Max Planck Computing and Data Facility

Kutzner C., Miletić V., Palacio Rodríguez K., Rampp M., Hummer G., de Groot B.L., Grubmüller H.

ABSTRACTWe benchmarked the performance of the GROMACS 2024 molecular dynamics (MD) code on a modern high‐performance computing (HPC) cluster with AMD CPUs on up to 65,536 CPU cores. We used five different MD systems, ranging in size from about 82,000 to 204 million atoms, and evaluated their performance using two different Message Passing Interface (MPI) libraries, Intel‐MPI and Open‐MPI. The largest system showed near‐perfect strong scaling up to 512 nodes or 65,536 cores, maintaining a parallel efficiency above 0.9 even at the highest level of parallelization. Energy efficiency for a given number of nodes was generally equal to or slightly better than parallel efficiency. We achieved peak performances of 687 ns/d for the 82k atom system, 116 ns/d for the 53M atom system, and about 35 ns/d for the largest 204M atom system. These results demonstrate that highly optimized software running on a state‐of‐the‐art HPC cluster provides sufficient computing power to simulate biomolecular systems at the mesoscale of viruses and organelles, and potentially small cells in the near future.

Bonafé F.P., Albar E.I., Ohlmann S.T., Kosheleva V.P., Bustamante C.M., Troisi F., Rubio A., Appel H.

We report an , nonrelativistic QED method that couples light and matter self-consistently beyond the electric dipole approximation and without multipolar truncations. This method is based on an extension of the Maxwell-Pauli-Kohn-Sham approach to a full minimal coupling Hamiltonian, where the space- and time-dependent vector potential is coupled to the matter system, and its back reaction to the radiated fields is generated by the full current density. The implementation in the open-source code is designed for massively parallel multiscale simulations considering different grid spacings for the Maxwell and matter subsystems. Here, we show applications of this framework to simulate renormalized Cherenkov radiation of an electronic wave packet, magneto-optical effects with nonchiral light in nonchiral molecular systems, and renormalized plasmonic modes in a nanoplasmonic dimer. We show that in some cases, the beyond-dipole effects cannot be captured by a multipolar expansion Hamiltonian in the length gauge. Finally, we discuss further opportunities enabled by the framework in the field of twisted light and orbital angular momentum, inelastic light scattering, and strong-field physics. Published by the American Physical Society 2025

Li Y., Colnaghi T., Gong Y., Zhang H., Yu Y., Wei Y., Gan B., Song M., Marek A., Rampp M., Zhang S., Pei Z., Wuttig M., Ghosh S., Körmann F., et. al.

Dang K.M., Zhang Y.J., Zhang T., Wang C., Sinner A., Coronica P., Poon J.K.

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals.

Yu J., Wang Z., Saksena A., Wei S., Wei Y., Colnaghi T., Marek A., Rampp M., Song M., Gault B., Li Y.

Quantitative analysis of microstructural features on the nanoscale, including precipitates, local chemical orderings (LCOs) or structural defects (e.g. stacking faults) plays a pivotal role in understanding the mechanical and physical responses of engineering materials. Atom probe tomography (APT), known for its exceptional combination of chemical sensitivity and sub-nanometer resolution, primarily identifies microstructures through compositional segregations. However, this fails when there is no significant segregation, as can be the case for LCOs and stacking faults. Here, we introduce a 3D deep learning approach, AtomNet, designed to process APT point cloud data at the single-atom level for nanoscale microstructure extraction, simultaneously considering compositional and structural information. AtomNet is showcased in segmenting L12-type nanoprecipitates from the matrix in an AlLiMg alloy, irrespective of crystallographic orientations, which outperforms previous methods. AtomNet also allows for 3D imaging of L10-type LCOs in an AuCu alloy, a challenging task for conventional analysis due to their small size and subtle compositional differences. Finally, we demonstrate the use of AtomNet for revealing 2D stacking faults in a Co-based superalloy, without any stacking-faults-relevant samples in the training dataset, expanding the capabilities for automated exploration of hidden microstructures in APT data. AtomNet can thus recognize challenging microstructures, including nanoprecipitates with diameters above 2 nm, LCOs with diameters of about 1–2 nm without obvious compositional segregation, and even unforeseen planar defects by analyzing atom-atom environments. AtomNet pushes the boundaries of APT analysis, and holds promise in establishing precise quantitative microstructure-property relationships across a diverse range of metallic materials.

Ju Y., Huber D., Perez A., Ulbl P., Markidis S., Schlatter P., Schulz M., Schreiber M., Laure E.

The computational power of High-Performance Computing (HPC) systems increases continuously and rapidly. Data-intensive applications are designed to leverage the high computational capacity of HPC resources and typically generate a large amount of data for traditional post-processing data analytics. However, the HPC systems’ in-/output (IO) subsystem develops relatively slowly, and the storage capacity is limited. This could lead to limited actual performance and scientific discovery. In-situ techniques are a partial remedy to these problems by reducing or avoiding the data flow through the IO subsystem to/from the storage. However, in current practice, asynchronous in-situ techniques with static resource management often allocate separate computing resources for executing in-situ task(s), which remain idle if no in-situ work is at hand. In the present work, we target improving the efficiency of computing resource usage by launching and releasing necessary additional computing resources for in-situ task(s). Our approach is based on extensions for MPI Sessions that enable the required dynamic resource management. In this paper, we propose a basic and an advanced in-situ techniques with dynamic resource management enabled by MPI Sessions, their implementations on two real-world use cases, and a critical analysis of the experimental results.

Li Y., Colnaghi T., Gong Y., Zhang H., Yu Y., Wei Y., Gan B., Song M., Marek A., Rampp M., Zhang S., Pei Z., Wuttig M., Ghosh S., Körmann F., et. al.

AbstractIn solids, chemical short‐range order (CSRO) refers to the self‐organization of atoms of certain species occupying specific crystal sites. CSRO is increasingly being envisaged as a lever to tailor the mechanical and functional properties of materials. Yet quantitative relationships between properties and the morphology, number density, and atomic configurations of CSRO domains remain elusive. Herein, it is showcased how machine learning‐enhanced atom probe tomography (APT) can mine the near‐atomically resolved APT data and jointly exploit the technique's high elemental sensitivity to provide a 3D quantitative analysis of CSRO in a CoCrNi medium‐entropy alloy. Multiple CSRO configurations are revealed, with their formation supported by state‐of‐the‐art Monte‐Carlo simulations. Quantitative analysis of these CSROs allows establishing relationships between processing parameters and physical properties. The unambiguous characterization of CSRO will help refine strategies for designing advanced materials by manipulating atomic‐scale architectures.

Krushevska V., Shugarov S., Ochner P., Kuznyetsova Y., Petrov M., Kroll P.

Abstract In this study, we present an investigation of the newly discovered dwarf nova ASASSN-19oc during its superoutburst on 2019 June 2. We carried out detailed UBVR c I c -photometric observations and also obtained a spectrum on day 7 of the outburst, which shows the presence of hydrogen absorption lines commonly found in dwarf nova outbursts. Analysis of photometric data reveals the occurrence of early superhumps in the initial days of observations, followed by ordinary and late superhumps. We have accurately calculated the period of the ordinary superhumps as P ord = 0.05681(10) days and determined the periods at different stages, as well as the rate of change of the superhump period (P dot = P ̇ /P = 8.1 × 10−5). Additionally, we have derived the mass ratio of the components (q = 0.09), and estimated the color temperature during the outburst as ∼11,000 K, the distance to the system (d = 560 pc) and absolute magnitude of the system in outburst (M V = 5.3). We have shown that outbursts of this star are very rare: based on brightness measurements on 600 archival photographic plates, we found only one outburst that occurred in 1984. This fact, as well as the properties listed above, convincingly shows that the variable ASASSN-19oc is a dwarf nova of WZ Sge type.

Kokott S., Merz F., Yao Y., Carbogno C., Rossi M., Havu V., Rampp M., Scheffler M., Blum V.

Hybrid density functional approximations (DFAs) offer compelling accuracy for ab initio electronic-structure simulations of molecules, nanosystems, and bulk materials, addressing some deficiencies of computationally cheaper, frequently used semilocal DFAs. However, the computational bottleneck of hybrid DFAs is the evaluation of the non-local exact exchange contribution, which is the limiting factor for the application of the method for large-scale simulations. In this work, we present a drastically optimized resolution-of-identity-based real-space implementation of the exact exchange evaluation for both non-periodic and periodic boundary conditions in the all-electron code FHI-aims, targeting high-performance central processing unit (CPU) compute clusters. The introduction of several new refined message passing interface (MPI) parallelization layers and shared memory arrays according to the MPI-3 standard were the key components of the optimization. We demonstrate significant improvements of memory and performance efficiency, scalability, and workload distribution, extending the reach of hybrid DFAs to simulation sizes beyond ten thousand atoms. In addition, we also compare the runtime performance of the PBE, HSE06, and PBE0 functionals. As a necessary byproduct of this work, other code parts in FHI-aims have been optimized as well, e.g., the computation of the Hartree potential and the evaluation of the force and stress components. We benchmark the performance and scaling of the hybrid DFA-based simulations for a broad range of chemical systems, including hybrid organic–inorganic perovskites, organic crystals, and ice crystals with up to 30 576 atoms (101 920 electrons described by 244 608 basis functions).

Bauer S., Benner P., Bereau T., Blum V., Boley M., Carbogno C., Catlow R., Dehm G., Eibl S., Ernstorfer R., Fekete Á., Foppa L., Fratzl P., Freysoldt C., Gault B., et. al.

Abstract Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm of’ materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research and experimental techniques like photoemission, and electron microscopy.
While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

Dharmawardena T.E., Bailer-Jones C.A., Fouesneau M., Foreman-Mackey D., Coronica P., Colnaghi T., Müller T., Wilson A.G.

Abstract Three-dimensional dust density maps are crucial for understanding the structure of the interstellar medium of the Milky Way and the processes that shape it. However, constructing these maps requires large datasets and the methods used to analyse them are computationally expensive and difficult to scale up. As a result it is has only recently become possible to map kiloparsec-scale regions of our Galaxy at parsec-scale grid sampling. We present all-sky three-dimensional dust density and extinction maps of the Milky Way out to 2.8 kpc in distance from the Sun using the fast and scalable Gaussian Process algorithm Dustribution. The sampling of the three-dimensional map is l, b, d = 1○ × 1○ × 1.7 pc. The input extinction and distance catalogue contains 120 million stars with photometry and astrometry from Gaia DR2, 2MASS and AllWISE. This combines the strengths of optical and infrared data to probe deeper into the dusty regions of the Milky Way. We compare our maps with other published 3D dust maps. All maps quantitatively agree at the 0.001 mag pc−1 scale with many qualitatively similar features, although each map also has its own features. We recover Galactic features previously identified in the literature. Moreover, we also see a large under-density that may correspond to an inter-arm or -spur gap towards the Galactic Centre.

Salvadore F., Rossi G., Sathyanarayana S., Bernardini M.

Nearly 20 years after the birth of general-purpose GPU computing, the HPC landscape is now dominated by GPUs. After years of undisputed dominance by NVIDIA, new players have entered the arena in a convincing manner, namely AMD and more recently Intel, whose devices currently power the first two clusters in the Top500 ranking. Unfortunately, code porting is still a major problem, even more due to the presence of different vendors, but at the same time the emergence of simplified standard paradigms suggests an encouraging prospect for developers. In this work, we provide a detailed OpenMP porting strategy of STREAmS, a community code for the compressible fluid dynamics. The proposed porting technique is based on the offload functionality of the OpenMP 5.x paradigm and in particular on a hybrid directives/APIs approach that fits seamlessly into the multi-backend software ecosystem of STREAmS. We further carry out a comprehensive performance analysis on the Intel® Data Center GPU Max 1550 (formerly called Ponte Vecchio or PVC). In addition, we analyze the performance of the code on two benchmark clusters powered by PVC, including the exascale Aurora cluster. The performance is evaluated at different levels of parallelism involved, i.e., the intrinsic parallelism of the PVC tile, the inter-tile parallelism within the GPU configuration, between the GPUs within the node and between the nodes within the cluster. The analysis shows that although the implementation complexity of the OpenMP porting is limited, it is necessary to follow some important guidelines to achieve satisfactory performance. The PVC GPU shows about 40% higher performance than the NVIDIA A100 or AMD MI250X GPUs, which, however, were released about 3 years earlier. Both intra-node and internode scalability show good results. Overall, the introduction of PVC into the GPU computing HPC landscape represents a positive step forward for the diversification and competitiveness of the sector.

Cruz-León S., Majtner T., Hoffmann P.C., Kreysing J.P., Kehl S., Tuijtel M.W., Schaefer S.L., Geißler K., Beck M., Turoňová B., Hummer G.

AbstractVisual proteomics attempts to build atlases of the molecular content of cells but the automated annotation of cryo electron tomograms remains challenging. Template matching (TM) and methods based on machine learning detect structural signatures of macromolecules. However, their applicability remains limited in terms of both the abundance and size of the molecular targets. Here we show that the performance of TM is greatly improved by using template-specific search parameter optimization and by including higher-resolution information. We establish a TM pipeline with systematically tuned parameters for the automated, objective and comprehensive identification of structures with confidence 10 to 100-fold above the noise level. We demonstrate high-fidelity and high-confidence localizations of nuclear pore complexes, vaults, ribosomes, proteasomes, fatty acid synthases, lipid membranes and microtubules, and individual subunits inside crowded eukaryotic cells. We provide software tools for the generic implementation of our method that is broadly applicable towards realizing visual proteomics.

Wlazłowski G., Forbes M.M., Sarkar S.R., Marek A., Szpindler M.

Abstract Ultracold atoms provide a platform for analog quantum computer capable of simulating the quantum turbulence that underlies puzzling phenomena like pulsar glitches in rapidly spinning neutron stars. Unlike other platforms like liquid helium, ultracold atoms have a viable theoretical framework for dynamics, but simulations push the edge of current classical computers. We present the largest simulations of fermionic quantum turbulence to date and explain the computing technology needed, especially improvements in the Eigenvalue soLvers for Petaflop Applications(ELPA) library that enable us to diagonalize matrices of record size (millions by millions). We quantify how dissipation and thermalization proceed in fermionic quantum turbulence by using the internal structure of vortices as a new probe of the local effective temperature. All simulation data and source codes are made available to facilitate rapid scientific progress in the field of ultracold Fermi gases.

Carretero J., Garcia-Blas J., Brinkmann A., Vef M., Besnard J., Torquati M., Ju Y., Montella R.

ADAPIO symposium was a forum to discuss how to create adaptive I/O systems through the creation of an active I/O stack that dynamically adjusts computation and storage requirements through intelligent coordination, malleability of computation and I/O, and the scheduling of storage resources along all levels of the storage hierarchy. Moreover, ADAPIO shows examples of co-designing applications and I/O.