Neumann, Philipp

Aerosol injectors applied in single-particle diffractive imaging experiments demonstrated their potential in efficiently delivering nanoparticles with high density. Continuous optimization of injector design is crucial for achieving high-density particle streams, minimizing background gas, enhancing x-ray interactions, and generating high-quality diffraction patterns. We present an updated simulation framework designed for the fast and effective exploration of the experimental parameter space to enhance the optimization process. The framework includes both the simulation of the carrier gas and the particle trajectories within injectors and their expansion into the experimental vacuum chamber. A hybrid molecular-continuum-simulation method [direct simulation Monte Carlo (DSMC)/computational fluid dynamics (CFD)] is utilized to accurately capture the multi-scale nature of the flow. The simulation setup, initial benchmark results of the coupled approach, and the validation of the entire methodology against experimental data are presented. The results of the enhanced methodology show a significant improvement in the prediction quality compared to previous approaches.

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.

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.

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.

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.