Long-range electrostatic interactions constitute a major computational bottleneck in particle-based simulations, particularly when periodic boundary conditions are employed. Mesh-based Ewald methods, such as the particle-particle particle-mesh (P3M) approach, reduce the computational complexity to O(N log N) but introduce challenges related to parallel scalability, communication overhead, and efficient utilization of modern heterogeneous architectures. While highly optimized implementations exist in established molecular dynamics packages, these are typically tightly coupled to full simulation frameworks, limiting their flexibility and reuse in emerging HPC applications.In this work, we present a performance-portable library for electrostatic solvers, providing implementations of both classical Ewald and P3M methods targeting CPU and GPU architectures. The library is designed to enable efficient execution on multi-GPU systems while maintaining portability across heterogeneous platforms. We investigate scaling and performance characteristics of the P3M method, with particular focus on the FFT-based long-range component, which is known to limit scalability. Strong-scaling experiments demonstrate that small problem sizes are dominated by communication overhead, leading to reduced parallel efficiency, whereas larger systems achieve near-ideal scaling over a wide range of GPU counts.Our results highlight the importance of balancing computation and communication in mesh-based electrostatics and demonstrate that portable, decoupled implementations can achieve competitive performance on modern HPC systems. The presented approach facilitates integration into diverse simulation workflows and provides a foundation for further optimization of long-range solvers in exascale computing environments.

Since Aurora entered the TOP500 list in November 2023, the top ten systems saw some shifts in the ratio of GPU vendors represented. With each vendor supplying their own preferred programming models for their hardware, it becomes relevant to compare the portability of these models on other hardware platforms. For the present paper we implemented the N-body problem with different optimizations using native and portable programming frameworks. For each of those we determined the best performing optimized version on one target architecture and compared the performance achieved for each platform.

With the inclusion of Aurora in the TOP500 list in November 2023 three different GPU (Graphics Processing Unit) vendors are represented in the top 10 of the list. Each vendor has its own preferred model for programming GPUs. For this paper we implemented the Nbody problem using portable programming frameworks and the vendors' preferred APIs. We show how the performance of the portable solutions compares to the performance of the native solution on each hardware both in absolute numbers and as raction of the achievable peak performance.

Many physical phenomena in liquids and soft matter are multiscale by nature and can involve processes with quantum and classical degrees of freedom occurring over a vast range of lengthand timescales.Examples range from structure formation processes of complex polymers or even polymer blends (Svaneborg & Everaers, 2023) on the classical side to charge and energy transport and conversion processes (Lee et al., 2019) involving explicit electronic and, therefore, quantum information.

Co-design across the Exascale Computing Project has been critical for both enabling science applications and bringing disparate communities together. Developing and porting applications to the various high-performance computing architectures on pre-exascale and exascale computers has been quite challenging due to the diversity of hardware features and software stacks. The Co-design Center for Particle Applications (CoPA) has developed and enhanced the Cabana and Parallel, Rapid O(N), and Graph-Based Recursive Electronic Structure Solver (PROGRESS)/Basic Matrix Library (BML) libraries to facilitate the creation of new particle applications, make existing particle applications exascale capable, and allow teams to explore new capabilities. Particle methods from the atomistic, mesoscale, and continuum through cosmological scales have been built with Cabana, along with new possibilities for application coupling. Similarly, the PROGRESS/BML library has enabled quantum particle applications with linear algebra solvers to use advanced hardware. Across these CoPA-developed libraries, the co-design abstraction layer combines performance portability with math library support to facilitate the separation of concerns and directly support science runs.

Most lithium batteries offer a wide range of applications. However, safety issues are still an unresolved issue for several applications. To solve the safety issue of Li-ion batteries, solid polymer electrolyte is a promising candidate to replace commercial liquid electrolyte. A 4-arm star poly(ethylene oxide) polymer with LiTFSI salt as an electrolyte was studied. The dynamics of this polymer were explored with the Quasi-Elastic Neutron Scattering technique. Furthermore, the influence of temperature and Li salt concentration on the polymer dynamics was investigated. The dynamics of the polymer ends of the arm show much higher flexibility than the core parts making those types of polymers attractive for further studies in battery research.

The Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. Cabana allows for a function portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system as well as analysis of said interplay with performance analysis tools. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to which heterogeneous applications might be more performant, using both CPU and GPU concurrently.

The Kokkos based library Cabana, which has been developed in the Co-design Center for Particle Applications (CoPA), is used for the implementation of Multi-Particle Collision Dynamics (MPCD), a particle-based description of hydrodynamic interactions. It allows a performance portable implementation, which has been used to study the interplay between CPU and GPU usage on a multi-node system. As a result, we see most advantages in a homogeneous GPU usage, but we also discuss the extent to heterogeneous applications, using both CPU and GPU concurrently.

We have implemented the computation of Coulomb interactions in particle systems using the performance portable C++ framework Kokkos. Coulomb interactions are evaluated with an Ewald-sum-based solver, where the interactions are split into long- and short-range contributions. The short-range contributions are calculated using pair-wise contributions of particles while long-range interactions are calculated using Fourier sums. We evaluate the performance portability of the implementation on Intel CPUs, including Intel Xeon Phi, and Nvidia GPUs.

Many scientific and medical researchers are working towards the creation of a virtual human-a personalized digital copy of an individual-that will assist in a patient's diagnosis, treatment and recovery. The complex nature of living systems means that the development of this remains a major challenge. We describe progress in enabling the HemeLB lattice Boltzmann code to simulate 3D macroscopic blood flow on a full human scale. Significant developments in memory management and load balancing allow near linear scaling performance of the code on hundreds of thousands of computer cores. Integral to the construction of a virtual human, we also outline the implementation of a self-coupling strategy for HemeLB. This allows simultaneous simulation of arterial and venous vascular trees based on human-specific geometries.

We have implemented the computation of Coulomb interactions in particle systems using the performance portable C++ framework Kokkos. For the computation of the electrostatic interactions in particle systems we used an Ewald summation. This implementation we consider as a basis for a performance portability study. As target architectures we used Intel CPUs, including Intel Xeon Phi, as well as Nvidia GPUs. To provide a measure for performance portability we compute the number of needed operations and required cycles, i.e. runtime, and compare these with the measured runtime. Results indicate a similar quality of performance portability on all investigated architectures.

We analyzed hydrodynamic fluctuations in nematic liquid crystals simulated by Multi-particle Collision Dynamics. Velocity effects on orientation were incorporated by allowing mesoscopic velocity gradients to exert torques on nematic particles. Backflow was included through an explicit application of angular momentum conservation during the collision events. We measured the spectra of hydrodynamic fluctuations and compared them with those derived from a linearized hydrodynamic scheme. Numerical results were found to reproduce the expected coupling between hydrodynamic modes, thus showing that the implementation simulates proper nematodynamic effects at the mesoscopic level.

Molecular Dynamics (MD) is a widely used tool for simulations of particle systems with pair-wise interactions. Since large scale MD simulations are very demanding in computation time, parallelisation is an important factor. As in the current HPC environment different heterogeneous computing architectures are emerging, a benchmark tool for a representative number of these architectures is desirable. OpenCL as a platform-overarching standard provides the capabilities for such a benchmark. This paper describes the implementation of an OpenCL MD benchmark code and discusses the results achieved on different types of computing hardware.

Classical molecular dynamics simulation for atomistic systems is implemented in OpenCL and benchmarked on a variety of different hardware platforms. Modifying the number of particles and system size in the study provides insight into characteristics of parallel compute platforms, where latency, data transfer, memory access characteristics and compute intense work can be identified as fingerprints in benchmark runs. Data layouts are compared, for which the access of structure-of-arrays shows best performance in most cases. It is demonstrated that function portability can be achieved straightforwardly with OpenCL, while performance portability lacks behind as various architectures strongly depend on specific vectorisation optimisation.

Current supercomputing platforms and scientific application codes have grown rapidly in complexity over the past years. Multi-scale, multi-domain simulations on one hand and deep hierarchies in large-scale computing platforms on the other make it exceedingly harder to map the former onto the latter and fully exploit the available computational power. The complexity of the software and hardware components involved calls for in-depth expertise that can only be met by diversity in the application development teams. With its model of simulation labs and cross-sectional groups, JARA-HPC enables such diverse teams to form on demand to solve concrete development problems. This work showcases the effectiveness of this model with two application case studies involving the JARA-HPC cross-sectional group “Parallel Efficiency” and simulation labs and domain-specific development teams. For one application, we show the results of a completed optimization and the estimated financial impact of the combined efforts. For the other application, we present results from an ongoing engagement, where we show how an on-demand team investigates the behavior of dynamic load balancing schemes for an MD particle simulation, leading to a better overall understanding of the application and revealing targets for further investigation.

We investigate the conformational properties of polymers in ionic microgels in the presence of salt ions by molecular dynamics simulations and analytical theory. A microgel particle consists of coarse-grained linear polymers, which are tetra-functionally crosslinked. Counterions and salt ions are taken into account explicitly, and charge-charge interactions are described by the Coulomb potential. By varying the charge interaction strength and salt concentration, we characterize the swelling of the polyelectrolytes and the charge distribution. In particular, we determine the amount of trapped mobile charges inside the microgel and the Debye screening length. Moreover, we analyze the polymer extension theoretically in terms of the tension blob model taking into account counterions and salt ions implicitly by the Debye–Hückel model. Our studies reveal a strong dependence of the amount of ions absorbed in the interior of the microgel on the electrostatic interaction strength, which is related to the degree of the gel swelling. This implies a dependence of the inverse Debye screening length κ on the ion concentration; we find a power-law increase of κ with the Coulomb interaction strength with the exponent 3 / 5 for a salt-free microgel and an exponent 1 / 2 for moderate salt concentrations. Additionally, the radial dependence of polymer conformations and ion distributions is addressed.

High-performance computing (HPC) is recognized as one of the pillars for further progress in science, industry, medicine, and education. Current HPC systems are being developed to overcome emerging architectural challenges in order to reach Exascale level of performance, projected for the year 2020. The much larger embedded and mobile market allows for rapid development of intellectual property (IP) blocks and provides more flexibility in designing an application-specific system-on-chip (SoC), in turn providing the possibility in balancing performance, energy-efficiency, and cost. In the Mont-Blanc project, we advocate for HPC systems being built from such commodity IP blocks, currently used in embedded and mobile SoCs. As a first demonstrator of such an approach, we present the Mont-Blanc prototype; the first HPC system built with commodity SoCs, memories, and network interface cards (NICs) from the embedded and mobile domain, and off-the-shelf HPC networking, storage, cooling, and integration solutions. We present the system's architecture and evaluate both performance and energy efficiency. Further, we compare the system's abilities against a production level supercomputer. At the end, we discuss parallel scalability and estimate the maximum scalability point of this approach across a set of applications.