeswehr Hamburg
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    Neumann, Julia
    ;
    With about ten million deceased patients per year, the diagnosis and therapy of cancer represents one of the most important medical challenges to date. In the central nervous system, a rare yet very relevant tumor entity are ependymomas, which affect patients of all age groups and present unique challenges for their diagnosis. In particular, they exhibit heterogeneous histomorphological and molecular characteristics, which are used along with other properties to define 10 ependymoma types. These types are associated with variable prognosis and clinical outcome of patients and their accurate diagnosis is hence crucial for patient-specific treatment decisions. While diagnoses of ependymomas were traditionally based on histomorphological patterns, neuropathologists nowadays manually integrate these patterns with other sources of information, in particular DNA methylation profiles. However, such DNA methylation data was found to be inconsistent with histological assessment for a fraction of cases and is additionally too expensive for worldwide use in routine diagnostics. Thus, the field requires a unified view on molecular and morphological analyses of ependymomas, e.g., via prediction of DNA methylation types from histological images. Prospectively, further improvements for the diagnoses and treatment of ependymomas may arise from the additional consideration of protein profiles of the tumor. To date, however, measurement biases (batch-effects) and missing values prevent the integration and quantitative comparison of independently acquired proteome profiles and render novel and efficient data integration algorithms and classification algorithms necessary. In this work, an interpretable method for the prediction of ependymoma DNA methylation types from histological whole-slide images is developed using self-supervised and multiple-instance learning approaches. The approach is characterized on spinal cord ependymomas from the University Medical Center Hamburg-Eppendorf and is found to outperform the diagnoses of experienced neuropathologists. Moreover, the algorithm generalizes to data from other medical facilities with human-grade performance. Further characterization studies demonstrate that the approach can be applied to other common ependymoma types and that it scales to large datasets. Seizing the interpretability of the algorithm, novel, morphological evidence of major DNA methylation types of ependymomas is extracted. In comparison to other studies, the presented approach is the first to use neural networks in order to provide a unified view on the molecular and histomorphological landscape of clinically relevant ependymoma types from multiple anatomical compartments. With respect to the integration of proteomic datasets, a novel and computationally efficient algorithm for batch-effect correction of incomplete data is presented. In extensive parameter studies it is shown that, in comparison to existing approaches, the new algorithm offers improved tolerance to missing values as well as provides enhanced flexibility with respect to imbalanced data. It is demonstrated, that the method scales to large data integration tasks and can leverage the multi-core architecture of modern computers. The unique suitability of the method for the integration of proteomic and even transcriptomic data is demonstrated and the benefit of dataset integration for (diagnostic) classification algorithms is explored. Finally, this work investigates how incomplete molecular data (e.g., from proteome analyses) can be used to additionally improve classification performance. To this end, it introduces a novel classification method based on average pairwise correlations, which is found to yield improved classification results compared to other correlation-based approaches and to allow for the combination of the results from the aforementioned, newly developed algorithms into an integrated approach to ependymoma diagnostics. The benefit of this integrated method over the independent consideration of histological images or proteome data is demonstrated. In summary, this work is the first to present multiple, novel algorithmic approaches for the integrated classification of tumors. In particular, the presented methods allow to solve the unique diagnostic challenges of ependymomas by integration of proteomic and histological data. Prospectively, this work will allow researchers and clinical practitioners to obtain a better, integrated understanding of the histo-molecular characteristics for diseases under consideration and thus to improve their respective diagnoses and therapy.
  • Publication
    Metadata only
    Multiomic profiling of medulloblastoma reveals subtype-specific targetable alterations at the proteome and N-glycan level
    (Springer Nature, 2024-07-24)
    Godbole, Shwera
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    Voß, Hannah
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    Gocke, Antonia
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    ; ;
    Peng, Bojia
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    Mynarek, Martin
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    Rutkowski, Stefan
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    Dottermusch, Matthias
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    Dorostkar, Mario M.
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    Korshunov, Andrey
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    Mair, Thomas
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    Pfister, Stefan M.
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    Kwiatkowski, Marcel
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    Hotze, Madlen
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    Hartmann, Christian
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    Weis, Joachim
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    Liesche-Starnecker, Friederike
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    Guan, Yudong
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    Moritz, Manuela
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    Siebels, Bente
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    Struve, Nina
    ;
    Schlüter, Hartmut
    ;
    Neumann, Julia
    Medulloblastomas (MBs) are malignant pediatric brain tumors that are molecularly and clinically heterogenous. The application of omics technologies—mainly studying nucleic acids—has significantly improved MB classification and stratification, but treatment options are still unsatisfactory. The proteome and their N-glycans hold the potential to discover clinically relevant phenotypes and targetable pathways. We compile a harmonized proteome dataset of 167 MBs and integrate findings with DNA methylome, transcriptome and N-glycome data. We show six proteome MB subtypes, that can be assigned to two main molecular programs: transcription/translation (pSHHt, pWNT and pG3myc), and synapses/immunological processes (pSHHs, pG3 and pG4). Multiomic analysis reveals different conservation levels of proteome features across MB subtypes at the DNA methylome level. Aggressive pGroup3myc MBs and favorable pWNT MBs are most similar in cluster hierarchies concerning overall proteome patterns but show different protein abundances of the vincristine resistance-associated multiprotein complex TriC/CCT and of N-glycan turnover-associated factors. The N-glycome reflects proteome subtypes and complex-bisecting N-glycans characterize pGroup3myc tumors. Our results shed light on targetable alterations in MB and set a foundation for potential immunotherapies targeting glycan structures.
  • Publication
    Open Access
  • Publication
    Metadata only
    Smaller stencil preconditioners for linear systems in RBF-FD discretizations
    (Springer, 2024-04-20)
    Koch, Michael
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    Le Borne, Sabine
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    Radial basis function finite difference (RBF-FD) discretization has recently emerged as an alternative to classical finite difference or finite element discretization of (systems) of partial differential equations. In this paper, we focus on the construction of preconditioners for the iterative solution of the resulting linear systems of equations. In RBF-FD, a higher discretization accuracy may be obtained by increasing the stencil size. This, however, leads to a less sparse and often also worse conditioned stiffness matrix which are both challenges for subsequent iterative solvers. We propose to construct preconditioners based on stiffness matrices resulting from RBF-FD discretization with smaller stencil sizes compared to the one for the actual system to be solved. In our numerical results, we focus on RBF-FD discretizations based on polyharmonic splines (PHS) with polynomial augmentation. We illustrate the performance of smaller stencil preconditioners in the solution of the three-dimensional convection-diffusion equation.