Graphics Processing Unit Acceleration Research Articles

Comparative study of CUDA-based parallel programming in C and Python for GPU acceleration of the 4th order Runge-Kutta method

In this paper, a comparative study is presented on the application of General-purpose Computing on Graphics Processing Units for solving the point reactor kinetics equations through the utilization of the 4th Order Runge-Kutta (RK4) method using the programming languages C and Python. Sequential and parallel algorithms of the RK4 method were developed in C/C++ and Python, with parallel algorithms specifically designed to operate on Graphics Processing Units (GPUs) utilizing the NVIDIA Compute Unified Device Architecture (CUDA) as the programming platform. As an experiment, the execution time for the sequential and parallel algorithms were compared for a reactivity value of ρ = 0.003 and a simulation time of t = 100 s. The parallel C and Python algorithms achieved, respectively, speedups of 9.33 and 409.7 when comparing the execution time on the best GPU utilized (RTX 3070Ti) with the best CPU (3600XT), while still maintaining numerical precision.

GPU acceleration of many‐body perturbation theory methods in MOLGW with OpenACC

AbstractQuasiparticle self‐consistent many‐body perturbation theory (MBPT) methods that update both eigenvalues and eigenvectors can calculate the excited‐state properties of molecular systems without depending on the choice of starting points. However, those methods are computationally intensive even on modern multi‐core central processing units (CPUs) and thus typically limited to small systems. Many‐core accelerators such as graphics processing units (GPUs) may be able to boost the performance of those methods without losing accuracy, making starting‐point‐independent MBPT methods applicable to large systems. Here, we GPU accelerate MOLGW, a Gaussian‐based MBPT code for molecules, with open accelerators (OpenACC) and achieve speedups of up to over 32 open multi‐processing (OpenMP) CPU threads.

High-Resolution Image Processing and Spatiotemporal Data Transmission System Based on GPU Acceleration

With the development of information technology and the increasing demand for data processing, the serial mode of the central processing unit (CPU) is difficult to efficiently transmit large-scale spatiotemporal data, and the processing effect for high-resolution images is not good. This paper designed a high-resolution image processing and spatiotemporal data transmission system based on graphics processing unit (GPU) acceleration to improve the processing efficiency of large-scale spatiotemporal data. In this paper, traffic spatiotemporal data was taken as an example for analysis. Large-scale traffic image data was collected by road monitoring equipment, and image compression was performed on the collected image. Fourier transform was used to eliminate image data redundancy, and GPU-accelerated parallel processing was used to achieve fast image defogging and data transmission. This paper selected 2TB of traffic spatiotemporal data with image resolutions of 540P, 720P, 1080P, 1440P, and 2160P. GPU acceleration was performed using the Compute Unified Device Architecture (CUDA). In images with a resolution of 2160P, the processing time for CPU and GPU acceleration was 2900ms and 28ms, respectively, with an acceleration ratio of 103.6. A high-resolution image processing and spatiotemporal data transmission system based on GPU acceleration can improve the efficiency of traffic spatiotemporal data processing and have excellent concurrent processing capabilities.

Efficient periodic resolution-of-the-identity Hartree-Fock exchange method with k-point sampling and Gaussian basis sets.

Simulations of condensed matter systems at the hybrid density functional theory level pose significant computational challenges. The elevated costs arise from the non-local nature of the Hartree-Fock exchange (HFX) in conjunction with the necessity to approach the thermodynamic limit. In this work, we address these issues with the development of a new efficient method for the calculation of HFX in periodic systems, employing k-point sampling. We rely on a local atom-specific resolution-of-the-identity scheme, the use of atom-centered Gaussian type orbitals, and the truncation of the Coulomb interaction to limit computational complexity. Our real-space approach exhibits a scaling that is, at worst, linear with the number of k-points. Issues related to basis set diffuseness are effectively addressed through the auxiliary density matrix method. We report the implementation in the CP2K software package, as well as accuracy and performance benchmarks. This method demonstrates excellent agreement with equivalent Γ-point supercell calculations in terms of relative energies and nuclear gradients. Good strong and weak scaling performances, as well as graphics processing unit (GPU) acceleration, make this implementation a promising candidate for high-performance computing.

An efficient quantum circuit implementation of Shor’s algorithm for GPU accelerated simulation

In this study, we introduce a novel implementation of Shor’s algorithm specifically designed for the Graphics Processing Unit (GPU) acceleration framework. Our focus lies on achieving efficient execution of the modular multiplication circuit through GPU simulation. To seamlessly integrate our design into the PyQPanda library framework, we made necessary modifications, making a deliberate trade-off by sacrificing a small number of quantum resources to leverage the advantages of GPU acceleration. Subsequently, we conducted simulations and rigorously validated the functionality of our circuit using the PyQPanda library, resulting in a significant speedup compared to a central processing unit-only mode.

GPU-accelerated lung CT segmentation based on level sets and texture analysis

This paper presents a novel semi-automatic method for lung segmentation in thoracic CT datasets. The fully three-dimensional algorithm is based on a level set representation of an active surface and integrates texture features to improve its robustness. The method’s performance is enhanced by the graphics processing unit (GPU) acceleration. The segmentation process starts with a manual initialisation of 2D contours on a few representative slices of the analysed volume. Next, the starting regions for the active surface are generated according to the probability maps of texture features. The active surface is then evolved to give the final segmentation result. The recent implementation employs features based on grey-level co-occurrence matrices and Gabor filters. The algorithm was evaluated on real medical imaging data from the LCTCS 2017 challenge. The results were also compared with the outcomes of other segmentation methods. The proposed approach provided high segmentation accuracy while offering very competitive performance.

Development of GPU‐based matrix‐free strategies for large‐scale elastoplasticity analysis using conjugate gradient solver

AbstractIn recent years, matrix‐free conjugate gradient (CG) solvers with graphics processing unit (GPU) acceleration have been effectively used to reduce the execution timings of finite element method (FEM)‐based engineering simulations. However, there is not much in the literature that discusses the application of matrix‐free CG solvers for elastoplasticity. The primary challenge is the presence of both elastic and plastic states, which introduce branching issues in the parallel computation of sparse matrix‐vector multiplication (SpMV). The current work proposes an efficient split kernel strategy for elastoplasticity that segregates elements in elastic and plastic zones and allows the application of standard GPU‐based matrix‐free SpMV strategies in the literature to elastoplastic simulation. The proposed strategy avoids branching issues, facilitates coalesced memory access, allows efficient usage of on‐chip memory, and uses minimum storage to realize large‐scale simulation on a GPU with limited memory. We also propose matrix‐free SpMV strategies for GPU implementation based on an element‐by‐element technique that makes efficient use of memory hierarchy available in a GPU. The computational efficiency of the proposed strategies is demonstrated over three large‐scale benchmark examples from elastoplasticity, and the performance results are compared with GPU optimized node‐based and degrees of freedom (DOF)‐based matrix‐free strategies. The results show that the splitting of computation is most suitable for low plasticity problems, where most of the matrix‐free strategies show significant speedups with respect to single kernel strategy for elastoplasticity. The proposed element‐by‐element strategy using node‐wise thread allocation is found to have the best performance, achieving up to 7.16 and 3.35 speedup over node‐based and DOF‐based strategy, respectively. For problems with higher amounts of plasticity, the proposed strategies perform slightly better and achieve modest speedups due to advantages in the initial stages of Newton iterations. In terms of wall‐clock timings, the proposed matrix‐free solver is found to be up to 2 faster than GPU optimized assembly‐based elastoplasticity solver.

Design and Development of a CCSDS 131.2-B Software-Defined Radio Receiver Based on Graphics Processing Unit Accelerators

In recent years, the number of Earth Observation missions has been exponentially increasing. Satellites dedicated to these missions usually embark with payloads that produce large amount of data and that need to be transmitted towards ground stations, in time-limited windows. Moreover, the noisy nature of the link between satellites and ground stations makes it hard to achieve reliable communication. To address these problems, a standard for a flexible advanced coding and modulation scheme for high-rate telemetry applications has been defined by the Consultative Committee for Space Data Systems (CCSDS). The defined standard, referred to as CCSDS 131.2-B, makes use of Serially Concatenated Convolutional Codes (SCCC) based on 27 ModCods to optimize transmission quality. A limiting factor in the adoption of this standard is represented by the complexity and the cost of the hardware required for developing high-performance receivers. In the last decade, the performance of software has grown due to the advancement of general-purpose processing hardware, leading to the development of many high-performance software systems even in the telecommunication sector. These are commonly referred to as Software-Defined Radio (SDR), indicating a radio communication system in which components that are usually implemented in hardware, by means of FPGAs or ASICs, are instead implemented in software, offering many advantages such as flexibility, modularity, extensibility, cheaper maintenance, and cost saving. This paper proposes the development of an SDR based on NVIDIA Graphics Processing Units (GPU) for implementing the receiver end of the CCSDS 131.2-B standard. At first, a brief description of the CCSDS 131.2-B standard is given, focusing on the architecture of the transmitter and receiver sides. Then, the receiver architecture is shown, giving an overview of its functional blocks and of the implementation choices made to optimize the processing of the signal, especially for the SCCC Decoder. Finally, the performance of the system is analyzed in terms of data-rate and error correction and compared with other SW systems to highlight the achieved improvements. The presented system has been demonstrated to be a perfect solution for CCSDS 131.2-B-compliant device testing and for its use in science missions, providing a valid low-cost alternative with respect to the state-of-the-art HW receivers.

Optimizing Network Function Virtualization: A Comprehensive Performance Analysis of Hardware- Accelerated Solutions

Network Function Virtualization (NFV) heralds a transformative shift in the realm of network infrastructure, moving away from the rigid confines of specialised hardware appliances towards nimble, software-driven solutions that operate on off-the-shelf hardware. This transition holds the tantalising promise of substantial reductions in both capital and operational expenditures. However, the deployment and orchestration of virtual network functions pose formidable challenges, particularly concerning the optimisation of performance and judicious allocation of resources. The significance of this research lies in its targeted approach to the pressing necessity for a thorough performance evaluation of diverse NFV implementations. By furnishing organisations with vital insights for strategic infrastructure planning, this study embarks on a comparative journey through various NFV architectures, juxtaposed with traditional hardware-centric solutions. It delves into the intricacies of practical deployment considerations while rigorously examining critical performance metrics, aiming to illuminate the path forward in this rapidly evolving landscape. The research methodology utilises the EDAS (Evaluation based on Distance from Average Solution) approach, a nuanced and multifaceted decisionmaking framework adept at assessing alternatives through their proximity to average solutions. This investigation delves into four pivotal evaluation parameters: scalability, resource utilisation, latency, and energy consumption. In total, five alternative implementations are scrutinised: traditional hardware, NFV with software acceleration, NFV leveraging GPU acceleration, NFV employing FPGA acceleration, and cloud-based NFV.The findings reveal that NFV with GPU acceleration emerges as the frontrunner, attaining an impressive appraisal score of 0.9626, indicative of its exceptional performance across all evaluated metrics. Hot on its heels, NFV with FPGA acceleration secures a noteworthy score of 0.9474, particularly excelling in the domain of resource utilisation. In contrast, NFV with software acceleration (0.7614) and cloud-based NFV (0.4675) exhibit moderate efficacy, while traditional hardware languishes at the bottom of the hierarchy, registering a dismal score of 0.0000.The findings illuminate that hardwareaccelerated Network Functions Virtualization (NFV) solutions, especially those harnessing the power of Graphics Processing Unit (GPU) technology, deliver an optimal and nuanced performance tailored to the demands of contemporary networking landscapes. This research significantly enriches the discourse on NFV implementation, shedding light on the intricate trade-offs involved, while offering empirical guidance for organisations making the pivotal shift from traditional hardware infrastructures to virtualised network functions. The results underscore that, although hardware acceleration markedly boosts NFV performance, the selection of implementation strategies must be meticulously aligned with the unique needs and operational constraints of each organisation. Keywords: Network Function Virtualization (NFV), Hardware Acceleration, Performance Benchmarking, EDAS Methodology, Resource Optimization and Virtualized Network Infrastructure.

Study on the flooding characteristics of a deep-water submarine based on δ plus-smoothed particle hydrodynamics method and graphic processing units acceleration

The process of a damaged ship flooding is a complicated free surface flow problem. There is a complex coupling effect between the ship cabin and the flow inside and outside of the cabin. In this paper, a GPU (graphic processing unit)-δ+-SPH (smoothed particle hydrodynamics) numerical model for the cabin flooding in deep-water environments is developed based on GPU parallel acceleration technology and Nvidia's CUDA (compute unified device architecture). First, the computational accuracy and efficiency of this numerical model are verified by experiments results on the water flooding of a simple damaged cabin model. Furthermore, the flooding characteristics of a submarine cabin are analyzed, considering different numbers of damaged cabins, depths, and opening positions. Finally, the progressive flooding and the dynamic response characteristics of a full-scale submarine model are investigated. The results show that the process of progressive flooding in a submarine cabin is characterized by its rapidity and intensity. Different factors, for example, damaged cabin numbers, cabin depths, and opening positions, have great influences on the process of flooding and the motion of the submarine cabin. This study can offer valuable technical assistance in the post-damage remediation process.

Classical molecular dynamics simulations of CsPbBr3 perovskite quantum dots embedded sodium borosilicate glasses

We have simulated the configuration of perovskite CsPbBr3 quantum dot (QD), sodium borosilicate (SBS) glass matrix, and the combination of these two, namely CsPbBr3 QDs embedded SBS glass. We delineate a microscope porthole containing ∼100,000 atoms and with a dimension of ∼30 nm × 30 nm × 1 nm, using the classical molecular dynamics (CMD) benefiting from graphics processing unit (GPU) acceleration. We quantify the structural properties of glass matrix and determined the crystallization of QDs, in excellent agreement with experimental observations. Based on analysis the of boron coordination, structural undulation influenced by the K value (KSiO2/B2O3), we demonstrate the significant compositional repulsion in the combination derived from electronegative nonmetal elements, namely oxygen (O) and bromine (Br). As the K value decreases, indicating an increase in the proportion of B2O3 and a decrease in the proportion of SiO2, the [BO3]/[BO4] ratio increases, resulting in a glass matrix with a stronger repulsive 'anionic sea' and a more relaxed encapsulated environment for CsPbBr3 QD. This creates greater repulsion for the allocation of oxide and bromide, and a more suitable space for the crystallization of CsPbBr3 clusters, which in turn regulates the formation of CsPbBr3 QDs. These results constructively extend our understanding of the microstructure of CsPbBr3 QDs embedded SBS glasses, and provide valuable insights for the engineering of CsPbBr3 QDs in SBS glasses on high luminescent CsPbBr3 QDs embedded glasses.

Optimizing MRI Data Processing by exploiting GPU Acceleration for Efficient Image Analysis and Reconstruction

Magnetic Resonance Imaging (MRI) is a non-invasive medical imaging modality, offering detailed anatomical insights without ionizing radiation The advent of Graphics Processing Units (GPUs) has stimulated a paradigm shift, propelling MRI techniques to new frontiers. Through parallel processing capabilities, GPUs have expedited real-time imaging, complex image reconstruction, noise reduction, and intricate data analysis. This paper provides an extensive survey on the GPUs based MRI techniques and its synergistic impact on core methodologies such as Diffusion Tensor Imaging (DTI) and Functional MRI (fMRI). Through parallel processing and frameworks like CUDA and OpenCL, GPUs have overcome computational hurdles in MRI data processing. Challenges like memory constraints and data transfer bottlenecks are addressed through hybrid CPU-GPU strategies and algorithmic enhancements. The integration of GPUs yields faster scans, enhanced image quality, and real-time insights, benefiting patient care and accelerating medical research. Considering the ethical issues regarding patient data privacy and algorithmic fairness, GPUs' potential in MRI research and development is evident. This paper concludes by looking ahead about the future of GPU based MRI, urging further exploration to uncover new possibilities and shape a transformative path for the future of medical imaging.

BPMF: A Backprojection and Matched-Filtering Workflow for Automated Earthquake Detection and Location

Abstract We introduce BPMF (backprojection and matched filtering)—a complete and fully automated workflow designed for earthquake detection and location, and distributed in a Python package. This workflow enables the creation of comprehensive earthquake catalogs with low magnitudes of completeness using no or little prior knowledge of the study region. BPMF uses the seismic wavefield backprojection method to construct an initial earthquake catalog that is then densified with matched filtering. BPMF integrates recent machine learning tools to complement physics-based techniques, and improve the detection and location of earthquakes. In particular, BPMF offers a flexible framework in which machine learning detectors and backprojection can be harmoniously combined, effectively transforming single-station detectors into multistation detectors. The modularity of BPMF grants users the ability to control the contribution of machine learning tools within the workflow. The computation-intensive tasks (backprojection and matched filtering) are executed with C and CUDA-C routines wrapped in Python code. This leveraging of low-level, fast programming languages and graphic processing unit acceleration enables BPMF to efficiently handle large datasets. Here, we first summarize the methodology and describe the application programming interface. We then illustrate BPMF’s capabilities to characterize microseismicity with a 10 yr long application in the Ridgecrest, California area. Finally, we discuss the workflow’s runtime scaling with numerical resources and its versatility across various tectonic environments and different problems.

A single-phase GPU-accelerated surface tension model using SPH

This paper presents an accelerated single-phase surface tension smoothed particle hydrodynamics (SPH) solver developed to run entirely on a graphics processing unit (GPU) capable of simulating millions of particles in three dimensions on a single GPU. The single-phase surface tension model is augmented with a contact line force to improve the prediction of the physics at the liquid-solid contact point. The surface tension model uses the modified dynamic boundary condition (mDBC) to impose no-slip conditions at the wall boundary. To enable simulations with millions of particles, the single-phase surface tension model has been implemented in the open-source SPH code DualSPHysics to exploit the GPU acceleration, paying special attention to the size of the kernel support and integration of the neighbour lists. The new scheme is validated using 2-D and 3-D test cases including drop deformation, drop oscillation, Rayleigh-Plateau instability and surface contact angles. The results show a good agreement with the analytical solutions with a standard spatial convergence behaviour. Profiling the new surface tension solver shows an additional computational complexity. The performance analysis shows that the new code has a speed up of up two orders of magnitude (x70-80) compared to the CPU-only code. Profiling the new CUDA kernels shows they have the near identical performance metrics with the main CUDA kernels in the original DualSPHysics solver.

A 1D-2D dynamic bidirectional coupling model for high-resolution simulation of urban water environments based on GPU acceleration techniques

Urban water pollution is an important issue of global concern. An integrated model that can be employed to efficiently and accurately simulate the whole system of urban surfaces, underground drainage systems and river and lake water environments is an important tool for comprehensively treating urban water pollution. Therefore, we developed a 1D-2D dynamic bidirectional coupling model for high-resolution simulation of the entire urban water environment (GAST-GCSE) comprising the graphics processing unit (GPU)-accelerated surface water flow and transport (GAST) model and the storm water management model (SWMM). The SWMM and GAST model were used for coupled simulation of hydrological and hydrodynamic quantities and water quality in 1D and 2D regions, respectively. The time steps of the two models are the same. Compute Unified Device Architecture (CUDA) parallel architecture programming and advanced model algorithms were adopted to improve the simulation accuracy and efficiency of the GAST model. The two models were dynamically coupled by calling the dynamic link library (DLL) in the SWMM and adding external functions. Two test cases were considered to verify the performance of the proposed model. The results showed that the proposed model provides high simulation accuracy. Compared with the results obtained by commercial software and theoretical values, the relative errors of the water quantity and water quality simulation results were less than 3%. The whole water environment of the main urban area of Changzhi city in North China was evaluated by the GAST-GCSE model, and the simulation results were compared with monitoring data and pure SWMM simulation results. The simulation errors at three waterlogging points were less than 14%, and the Nash efficiency coefficients (NSE) of the NH4+-N concentration in the three river sections were 0.77, 0.56 and 0.64, respectively. Notably, the model needed only 4.39 h to simulate rainfall production and confluence on a 2D surface, a river flood in complex terrain and its accompanying pollutant evolution process involving 3,849,428 cells over 4 h. This study provides new insights, ideas and tools for urban water environment simulation and change mechanism study.

GPU acceleration of conjugate gradient method obtaining Green's function for transport-property calculation

We present a graphics processing unit (GPU) acceleration procedure for the transport-property calculation of the RSPACE code, which uses density functional theory and the real-space finite-difference method. Porting the RSPACE code to run on a GPU using Open Accelerator and libraries results in the improvement of the computational speed and efficiency compared with a central processing unit. Furthermore, we achieved a speed up of ∼ 4x compared with a single GPU and effective processing in a multi-GPU environment by parallelizing in a way that minimizes communication and synchronization. Our GPU-acceleration procedure is demonstrated by the calculation of electron-transport properties of (4,4) carbon nanotubes (CNTs) with a 5-8-5 defect for which the calculations are suitable for parallel processing on GPUs. It is found that the information loss is strongly affected by the defect geometry when electron waves propagating in CNTs are utilized as the information transfer signal.

Development of scalable GPU-based direct whole-core depletion calculation methods

GPU (Graphical Processing Unit) acceleration methods are introduced to the depletion solver and the cross section (XS) calculation routines of the direct whole core calculation code, nTRACER, for scalable and superior computing performance. Extensive GPU acceleration besides transport solvers is necessary to resolve the performance degradation in depletion calculations caused by the newly generated nuclides in depleted fuel regions and the poor scalability. A matrix exponential solver employing the Gauss-Seidel-based Chebyshev rational approximate method is implemented to reduce branch divergence and load imbalance in GPU computing in the solution of the Bateman equation. A non-zero element major storage scheme is developed to optimize memory access efficiency on GPUs by exploiting the same sparsity pattern of the depletion matrix in all the depletion regions. XS data are serialized for effective massive parallelization of XS calculation on GPUs. A block decomposition scheme for XS calculation is implemented to reduce the global memory requirement due to the limited memory capacity of commercial GPUs. It is demonstrated that nTRACER can complete a direct whole core depletion calculation for a realistic core in a few hours on a moderate-sized GPU-based heterogeneous computing platform after extensive GPU porting to all the essential routines.

Particle-resolved thermal lattice Boltzmann simulation using OpenACC on multi-GPUs

We utilize the Open Accelerator (OpenACC) approach for graphics processing unit (GPU) accelerated particle-resolved thermal lattice Boltzmann (LB) simulation. We adopt the momentum-exchange method to calculate fluid-particle interactions to preserve the simplicity of the LB method. To address load imbalance issues, we extend the indirect addressing method to collect fluid-particle link information at each timestep and store indices of fluid-particle link in a fixed index array. We simulate the sedimentation of 4,800 hot particles in cold fluids with a domain size of 40002, and the simulation achieves 1750 million lattice updates per second (MLUPS) on a single GPU. Furthermore, we implement a hybrid OpenACC and message passing interface (MPI) approach for multi-GPU accelerated simulation. This approach incorporates four optimization strategies, including building domain lists, utilizing request-answer communication, overlapping communications with computations, and executing computation tasks concurrently. By reducing data communication between GPUs, hiding communication latency through overlapping computation, and increasing the utilization of GPU resources, we achieve improved performance, reaching 10846 MLUPS using 8 GPUs. Our results demonstrate that the OpenACC-based GPU acceleration is promising for particle-resolved thermal lattice Boltzmann simulation.

Speeding up the GENGA N-body integrator on consumer-grade graphics cards

Context. Graphics processing unit (GPU) computing has become popular due to the enormous calculation potential that can be harvested from a single card. The N-body integrator Gravitational ENcounters with GPU Acceleration (GENGA) is built to harvest the computing power from such cards, but it suffers a severe performance penalty on consumer-grade Nvidia GPUs due to their artificially truncated double precision performance. Aims. We aim to speed up GENGA on consumer-grade cards by harvesting their high single-precision performance. Methods. We modified GENGA to have the option to compute the mutual long-distance forces between bodies in single precision and tested this with five experiments. First, we ran a high number of simulations with similar initial conditions of on average 6600 fully self-gravitating planetesimals in both single and double precision to establish whether the outcomes were statistically different. These simulations were run on Tesla K20 cards. We supplemented this test with simulations that (i) began with a mixture of planetesimals and planetary embryos, (ii) planetesimal-driven giant planet migration, and (iii) terrestrial planet formation with a dissipating gas disc. All of these simulations served to determine the accuracy of energy and angular momentum conservation under various scenarios with single and double precision forces. Second, we ran the same simulation beginning with 40 000 self-gravitating planetesimals using both single and double precision forces on a variety of consumer-grade and Tesla GPUs to measure the performance boost of computing the long-range forces in single precision. Results. We find that there are no statistical differences when simulations are run with the gravitational forces in single or double precision that can be attributed to the force prescription rather than stochastic effects. The accumulations in uncertainty in energy are almost identical when running with single or double precision long-range forces. However, the uncertainty in the angular momentum using single rather than double precision long-range forces is about two orders of magnitude greater, but still very low. Running the simulations in single precision on consumer-grade cards decreases running time by a factor of three and becomes within a factor of three of a Tesla A100 GPU. Additional tuning speeds up the simulation by a factor of two across all types of cards. Conclusions. The option to compute the long-range forces in single precision in GENGA when using consumer-grade GPUs dramatically improves performance at a little penalty to accuracy. There is an additional environmental benefit because it reduces energy usage.

Wind Tunnel and Grid Resolution Effects in Large-Eddy Simulations of the High-Lift Common Research Model

This paper describes the application of a compressible large-eddy simulation (LES) solver to the flow over the High-Lift Common Research Model (CRM-HL) in landing configuration, a complex external aerodynamic flow configuration with deployed slats, flaps, a flow-through nacelle, and the associated brackets/fairings on the high-lift devices. The bulk Mach number is (0.2) and the mean-aerodynamic-chord-based Reynolds number is typical of a wind tunnel experiment (5.49E6). A key development in these simulations relative to previous work is that this work serves to establish robustness of LES methods to Reynolds number and aircraft configuration. Previous studies focused on the Japan Aerospace Exploration Agency Standard Model, which featured a nearly 3× lower Reynolds number than the present CRM-HL investigation and less aggressive wing/slat leading-edge curvature than the CRM-HL geometry, which led to lower leading-edge pressure suction peak magnitudes, both of which led to less stringent grid resolution requirements. In these LES simulations, an algebraic equilibrium wall modeling approach is employed along with a dynamic implementation of the Smagorinsky subgrid-scale model. The calculations are carried out in both a free air setting and one that includes the wind tunnel facility at seven angles of attack at five grid resolution levels, ranging from ≈10 to 1500 million control volumes. In free air, the solutions show decreasing sensitivity to the grid with each successive refinement level and systematically approach the experimental lift coefficient data as the grid is refined, with the 1.5 billion control volume case showing excellent agreement with the corrected experimental data. The simulations in both free air and in the wind tunnel predict a stall mechanism featuring a large inboard juncture stall and a nose-down break in the pitching moment curve, both of which agree with the experimental observations. The accuracy of the simulations is assessed via comparisons of integrated forces/moments, surface pressures, and surface skin friction visualizations. Graphics processing unit (GPU) acceleration of the charLES solver results in tractable turnaround times that make LES a useful tool in the aerospace industry design cycle. Recent GPU acceleration of the flow solver has made LES solutions that are highly accurate in lift/drag/moment for relevant high-lift aircraft flows achievable within about 5 h of wall time on 600 GPU cores.