We present a hybrid quantum-classical electrostatic Particle-in-Cell (PIC) method, where the electrostatic field Poisson solver is implemented on a quantum computer simulator using a hybrid classical-quantum Neural Network (HNN) using data-driven and physics-informed learning approaches. The HNN is trained on classical PIC simulation results and executed via a PennyLane quantum simulator. The remaining computational steps, including particle motion and field interpolation, are performed on a classical system. To evaluate the accuracy and computational cost of this hybrid approach, we test the hybrid quantum-classical electrostatic PIC against the two-stream instability, a standard benchmark in plasma physics. Our results show that the quantum Poisson solver achieves comparable accuracy to classical methods. It also provides insights into the feasibility of using quantum computing and HNNs for plasma simulations. We also discuss the computational overhead associated with current quantum computer simulators, showing the challenges and potential advantages of hybrid quantum-classical numerical methods.

Turbulence data sets produced from computational fluid dynamics (CFD), especially from fine-resolved direct numerical simulations (DNS) and large eddy simulations (LES) of turbulent flows, tend to be very large due to high resolutions adopted to accurately resolve the smallest scales. While the computational capacity of high-performance computing (HPC) platforms has kept increasing, storage capacity has lagged to the point that more data is being produced than what can be efficiently managed. Among the several methods emerged to deal with this problem, an efficient technique is data compression. In this study, we present a proof of concept of a novel data compression approach that relies on Gaussian process regression (GPR) within a Bayesian framework to handle data sets in such a way that initially discarded information can be recovered a posteriori. The approach can be used to supplement existing compression algorithms with measures of uncertainty and we show that it can be applied to compress not only the 3D spatial fields of turbulence but also the discrete sets of time series data. The compression algorithm has been designed for data from spectral element method (SEM) simulations but can be extended to spatiotemporal fields obtained from other methods arising in engineering and physics. Our investigation shows that it is possible to use Gaussian process regression for data compression, however also highlights several of its limitations, in particular, that efficient implementations of GPR are crucial for its adoption, and that, while it is unlikely that the method can compete in terms of throughput with state of the art methods, given the cost of GPR, there is potential in terms of compression performance, as long as efficient bit-plane coding is integrated.

Modern computing clusters offer specialized hardware for reduced-precision arithmetic, which can significantly speed up the time to solution. This is possible due to a decrease in data movement, as well as the ability to perform arithmetic operations at a faster rate. However, for high-fidelity simulations of turbulence, such as direct and large-eddy simulation, the impact of reduced precision on the computed solution and the resulting uncertainty across flow solvers and different flow cases has not been explored in detail, and limits the optimal utilization of new high-performance computing systems. In this work, the effect of reduced precision is studied using four diverse computational fluid dynamics (CFD) solvers (two incompressible, Neko and Simson, and two compressible, PadeLibs and SSDC) using four test cases: turbulent channel flow at Reτ=550 and higher, forced transition in a channel, flow over a cylinder at ReD=3900, and compressible flow over a wing section at Rec=50000. We observe that the flow physics are remarkably robust with respect to reductions in lower floating-point precision, and that often other forms of uncertainty, due to, for example, time averaging, often have a much larger impact on the computed result. Our results indicate that different terms in the Navier–Stokes equations can be computed to a lower floating-point accuracy without affecting the results. In particular, standard IEEE single precision can be used effectively for the entirety of the simulation, showing no significant discrepancies from double-precision results across the solvers and cases considered. Potential pitfalls are also discussed. 

Efficient simulation of complex plasma dynamics is crucial for advancing fusion energy research. Particle-in-Cell (PIC) Monte Carlo (MC) simulations provide insights into plasma behavior, including turbulence and confinement, which are essential for optimizing fusion reactor performance. Transitioning to exascale simulations introduces significant challenges, with traditional file input/output (I/O) inefficiencies remaining a key bottleneck. This work advances BIT1, an electrostatic PIC MC code, by improving the particle mover with OpenMP task-based parallelism, integrating openPMD’s streaming API, and enabling in-memory data streaming with the ADIOS2 Sustainable Staging Transport (SST) engine to enhance I/O performance, computational efficiency, and system storage utilization. We employ profiling tools such as gprof, perf, IPM and Darshan, which provide insights into computation, communication, and I/O operations. We implement time-dependent data checkpointing with the openPMD API enabling seamless data movement and in-situ visualization for real-time analysis without interrupting the simulation. We demonstrate improvements in simulation runtime, data accessibility and real-time insights by comparing traditional file I/O with the ADIOS2 BP4 and SST backends. The proposed hybrid BIT1 openPMD SST enhancement introduces a new paradigm for real-time scientific discovery in plasma simulations, enabling faster insights and more efficient use of exascale computing resources.

We present the design, development, and application of QHDL, a quantum hardware description language specifically designed for tightly-coupled quantum–classical computing systems. Together with the language design principles, we describe the QHDL compiler, debugger, and co-simulation infrastructure. We showcase the benefits of using a quantum–classical integrated approach in four use cases, requiring close quantum–classical device interaction: Bell's pair circuit, dynamic delay, Quantum Fourier Transform (QFT), and teleportation. To interface with QHDL, we propose to use synchronous techniques that are commonplace in digital hardware design. We illustrate examples of modeling both loosely-coupled and tightly-coupled quantum circuits that use so-called measurement-in-the-middle by utilizing these techniques in QHDL. For clock-cycle accurate implementations, we propose implementing such classical modules as programmable hardware blocks using Register-Transfer Level (RTL) or gate-level approaches. These approaches provide the highest coupling performance and are feasible to be implemented in state-of-the-art control systems.

As fusion energy devices advance, plasma simulations play a critical role in fusion reactor design. Particle-in-Cell Monte Carlo simulations are essential for modelling plasma-material interactions and analysing power load distributions on tokamak divertors. Previous work introduced hybrid parallelization in BIT1 using MPI and OpenMP/OpenACC for shared-memory and multicore CPU processing. In this extended work, we integrate MPI with OpenMP and OpenACC, focusing on asynchronous multi-GPU programming with OpenMP Target Tasks using the "nowait" and "depend" clauses, and OpenACC Parallel with the "async(n)" clause. Our results show significant performance improvements: 16 MPI ranks plus OpenMP threads reduced simulation runtime by 53% on a petascale EuroHPC supercomputer, while the OpenACC multicore implementation achieved a 58% reduction compared to the MPI-only version. Scaling to 64 MPI ranks, OpenACC outperformed OpenMP, achieving a 24% improvement in the particle mover function. On the HPE Cray EX supercomputer, OpenMP and OpenACC consistently reduced simulation times, with a 37% reduction at 100 nodes. Results from MareNostrum 5, a pre-exascale EuroHPC supercomputer, highlight OpenACC's effectiveness, with the "async(n)" configuration delivering notable performance gains. However, OpenMP asynchronous configurations outperform OpenACC at larger node counts, particularly for extreme scaling runs. As BIT1 scales asynchronously to 128 GPUs, OpenMP asynchronous multi-GPU configurations outperformed OpenACC in runtime, demonstrating superior scalability, which continues up to 400 GPUs, further improving runtime. Speedup and parallel efficiency (PE) studies reveal OpenMP asynchronous multi-GPU achieving an 8.77x speedup (54.81% PE) and OpenACC achieving an 8.14x speedup (50.87% PE) on MareNostrum 5, surpassing the CPU-only version. At higher node counts, PE declined across all implementations due to communication and synchronization costs. However, the asynchronous multi-GPU versions maintained better PE, demonstrating the benefits of asynchronous multi-GPU execution in reducing scalability bottlenecks. While the CPU-only implementation is faster in some cases, OpenMP's asynchronous multi-GPU approach delivers better GPU performance through asynchronous data transfer and task dependencies, ensuring data consistency and avoiding race conditions. Using NVIDIA Nsight tools, we confirmed BIT1's overall efficiency for large-scale plasma simulations, leveraging current and future exascale supercomputing infrastructures. Asynchronous data transfers and dedicated GPU assignments to MPI ranks enhance performance, with OpenMP’s asynchronous multi-GPU implementation utilizing OpenMP Target Tasks with "nowait" and "depend" clauses outperforming other configurations. This makes OpenMP the preferred application programming interface when performance portability, high throughput, and efficient GPU utilization are critical. This enables BIT1 to fully exploit modern supercomputing architectures, advancing fusion energy research. MareNostrum 5 brings us closer to achieving exascale performance.

This paper investigates the architectural features and performance potential of the Apple Silicon M-Series SoCs (M1, M2, M3, and M4) for HPC. We provide a detailed review of the CPU and GPU designs, the unified memory architecture, and coprocessors such as Advanced Matrix Extensions (AMX). We design and develop benchmarks in the Metal Shading Language and Objective-C++ to assess FP32 computational and memory performance. We also measure power consumption and efficiency using Apple's powermetrics tool. Our results show that the M-Series chips offer up to 100 GB/s memory bandwidth, and significant generational improvements in computational performance, with up to 2.9 FP32 TFLOPS on the M4. Power consumption varies from a few Watts to 10-20 Watts, with more than 200 GFLOPS per Watt efficiency of GPU and accelerator reached by all four chips. Despite limitations in FP64 support on the GPU, the M-Series chips demonstrate strong potential for energy-efficient HPC applications. While existing HPC solutions such as the Nvidia Grace-Hopper superchip outperform Apple Silicon in both memory bandwidth and computational performance, we see that the M-Series provides a competitive power-efficient alternative to traditional HPC architectures and represents a distinct category altogether - forming an apples-to-oranges comparison.

Cellulose Nanofibrils (CNFs), highly present in nature, can be used as building blocks for future sustainable materials, including strong and stiff filaments. A rheo-optical flow-stop technique is used to conduct experiments to characterize the CNFs by studying Brownian dynamics through the CNFs' birefringence decay after stop. As the experiments produce large quantities of data, we reduce their dimensionality using Principal Component Analysis (PCA) and exploit the possibility of visualizing the reduced data in two ways. First, we plot the principal components (PCs) as time series, and by training LSTM networks assigned for each PC time series with the data before the flow stop, we predict the behavior after the flow stop (Bragone et al., 2024). Second, we plot the first PCs against each other to create clusters that give information about the different CNF materials and concentrations. Our approach aims at classifying the CNF materials to varying concentrations by applying unsupervised machine learning algorithms, such as k-means and Gaussian Mixture Models (GMMs). Finally, we analyze the Autocorrelation Function (ACF) and the Partial Autocorrelation Function (PACF) of the first principal component, detecting seasonality in lower concentrations.

Recent trends and advancements in including more diverse and heterogeneous hardware in High-Performance Computing (HPC) are challenging scientific software developers in their pursuit of efficient numerical methods with sustained performance across a diverse set of platforms. As a result, researchers are today forced to re-factor their codes to leverage these powerful new heterogeneous systems. We present our design considerations of Neko—a portable framework for high-fidelity spectral element flow simulations. Unlike prior works, Neko adopts a modern object-oriented Fortran 2008 approach, allowing multi-tier abstractions of the solver stack and facilitating various hardware backends ranging from general-purpose processors, accelerators down to exotic vector processors and Field-Programmable Gate Arrays (FPGAs). Focusing on the performance and portability of Neko, we describe the framework's device abstraction layer managing device memory, data transfer and kernel launches from Fortran, allowing for a solver written in a hardware-neutral yet performant way. Accelerator-specific optimizations are also discussed, with auto-tuning of key kernels and various communication strategies using device-aware MPI. Finally, we present performance measurements on a wide range of computing platforms, including the EuroHPC pre-exascale system LUMI, where Neko achieves excellent parallel efficiency for a large direct numerical simulation (DNS) of turbulent fluid flow using up to 80% of the entire LUMI supercomputer.