Single Graphics Processing Units Research Articles

O-260 Identifying seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE) using deep learning

Abstract Study question Is it possible to utilize artificial intelligence (AI) in the selection of seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE)? Summary answer AI algorithm was able to segment seminiferous tubules deemed to be good candidates for sperm retrieval with IoU of 0.746 and F1 score of 72.5%. What is known already M-TESE is an advanced surgical procedure reserved for sperm retrieval in men with non-obstructive azoospermia (NOA). Meta-analysis have shown M-TESE might be superior in relation to sperm retrieval rates and postsurgical complications when compared to conventional techniques. Limitations of the technique are the relatively long learning curve for the surgeons and the longer intraoperative time required to identify the tubules with active spermatogenesis. Convolutional neural networks (CNNs) have been shown to successfully identify areas of interest in medical images such as CT and MRI scans. The possibility of utilizing trained CNNs in M-TESE procedures has so far not been studied. Study design, size, duration This is a cross sectional study analyzing image data gathered during M-TESE procedures including the first five men enrolled in a prospective study designed specifically to test the utilization of AI in the treatment of NOA. All participants signed written informed consent. Images were collected intraoperatively with low and high resolution cameras according to predefined protocol. Participants/materials, setting, methods CNN model with down and upsampling architecture was trained using patch-based technique with corresponding labelled truth image pairs based on combination of semi-automated hue saturation value thresholding and manual segmentation of seminiferous tubules. Tubules were allocated into two different classes by the operating surgeon according to the visual perception of their morphology as type 1 (i.e. larger, opaque, good candidates for micro dissection) and type 2 (i.e. sclerotic or atrophic; unlikely to harbor active spermatogenesis). Main results and the role of chance CNN models were trained on 1809 patches with corresponding ground truth segmented images. Two algorithms were tested: the first was trained solely to detect and semantically distinguish type 1 seminiferous tubules. The second was trained to semantically segment the major anatomical structures: tunica albuginea, testicular parenchyma, blood vessels; lobules and the two types of seminiferous tubules describe above. Validation was performed on 988 image patches not previously shown to the algorithm. After grid search for optimal learning rate and augmentation the first algorithm was able to segment type 1 tubules with F1 score of 72.5% and Jaccard Index (IoU) of 0.746. Training took 856 minutes on 48 core high performance computer (HPC) equipped with single graphics processing unit (GPU). Same computational environment was used in the training of the second algorithm. This model was able to segment anatomical structures such as parenchyma (IoU=0.732) and testicular lobules (IoU=0.683) but performed sub optimally on the clinically relevant type 1 tubules (IoU=0.432). The first trained algorithm was able to segment a single high resolution surgical image in 3.4 seconds on an HPC. However, the same procedure took 5.5 minutes on standard desktop computer with 8 core Intel processor without GPU. Limitations, reasons for caution The number of patients is relatively small and therefore more training data would be needed to achieve robust performance. Training data were labelled by single surgeon limiting the generalizability and possibly biasing the selection process. The need of HPC environment for real time segmentation can limit the clinical use. Wider implications of the findings This pilot study shows the potential of AI to help in choosing suitable tubules for micro dissection, shortening the learning curve for young andrological surgeons. The access to cluster based computation is getting easier by the day and can soon allow instant intraoperative AI segmentation, thus shortening the surgical procedure. Trial registration number NA

Do we really need machine learning interatomic potentials for modeling amorphous metal oxides? Case study on amorphous alumina by recycling an existing ab initio database

In this study, we critically evaluate the performance of various interatomic potentials/force fields against a benchmark ab initio database for bulk amorphous alumina. The interatomic potentials tested in this work include all major fixed charge and variable charge models developed to date for alumina. Additionally, we introduce a novel machine learning interatomic potential constructed using the NequIP framework based on graph neural networks. Our findings reveal that the fixed-charge potential developed by Matsui and coworkers offers the most optimal balance between computational efficiency and agreement with ab initio data for stoichiometric alumina. Such balance cannot be provided by machine learning potentials when comparing performance with Matsui potential on the same computing infrastructure using a single Graphical Processing Unit. For non-stoichiometric alumina, the variable charge potentials, in particular ReaxFF, exhibit an impressive concordance with density functional theory calculations. However, our NequIP potentials trained on a small fraction of the ab initio database easily surpass ReaxFF in terms of both accuracy and computational performance. This is achieved without large overhead in terms of potential fitting and fine-tuning, often associated with the classical potential development process as well as training of standard deep neural network potentials, thus advocating for the use of data-efficient machine learning potentials like NequIP for complex cases of non-stoichiometric amorphous oxides.

A GPU-accelerated computational fluid dynamics solver for assessing shear-driven indoor airflow and virus transmission by scale-resolved simulations

We explore the applicability of MATLAB for 3D computational fluid dynamics (CFD) of shear-driven indoor airflows. A new scale-resolving, large-eddy simulation (LES) solver titled DNSLABIB is proposed for MATLAB utilizing graphics processing units (GPUs). In DNSLABIB, the finite difference method is applied for the convection and diffusion terms while a Poisson equation solver based on the fast Fourier transform (FFT) is employed for the pressure. The immersed boundary method (IBM) for Cartesian grids is proposed to model solid walls and objects, doorways, and air ducts by binary masking of the solid/fluid domains. The solver is validated in two canonical reference cases and against experimental data. Then, we demonstrate the validity of DNSLABIB in a room geometry by comparing the results against another CFD software (OpenFOAM). Next, we demonstrate the solver performance in several isothermal indoor ventilation configurations and the implications of the results are discussed in the context of airborne transmission of COVID-19. The novel numerical findings using the new CFD solver are as follows. First, a linear scaling of DNSLABIB is demonstrated and a speed-up by a factor of 3-4 is also noted in comparison to similar OpenFOAM simulations. Second, ventilation in three different indoor geometries are studied at both low (0.1 m/s) and high (1 m/s) airflow rates corresponding to Re=5000 and Re=50000. An analysis of the indoor CO2 concentration is carried out as the room is emptied from stale, high CO2 content air. We estimate the air changes per hour (ACH) values for three different room geometries and show that the numerical estimates from 3D CFD simulations differ by 80%–150% (Re=50000) and 75%–140% (Re=5000) from the theoretical ACH value based on the perfect mixing assumption. Third, the analysis of the CO2 probability distributions (PDFs) indicates a relatively non-uniform distribution of fresh air indoors. Fourth, utilizing a time-dependent Wells-Riley analysis, an example is provided on the growth of the cumulative infection risk which is shown to reduce rapidly after the ventilation is started. The average infection risk is shown to reduce by a factor of 2 for lower ventilation rates (ACH=3.4-6.3) and 10 for the higher ventilation rates (ACH=37-64). Finally, we utilize the new solver to comment on respiratory particle transport indoors. A key contribution of the paper is to provide an efficient, GPU compatible CFD solver environment enabling scale-resolved simulations (LES/DNS) of airflow in large indoor geometries on a single GPU designed for high-performance computing. The demonstrated efficacy of MATLAB for GPU computing indicates a high potential of DNSLABIB for various future developments on airflow prediction.

Iterative Reconstruction of Micro Computed Tomography Scans Using Multiple Heterogeneous GPUs.

Graphics processing units (GPUs) facilitate massive parallelism and high-capacity storage, and thus are suitable for the iterative reconstruction of ultrahigh-resolution micro computed tomography (CT) scans by on-the-fly system matrix (OTFSM) calculation using ordered subsets expectation maximization (OSEM). We propose a finite state automaton (FSA) method that facilitates iterative reconstruction using a heterogeneous multi-GPU platform through parallelizing the matrix calculations derived from a ray tracing system of ordered subsets. The FSAs perform flow control for parallel threading of the heterogeneous GPUs, which minimizes the latency of launching ordered-subsets tasks, reduces the data transfer between the main system memory and local GPU memory, and solves the memory-bound of a single GPU. In the experiments, we compared the operation efficiency of OS-MLTR for three reconstruction environments. The heterogeneous multiple GPUs with job queues for high throughput calculation speed is up to five times faster than the single GPU environment, and that speed up is nine times faster than the heterogeneous multiple GPUs with the FIFO queues of the device scheduling control. Eventually, we proposed an event-triggered FSA method for iterative reconstruction using multiple heterogeneous GPUs that solves the memory-bound issue of a single GPU at ultrahigh resolutions, and the routines of the proposed method were successfully executed on each GPU simultaneously.

High-order thread-safe lattice Boltzmann model for high performance computing turbulent flow simulations

We present a highly optimized thread-safe lattice Boltzmann model in which the non-equilibrium part of the distribution function is locally reconstructed via recursivity of Hermite polynomials. Such a procedure allows the explicit incorporation of non-equilibrium moments of the distribution up to the order supported by the lattice. Thus, the proposed approach increases accuracy and stability at low viscosities without compromising performance and amenability to parallelization with respect to standard lattice Boltzmann models. The high-order thread-safe lattice Boltzmann is tested on two types of turbulent flows, namely, the turbulent channel flow at Reτ=180 and the axisymmetric turbulent jet at Re = 7000; it delivers results in excellent agreement with reference data [direct numerical simulations (DNS), theory, and experiments] and (a) achieves peak performance [∼5×1012 floating point operations (FLOP) per second and an arithmetic intensity of ∼7 FLOP/byte on a single graphic processing unit] by significantly reducing the memory footprint, (b) retains the algorithmic simplicity of standard lattice Boltzmann computing, and (c) allows to perform stable simulations at vanishingly low viscosities. Our findings open attractive prospects for high-performance simulations of realistic turbulent flows on GPU-based architectures. Such expectations are confirmed by excellent agreement among lattice Boltzmann, experimental, and DNS reference data.

Multi-Device Parallel MRI Reconstruction: Efficient Partitioning for Undersampled 5D Cardiac CINE.

Cardiac CINE, a form of dynamic cardiac MRI, is indispensable in the diagnosis and treatment of heart conditions, offering detailed visualization essential for the early detection of cardiac diseases. As the demand for higher-resolution images increases, so does the volume of data requiring processing, presenting significant computational challenges that can impede the efficiency of diagnostic imaging. Our research presents an approach that takes advantage of the computational power of multiple Graphics Processing Units (GPUs) to address these challenges. GPUs are devices capable of performing large volumes of computations in a short period, and have significantly improved the cardiac MRI reconstruction process, allowing images to be produced faster. The innovation of our work resides in utilizing a multi-device system capable of processing the substantial data volumes demanded by high-resolution, five-dimensional cardiac MRI. This system surpasses the memory capacity limitations of single GPUs by partitioning large datasets into smaller, manageable segments for parallel processing, thereby preserving image integrity and accelerating reconstruction times. Utilizing OpenCL technology, our system offers adaptability and cross-platform functionality, ensuring wider applicability. The proposed multi-device approach offers an advancement in medical imaging, accelerating the reconstruction process and facilitating faster and more effective cardiac health assessment.

Graphics-processing-unit-accelerated ice flow solver for unstructured meshes using the Shallow-Shelf Approximation (FastIceFlo v1.0.1)

Abstract. Ice-sheet flow models capable of accurately projecting their future mass balance constitute tools to improve flood risk assessment and assist sea-level rise mitigation associated with enhanced ice discharge. Some processes that need to be captured, such as grounding-line migration, require high spatial resolution (under the kilometer scale). Conventional ice flow models mainly execute on central processing units (CPUs), which feature limited parallel processing capabilities and peak memory bandwidth. This may hinder model scalability and result in long run times, requiring significant computational resources. As an alternative, graphics processing units (GPUs) are ideally suited for high spatial resolution, as the calculations can be performed concurrently by thousands of threads, processing most of the computational domain simultaneously. In this study, we combine a GPU-based approach with the pseudo-transient (PT) method, an accelerated iterative and matrix-free solution strategy, and investigate its performance for finite elements and unstructured meshes with application to two-dimensional (2-D) models of real glaciers at a regional scale. For both the Jakobshavn and Pine Island glacier models, the number of nonlinear PT iterations required to converge a given number of vertices (N) scales in the order of 𝒪(N1.2) or better. We further compare the performance of the PT CUDA C implementation with a standard finite-element CPU-based implementation using the price-to-performance metric. The price of a single Tesla V100 GPU is 1.5 times that of two Intel Xeon Gold 6140 CPUs. We expect a minimum speedup of at least 1.5 times to justify the Tesla V100 GPU price to performance. Our developments result in a GPU-based implementation that achieves this goal with a speedup beyond 1.5 times. This study represents a first step toward leveraging GPU processing power, enabling more accurate polar ice discharge predictions. The insights gained will benefit efforts to diminish spatial resolution constraints at higher computing performance. The higher computing performance will allow for ensembles of ice-sheet flow simulations to be run at the continental scale and higher resolution, a previously challenging task. The advances will further enable the quantification of model sensitivity to changes in upcoming climate forcings. These findings will significantly benefit process-oriented sea-level-projection studies over the coming decades.

PARALLEL AND DISTRIBUTED COMPUTING TECHNOLOGIES FOR AUTONOMOUS VEHICLE NAVIGATION

Context. Autonomous vehicles are becoming increasingly popular, and one of the important modern challenges in their development is ensuring their effective navigation in space and movement within designated lanes. This paper examines a method of spatial orientation for vehicles using computer vision and artificial neural networks. The research focused on the navigation system of an autonomous vehicle, which incorporates the use of modern distributed and parallel computing technologies.
 Objective. The aim of this work is to enhance modern autonomous vehicle navigation algorithms through parallel training of artificial neural networks and to determine the optimal combination of technologies and nodes of devices to increase speed and enable real-time decision-making capabilities in spatial navigation for autonomous vehicles.
 Method. The research establishes that the utilization of computer vision and neural networks for road lane segmentation proves to be an effective method for spatial orientation of autonomous vehicles. For multi-core computing systems, the application of parallel programming technology, OpenMP, for neural network training on processors with varying numbers of parallel threads increases the algorithm’s execution speed. However, the use of CUDA technology for neural network training on a graphics processing unit significantly enhances prediction speeds compared to OpenMP. Additionally, the feasibility of employing PyTorch Distributed Data Parallel (DDP) technology for training the neural network across multiple graphics processing units (nodes) simultaneously was explored. This approach further improved prediction execution times compared to using a single graphics processing unit.
 Results. An algorithm for training and prediction of an artificial neural network was developed using two independent nodes, each equipped with separate graphics processing units, and their synchronization for exchanging training results after each epoch, employing PyTorch Distributed Data Parallel (DDP) technology. This approach allows for scalable computations across a higher number of resources, significantly expediting the model training process.
 Conclusions. The conducted experiments have affirmed the effectiveness of the proposed algorithm, warranting the recommendation of this research for further advancement in autonomous vehicles and enhancement of their navigational capabilities. Notably, the research outcomes can find applications in various domains, encompassing automotive manufacturing, logistics, and urban transportation infrastructure. The obtained results are expected to assist future researchers in understanding the most efficient hardware and software resources to employ for implementing AI-based navigation systems in autonomous vehicles. Prospects for future investigations may encompass refining the accuracy of the proposed parallel algorithm without compromising its efficiency metrics. Furthermore, there is potential for experimental exploration of the proposed algorithm in more intricate practical scenarios of diverse nature and dimensions.

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.

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.

Accelerating micromagnetic and atomistic simulations using multiple GPUs

It is shown that micromagnetic and atomistic spin dynamics simulations can use multiple graphical processing units (GPUs) not only to reduce computation time but also to allow for a larger simulation size than is possible on a single GPU. While interactions that depend on neighboring spins, such as exchange interactions, may be implemented efficiently by transferring data between GPUs using halo regions or direct memory accesses, implementing the long-range demagnetizing interaction is the main difficulty in achieving good performance scaling, where the data transfer rate between GPUs is a significant bottleneck. A multi-GPU convolution algorithm is developed here, which relies on single-GPU FFTs executed in parallel. It is shown that even for micromagnetic simulations where the demagnetizing interaction computation time dominates, good performance scaling may be achieved, with speedup factors up to 1.8, 2.5, and 3.1, for two, three, and four GPUs, respectively. The code developed here can be used for any number of GPUs in parallel, with performance scaling strongly dependent on the inter-GPU data transfer rate and connection topology. This is further improved in micromagnetic simulations, which include a spin transport solver, obtaining speedup factors up to 1.96, 2.8, and 3.7, for two, three, and four GPUs, respectively. The best case scenario is obtained for atomistic simulations, where the demagnetizing interaction is implemented with spin-averaged cells. Using a single workstation with four GPUs, it is shown that atomistic spin dynamics simulations with up to 1 × 109 spins and atomistic Monte Carlo simulations with up to 2 × 109 spins are possible, with near-ideal performance scaling.

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.

A hybrid CPU/GPU method for Hartree-Fock self-consistent-field calculation.

The calculation of two-electron repulsion integrals (ERIs) is a crucial aspect of Hartree-Fock calculations. In computing the ERIs of varying angular momentum, both the central processing unit (CPU) and the graphics processing unit (GPU) have their respective advantages. To accelerate the ERI evaluation and Fock matrix generation, a hybrid CPU/GPU method has been proposed to maximize the computational power of both CPU and GPU while overlapping the CPU and GPU computations. This method employs a task queue where each task corresponds to ERIs with the same angular momentum. The queue begins with ERIs of low angular momentum, which are computationally efficient on GPUs, and ends with ERIs of high angular momentum, which are better suited for CPU computation. CPUs and GPUs dynamically grab and complete tasks from the start and end of the queue using OpenMP dynamic scheduling until all tasks are finished. The hybrid CPU/GPU computation offers the advantage of enabling calculations with arbitrary angular momentum. Test calculations showed that the hybrid CPU/GPU algorithm is more efficient than "GPU-only" when using a single GPU. However, as more GPUs are involved, the advantage diminishes or disappears. The scaling exponents of the hybrid method were slightly higher than "GPU-only," but the pre-exponent factor was significantly lower, making the hybrid method more effective overall.

IDEFIX: A versatile performance-portable Godunov code for astrophysical flows

Context. The exascale super-computers becoming available rely on hybrid energy-efficient architectures that involve an accelerator such as a graphics processing unit (GPU). Leveraging the computational power of these machines often means a significant rewrite of the numerical tools each time a new architecture becomes available. Aims. We present IDEFIX, a new code for astrophysical flows that relies on the KOKKOS meta-programming library to guarantee performance portability on a wide variety of architectures while keeping the code as simple as possible to the user. Methods. IDEFIX is based on a Godunov finite-volume method that solves the nonrelativistic hydrodynamical (HD) and magnetohy-drodynamical (MHD) equations on various grid geometries. IDEFIX includes a large choice of solvers and several additional modules (constrained transport, orbital advection, nonideal MHD), allowing users to address complex astrophysical problems. Results. IDEFIX has been successfully tested on Intel and AMD CPUs (up to 131 072 CPU cores on Irene-Rome at TGCC) as well as NVidia and AMD GPUs (up to 1024 GPUs on Adastra at CINES). IDEFIX achieves more than 108 cell s−1 in MHD on a single NVidia V100 GPU and 3 × 1011 cell s−1 on 256 Adastra nodes (1024 GPUs) with 95% parallelization efficiency (compared to single node). For the same problem, IDEFIX is up to six times more energy efficient on GPUs compared to Intel Cascade Lake CPUs. Conclusions. IDEFIX is now a mature exascale-ready open-source code that can be used on a large variety of astrophysical and fluid dynamics applications.

GPU accelerated volumetric lattice Boltzmann model for image-based hemodynamics in portal hypertension

We are developing a new technique for monitoring portal hypertension by the pressure gradient between the portal vein and the inferior vena cava (PPG) based on non-invasive measurements (MRI images). Massive parametrization and classification are required to investigate the underlying relationship between the porosity and the stages of liver cirrhosis numerically, and the hepatic-portal venous system is a multi-scale system. Both of them need high computational costs. The suitability of the lattice Boltzmann method for GPU (Graphics Processing Unit) parallel computation provides an opportunity to overcome it. In this paper, we perform GPU parallelization and optimization for the volumetric lattice Boltzmann method with arbitrary geometry based on images. Three application cases, including pipe flow, hemodynamics in the portal venous system, and hemodynamics in a simple hepatic-portal venous system, are employed to prove the method can be applied in the hepatic-portal venous system based on accuracy and efficiency. The reliability of the model is qualitatively validated by the analytical solution of velocity and pressure difference distribution of pipe flow and quantitatively confirmed by the pulsatility of velocity and pressure difference that can be neglected in the portal venous system. The performance of the application cases is examined with Intel Broadwell E5-2683 v3@ 2.30 GHz (CPU) and NVIDIA Tesla V100 16GB (GPU). It shows the GPU algorithm for sparse geometry (SPARSE) has a similar speed to the regular GPU algorithm for dense geometry (DENSE) when the fluid volume fraction (q) is close to 1. And SPARSE speeds up to 2.2 times compared with DENSE when q is in the range of 0.19∼0.27. Meanwhile, the saving ratio of memory cost depends on q. For a numerical case in the hepatic-portal venous system, i.e., a large-scale system, parallel execution can be converged around half an hour with SPARSE, while the memory spills the limitation with DENSE with a single GPU. Hence, multi-GPU implementation is applied to release the limitation, and it can improve performance by increasing the number of GPU cards. In summary, the method presented in the paper is feasibility applied in the hepatic-portal venous system, laying the foundation of the new technique development.

A scalable parallel algorithm for global sequence alignment with customizable scoring scheme

SummarySequence alignment is a critical computational problem in various domains, including genomics, proteomics, and natural language processing. The Needleman‐Wunsch (NW) algorithm is a classical dynamic programming approach for finding the optimal global alignment between two sequences. However, its quadratic time and space complexity make it impractical for aligning large‐scale sequences, which are increasingly common in modern applications. In this article, we propose a parallel variation of the NW algorithm that enables scalable global sequence alignment with customizable scoring schemes. Our approach re‐formulates the dependencies in the NW algorithm to enable parallel execution, thereby leveraging the computational power of modern parallel architectures, such as graphics processing unit (GPU). Furthermore, our algorithm supports arbitrary linear scoring schemes, which allows us to use domain‐specific knowledge to improve alignment accuracy. We establish the correctness of our algorithm and evaluate its performance using real DNA and user trajectory sequences on GPUs. Our parallel algorithm has shown impressive results in our experiments, with a peak performance of 27.99 GCUPS (giga cell updates per second) and a maximum speedup of 48.18 times compared to the traditional sequential implementation. Additionally, our algorithm demonstrates remarkable scalability, enabling the alignment of sequences of any length while ensuring balanced work distribution and optimal utilization of resources. Our primary objective is to harness the computational capabilities of a single GPU and fully utilize the processing power of multi‐core CPUs.

A Multi-GPU Aggregation-Based AMG Preconditioner for Iterative Linear Solvers

We present and release in open source format a sparse linear solver which efficiently exploits heterogeneous parallel computers. The solver can be easily integrated into scientific applications that need to solve large and sparse linear systems on modern parallel computers made of hybrid nodes hosting Nvidia Graphics Processing Unit (GPU) accelerators. The work extends previous efforts of some of the authors in the exploitation of a single GPU accelerator and proposes an implementation, based on the hybrid MPI-CUDA software environment, of a Krylov-type linear solver relying on an efficient Algebraic MultiGrid (AMG) preconditioner already available in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">BootCMatchG</i> library. Our design for the hybrid implementation has been driven by the best practices for minimizing data communication overhead when multiple GPUs are employed, yet preserving the efficiency of the GPU kernels. Strong and weak scalability results of the new version of the library on well-known benchmark test cases are discussed. Comparisons with the Nvidia AmgX solution show a speedup, in the solve phase, up to 2.0x.

Graph Traversal (GT) on Linear and Quadratic Graphic Processing Units with CPU

Nowadays, Graph Traversal (GT) is a fundamental procedure for developing a measure in computing systems and algorithms. Graph traversal algorithms usually iterate input datasets of a graph to provide a logical convergence in every iteration. Graphic Processing Units (GPUs) have been utilized broadly as accelerators of GT due to their massive parallelism and accessibility of high-transfer speed memory. Furthermore, graph-based computation is pervasive and challenging for memory systems implementation in a specific time period. This paper provides quadratic and single GPUs, allowing developers to extend graph-based GPU computation effortlessly. The paper concentrates on the creditability and validation of sparse graphs in GPUs. Since the benchmark characteristic of Breadth-First Search (BFS) plays a critical role in graphs, this paper considers a primary characteristic benchmark for BFS computation usage. This paper explores the effectiveness and applicability of BFS for analyzing graph systems by utilizing the mentioned characteristics in BFS computation. This paper also utilizes the penalization of GPU BFS during the analysis and simulation results. In addition, using a comparative simulation result, this paper concludes that improved traversal rates speed up the computation and reduce memory usage for the single and quadratic GPU structures.

Large eddy simulations of ammonia-hydrogen jet flames at elevated pressure using principal component analysis and deep neural networks

The combustion of ammonia/hydrogen is currently gaining importance in the power generation sector as an alternative for hydrocarbon fuels and improved fundamental insights will facilitate its application. To investigate the complex interactions between turbulence and chemistry for ammonia-hydrogen jet flames under high-pressure conditions, large eddy simulation (LES) computations are conducted using the PC-transport model, which is based on Principal Component Analysis (PCA), coupled with nonlinear regression that utilizes deep neural networks (DNN) to enhance the size-reduction potential of PCA. Classical statistics-based nonlinear regression methods are inefficient at fitting highly nonlinear manifolds and when large data sets are involved. These two drawbacks can be overcome by utilizing DNN tools. Several DNN architectures composed of fully connected layers of different depths and widths, batch normalization, and various activation functions coupled with various loss functions (mean squared error, absolute error, and R2) are explored to find an optimal fit to the thermo-chemical state-space manifold. The ability to achieve highly accurate mapping through DNN-based nonlinear regression with an R2-score >0.99 is shown by employing a single graphical processing unit (Nvidia RTX 3090). Furthermore, the proposed PC-DNN approach is extended to include differential diffusion based on a rotation technique and utilization of the mixture-averaged transport model for the training data set. To demonstrate the potential of the PC-DNN approach in modeling turbulent non-premixed combustion, LES results are compared with the recent Raman/Rayleigh scattering measurements that were obtained at the KAUST high-pressure combustion duct (HPCD). Results show that the PC-DNN approach is able to capture key flame characteristics with reasonable accuracy using only two principal components. The inclusion of differential diffusion leads to improved predictions, although some discrepancies are observed in fuel-lean regions.

Simple and efficient GPU accelerated topology optimisation: Codes and applications

This work presents topology optimisation implementations for linear elastic compliance minimisation in three dimensions, accelerated using Graphics Processing Units (GPUs). Three different open-source implementations are presented for linear problems. Two implementations use GPU acceleration, based on either OpenMP 4.5 or the Futhark language to implement the hardware acceleration. Both GPU implementations are based on high level GPU frameworks, and hence, avoid the need for expertise knowledge of e.g. CUDA or OpenCL. The third implementation is a vectorised and multi-threaded CPU code, which is included for reference purposes. It is shown that both GPU accelerated codes are able to solve large-scale topology optimisation problems with 65.5 million elements in approximately 2 h using a single GPU, while the reference implementation takes approximately 3 h and 10 min using 48 CPU cores. Furthermore, it is shown that it is possible to solve nonlinear topology optimisation problems using GPU acceleration, demonstrated by a nonlinear end-compliance optimisation with finite strains and a Neo-Hookean material model discretised by 1 million elements.