Graphics Processing Units Architecture Research Articles

High-speed turbulent flows towards the exascale: STREAmS-2 porting and performance

Exascale High Performance Computing (HPC) represents a tremendous opportunity to push the boundaries of Computational Fluid Dynamics (CFD), but despite the consolidated trend towards the use of Graphics Processing Units (GPUs), programmability is still an issue. STREAmS-2 (Bernardini et al. Comput. Phys. Commun. 285 (2023) 108644) is a compressible solver for canonical wall-bounded turbulent flows capable of harvesting the potential of NVIDIA GPUs. Here we extend the already available CUDA Fortran backend with a novel HIP backend targeting AMD GPU architectures. The main implementation strategies are discussed along with a novel Python tool that can generate the HIP and CPU code versions allowing developers to focus their attention only on the CUDA Fortran backend. Single GPU performance is analysed focusing on NVIDIA A100 and AMD MI250x cards which are currently at the core of several HPC clusters. The gap between peak GPU performance and STREAmS-2 performance is found to be generally smaller for NVIDIA cards. Roofline analysis allows tracing this behavior to unexpectedly different computational intensities of the same kernel using the two cards. Additional single-GPU comparisons are performed to assess the impact of grid size, number of parallelized loops, thread masking and thread divergence. Parallel performance is measured on the two largest EuroHPC pre-exascale systems, LUMI (AMD GPUs) and Leonardo (NVIDIA GPUs). Strong scalability reveals more than 80% efficiency up to 16 nodes for Leonardo and up to 32 for LUMI. Weak scalability shows an impressive efficiency of over 95% up to the maximum number of nodes tested (256 for LUMI and 512 for Leonardo). This analysis shows that STREAmS-2 is the perfect candidate to fully exploit the power of current pre-exascale HPC systems in Europe, allowing users to simulate flows with over a trillion mesh points, thus reducing the gap between the Reynolds numbers achievable in high-fidelity simulations and those of real engineering applications.

LibERI-A portable and performant multi-GPU accelerated library for electron repulsion integrals via OpenMP offloading and standard language parallelism.

A portable and performant graphics processing unit (GPU)-accelerated library for electron repulsion integral (ERI) evaluation, named LibERI, has been developed and implemented via directive-based (e.g., OpenMP and OpenACC) and standard language parallelism (e.g., Fortran DO CONCURRENT). Offloaded ERIs consist of integrals over low and high contraction s, p, and d functions using the rotated-axis and Rys quadrature methods. GPU codes are factorized based on previous developments [Pham et al., J. Chem. Theory Comput. 19(8), 2213-2221 (2023)] with two layers of integral screening and quartet presorting. In this work, the density screening is moved to the GPU to enhance the computational efficacy for large molecular systems. The L-shells in the Pople basis set are also separated into pure S and P shells to increase the ERI homogeneity and reduce atomic operations and the memory footprint. LibERI is compatible with any quantum chemistry drivers supporting the MolSSI Driver Interface. Benchmark calculations of LibERI interfaced with the GAMESS software package were carried out on various GPU architectures and molecular systems. The results show that the LibERI performance is comparable to other state-of-the-art GPU-accelerated codes (e.g., TeraChem and GMSHPC) and, in some cases, outperforms conventionally developed ERI CUDA kernels (e.g., QUICK) while fully maintaining portability.

A GPU-accelerated Monte Carlo code, RT2 for coupled transport of photon, electron/positron, and neutron

Objective. This work aims to develop a graphics processing unit (GPU)-accelerated Monte Carlo code for the coupled transport of photon, electron/positron and neutron over a broad range of energies for medical applications. Approach. By separating the MC evolution of radiation into source, transport, and interaction kernels, the branch divergence was alleviated. The memory coalescence was achieved by vectorizing the access pattern in which the secondary particles were archived. To accelerate further particle tracking, ray-tracing hardware acceleration in the Nvidia OptiXTM framework was applied. For photon and electron/positron, the EGSnrc interaction modules were ported as a GPU-optimized configuration. For neutron, a group-wised transport based on NJOY21 preprocessed data was implemented. The developed code was validated against CPU-based FLUKA. Neutron, x-ray and electron beams incident on water and ICRP phantoms were simulated. The neutron energy group and the transport parameters of photon and electron were set to be the same in both codes. A single Nvidia RTX 4090 card was used in this code while all 20 threads of a single Intel Core i9-10900K node were used in FLUKA. Main results. The number of histories was set to ensure that statistical uncertainties lower than 2% for all voxels whose doses were larger than 20% of the maximum. In all cases, the dose differences in the voxels between the codes were within 2.5%. For photons and electrons, the developed code was 150–300 times faster than FLUKA in both geometries. For neutrons, the code was respectively 80 and 135 times faster in the water and ICRP phantoms than FLUKA. Significance. This study offers an appropriate solution for uncoalesced memory access and branch divergence commonly encountered in coupled MC transport on the GPU architecture. The formidable acceleration in computing times and accuracy shown in this study can promise a routine clinical use of MC simulations.

A novel phase-field lattice Boltzmann framework for diffusion-driven multiphase evaporation

Heat transfer and phase change phenomena, particularly diffusion-driven droplet evaporation, play pivotal roles in various industrial applications and natural processes. Despite advancements in computational fluid dynamics, modeling multiphase flows with large density ratios remains challenging. In this study, we developed a robust and stable conservative Allen–Cahn-based phase-field lattice Boltzmann method to solve the flow field equations. This method is coupled with the finite difference discretization of vapor species transport equation and the energy equation. The coupling between the vapor concentration and temperature field at the interface is modeled by the well-known Clausius–Clapeyron correlation. Our approach is capable of simulations under real physical conditions and is compatible with graphics processing unit architecture, making it ideal for large-scale industrial simulations. Three validation test cases are conducted to demonstrate the consistency of the presented model, including simulations of Stefan flow, the evaporation of suspended droplets containing water, acetone, and ethanol in the air, and the evaporation of a water sessile droplet on a flat surface. The results show that the model is able to predict the behavior and characteristics of each case accurately. Notably, our numerical results exhibit a maximum relative error of approximately 1% in simulations of Stefan flow. In the case of suspended droplet evaporation, the observed maximum difference between the calculated wet bulb temperatures and those derived from psychrometric charts is approximately 0.9 K. Moreover, our analysis of the sessile droplet reveals a good agreement between the results obtained by our model for the evaporative mass flux and those obtained from the existing models in the literature for different contact angles.

GPU-based parallel programming for FEM analysis in the optimization of steel frames

ABSTRACT Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a reasonable level is possible by running GPU (Graphical Processing Unit) processors, which can be found on standard computers. This study presents an algorithm for the acceleration of size optimization of steel frames by using the BBO (Biogeography-Based Optimization) method that is suitable for GPU architecture. The GPU-based parallel algorithm, designed for FEM (Finite Element Method) analysis, is applied to three hypothetical steel-frame case structures with different numbers of members and nodes; and processed on four different computers which are available on the market. The presented case studies revealed that the proposed solution’s efficiency increases as the number of members increases and confirmed the ability of the acceleration algorithm for optimization of large-scale frame structures and provided time efficiency.

Efficient computation of the geopotential gradient in graphic processing units

Efficient computation of the geopotential gradient is essential for numerical propagators, particularly in scenarios involving low Earth orbits. Conventional geopotential calculations are based on spherical harmonics series, which become computationally demanding as the degree/order increases. This computational burden can be mitigated by means of parallelized algorithms. Additionally, certain situations lend themselves to high parallelization, such as the propagation of space debris catalogs, satellite mega-constellations, or the dispersion of particles resulting from a space collision event. This paper introduces an optimized Graphics Processing Unit (GPU) implementation designed to facilitate extensive parallelization in the geopotential gradient calculation. The formulation developed in this study is not specific to any GPU. However, to illustrate the low-level optimizations necessary for an efficient implementation, we selected the Compute Unified Device Architecture (CUDA) as the dominant and de facto standard in parallel computing. Nevertheless, most of the concepts and optimizations presented in this paper are also valid for other GPU architectures. Built upon the spherical harmonic expansion using the Cunningham formulation, which is well-suited for GPU computations, our implementation offers several variants with different tradeoffs between speed and accuracy. Besides GPU double precision, we introduced a mixed precision arithmetic –a hybrid between single and double precision– that exploits GPU capabilities with a low penalty in accuracy. The proposed algorithm was implemented as a software reusable module, and its performance was evaluated against GMAT, GODOT, and Orekit astrodynamic codes. The algorithm’s accuracy in double precision is comparable to such codes. The mixed precision version showed enough accuracy for LEO satellite propagation, with around 1 m difference in four days. Testing across different CUDA architectures revealed very high speed-up factors compared to a single CPU, reaching a speed-up of 645 for the mixed precision variant and 450 for the double precision one in the propagation of about 3200 objects with a geopotential of degree/order 126 × 126 using an A100 GPU device.

Time Predictable Modeling Method for GPU Architecture with SIMT and Cache Miss Awareness

Graphics Processing Units (GPUs) are used to accelerate computing-intensive tasks, such as neural networks,data analysis, high-performance computing, etc. In the past decade or so, researchers have done a lot of work on GPU architecture and proposed a variety of theories and methods to study the microarchitectural characteristics of various GPUs. In this study, the GPU serves as a co-processor and works together with the CPU in an embedded real-time system to handle computationally intensive tasks. It models the architecture of the GPU and further considers it based on some excellent work. The SIMT mechanism and Cache-miss situation provide a more detailed analysis of the GPU architecture. In order to verify the GPU architecture model proposed in this article, 10 GPU kernel_task and an Nvidia GPU device were used to perform experiments. The experimental results showed that the minimum error between the kernel task execution time predicted by the GPU architecture model proposed in this article and the actual measured kernel task execution time was 3.80%, and the maximum error was 8.30%.

Enhancing GPU performance and energy efficiency: Innovative strategies for sustainable computing

With the rapid advancement of science and information technology, Graphics Processing Units (GPUs) have become indispensable tools in contemporary scientific research and industrial production. This paper delves into optimizing GPU performance and energy efficiency, with a specific focus on five aspects: core count and layout, memory hierarchy and cache design, clock speed, specialized hardware units for specific tasks, and interconnections among GPU components. Increasing core count generally enhances GPU performance but also elevates energy consumption. Optimizing core count and layout strikes a balance between performance and energy use. Memory hierarchy and cache design are crucial for handling the inherent parallelism of GPU architecture; optimizing these aspects boosts GPU efficiency and sustainability. Clock speed is a vital performance indicator, with the right speed achieving an optimal balance between performance and energy use. Tailoring hardware units for specific tasks enhances computational efficiency and lowers energy consumption. Optimizing interconnections between GPU components enhances data transfer efficiency, yielding higher performance. Through comprehensive research and optimization in these five areas, this paper introduces innovative strategies and techniques to boost GPU performance and reduce energy consumption, laying the foundation for sustainable computing development.

Agnostic Energy Consumption Models for Heterogeneous GPUs in Cloud Computing

The adoption of cloud computing has grown significantly among individuals and in organizations. According to this growth, Cloud Service Providers have continuously expanded and updated cloud-computing infrastructures, which have become more heterogeneous. Managing these heterogeneous resources in cloud infrastructures while ensuring Quality of Service (QoS) and minimizing energy consumption is a prominent challenge. Therefore, unifying energy consumption models to deal with heterogeneous cloud environments is essential in order to efficiently manage these resources. This paper deeply analyzes factors affecting power consumption and employs these factors to develop power models. Because of the strong correlation between power consumption and energy consumption, the influencing factors on power consumption, with the addition of other factors, are considered when developing energy consumption models to enhance the treatment in heterogeneous infrastructures in cloud computing. These models have been developed for two Virtual Machines (VMs) containing heterogeneous Graphics Processing Units (GPUs) architectures with different features and capabilities. Experiments evaluate the models through a cloud testbed between the actual and predicted values produced by the models. Deep Neural Network (DNN) power models are validated with shallow neural networks using performance counters as inputs. Then, the results are significantly enhanced by 8% when using hybrid inputs (performance counters, GPU and memory utilization). Moreover, a DNN energy-agnostic model to abstract the complexity of heterogeneous GPU architectures is presented for the two VMs. A comparison between the standard and agnostic energy models containing common inputs is conducted in each VM. Agnostic energy models with common inputs for both VMs show a slight enhancement in accuracy with input reduction.

FLEW: A DNS Solver for Compressible Flows in Generalized Curvilinear Coordinates

We present FLEW, an in-house high-fidelity solver for direct numerical simulation (DNS) of turbulent compressible flows over arbitrary shaped geometries. FLEW solves the Navier–Stokes equations written in a generalized curvilinear coordinate system, in which the surface coordinates are non-orthogonal, whereas the third axis is normal to the surface. Spatial discretization relies on high-order finite-difference schemes. The convective terms are discretized using an hybrid approach, combining the near-zero numerical dissipation provided by central approximations with the robustness of weighted essentially non-oscillatory (WENO) schemes, required to capture shock waves. Central schemes are stabilized using a skew-symmetric-like splitting of convective derivatives, endowing the solver with the energy-preserving property in the inviscid limit. The maximum order of accuracy is eighth for central schemes (also used for viscous terms discretization) and seventh for WENO. The code is oriented to modern high-performance computing (HPC) platforms thanks to message passing interface (MPI) parallelization and the ability to run on graphics processing unit (GPU) architectures. Reliability, accuracy and robustness of the code are assessed in the low-subsonic, transonic and supersonic regimes. We present the results of several benchmarks, namely the inviscid Taylor–Green vortex, turbulent curved channel flow, transonic laminar flow over a NACA 0012 airfoil and turbulent supersonic ramp flow. The results for all configurations proved to be in excellent agreement with previous studies.

H3DM: A High-bandwidth High-capacity Hybrid 3D Memory Design for GPUs

Graphics Processing Units (GPUs) are widely used for modern applications with huge data sizes. However, the performance benefit of GPUs is limited by their memory capacity and bandwidth. Although GPU vendors improve memory capacity and bandwidth using 3D memory technology (HBM), many important workloads with terabytes of data still cannot fit in the provided capacity and are bound by the provided bandwidth. With a limited GPU memory capacity, programmers should handle the data movement between GPU and host memories by themselves, causing a significant programming burden. To improve programming ease, GPUs use a unified address space with the host that allows over-subscribing GPU memory, but this approach is not effective in terms of performance once GPUs encounter memory page faults. Many recent works have tried to remedy capacity and bandwidth bottlenecks using dense non-volatile memories (NVMs) and true-3D stacking. However, these works mainly focus on one bottleneck or do not provide a scalable solution that fits future requirements. In this paper, we investigate true-3D stacking of dense, low-power, and refresh-free non-volatile phase change memory (PCM) on top of state-of-the-art GPU configurations to provide higher capacity and bandwidth within the available area and power budget. The higher density and lower power consumption of PCM provide higher capacity through integrating more cells in each 3D layer and enabling stacking more layers. However, we observe that stacking more than six layers of pure-PCM memory violates the thermal constraint and severely harms the performance and power efficiency due to its higher write latency and energy. Further, it degrades the lifetime of GPU to less than one year. Utilizing a hybrid architecture that leverages the benefits of both DRAM and PCM memories has been widely studied by prior proposals; however, true-3D integration of such a hybrid memory architecture especially on top of state-of-the-art powerful GPU architecture has not been investigated yet. We experimentally demonstrate that by covering 80% of write requests in DRAM and eliminating refresh overhead, true-3D stacking of eight 32GB layers of PCM along with two 8GB layers of DRAM is possible resulting in a total of 272GB memory capacity. Based on the explored design requirements, We propose a 3D high-bandwidth high-capacity hybrid memory (H3DM) system utilizing a hybrid-3D (H3D)-aware remapping scheme to reduce expensive PCM writes to under 20% while avoiding DRAM refresh overhead. H3DM improves the performance up to 291% compared to the baseline GPU architecture while remaining within only 3% of an ideal case with DRAM-like access latency, on average. Moreover, by increasing the dataset size above the baseline GPU memory space, H3DM improves performance and power up to 648% and 87% compared to the baseline GPU architecture since it avoids expensive data transfers through off-chip communication links.

Assessing the Impact of Compiler Optimizations on GPUs Reliability

Graphics Processing Units (GPUs) compilers have evolved in order to support general-purpose programming languages for multiple architectures. NVIDIA CUDA Compiler (NVCC) has many compilation levels before generating the machine code and applies complex optimizations to improve performance. These optimizations modify how the software is mapped in the underlying hardware; thus, as we show in this article, they can also affect GPU reliability. We evaluate the effects on the GPU error rate of the optimization flags applied at the NVCC Parallel Thread Execution (PTX) compiling phase by analyzing two NVIDIA GPU architectures (Kepler and Volta) and two compiler versions (NVCC 10.2 and 11.3). We compare and combine fault propagation analysis based on software fault injection, hardware utilization distribution obtained with application-level profiling, and machine instructions radiation-induced error rate measured with beam experiments. We consider eight different workloads and 144 combinations of compilation flags, and we show that optimizations can impact the GPUs’ error rate of up to an order of magnitude. Additionally, through accelerated neutron beam experiments on a NVIDIA Kepler GPU, we show that the error rate of the unoptimized GEMM (-O0 flag) is lower than the optimized GEMM’s (-O3 flag) error rate. When the performance is evaluated together with the error rate, we show that the most optimized versions (-O1 and -O3) always produce a higher amount of correct data than the unoptimized code (-O0).

BLAS Kütüphanelerinin GPU Mimarilerindeki Nicel Performans Analizi

Basic Linear Algebra Subprograms (BLAS) are a set of linear algebra routines commonly used by machine learning applications and scientific computing. BLAS libraries with optimized implementations of BLAS routines offer high performance by exploiting parallel execution units in target computing systems. With massively large number of cores, graphics processing units (GPUs) exhibit high performance for computationally-heavy workloads. Recent BLAS libraries utilize parallel cores of GPU architectures efficiently by employing inherent data parallelism. In this study, we analyze GPU-targeted functions from two BLAS libraries, cuBLAS and MAGMA, and evaluate their performance on a single-GPU NVIDIA architecture by considering architectural features and limitations. We collect architectural performance metrics and explore resource utilization characteristics. Our work aims to help researchers and programmers to understand the performance behavior and GPU resource utilization of the BLAS routines implemented by the libraries.

Parallel computation to bidimensional heat equation using MPI/CUDA and FFTW package

In this study, we present a fast algorithm for the numerical solution of the heat equation. The heat equation models the heat diffusion over time and through a given region. We engage a finite difference method to solve this equation numerically. The performance of its parallel implementation is considered using Message Passing Interface (MPI), Compute Unified Device Architecture (CUDA), and time schemes, such as Forward Euler (FE) and Runge-Kutta (RK) methods. The originality of this study is research on parallel implementations of the fourth-order Runge-Kutta method (RK4) for sparse matrices on Graphics Processing Unit (GPU) architecture. The supreme proprietary framework for GPU computing is CUDA, provided by NVIDIA. We will show three metrics through this parallelization to compare the computing performance: time-to-solution, speed-up, and performance. The spectral method is investigated by utilizing the FFTW software library, based on the computation of the fast Fourier transforms (FFT) in parallel and distributed memory architectures. Our CUDA-based FFT, named CUFFT, is performed in platforms, which is a highly optimized FFTW implementation. We will give numerical tests to reveal that this method is up-and-coming for solving the heat equation. The final result demonstrates that CUDA has a significant advantage and performance since the computational cost is tiny compared with the MPI implementation. This vital performance gain is also achieved through careful attention of managing memory communication and access.

GPU-Accelerated Signal Processing for Passive Bistatic Radar

Passive bistatic radar is a novel radar technology that passively detects targets without actively emitting signals. Since passive bistatic radar entails larger data volumes and computations compared to traditional active radiation radar, the development of hardware and software platforms capable of efficiently processing signals from passive bistatic radar has emerged as a research focus in this field. This research investigates the signal processing flow of passive bistatic radar based on its characteristics and devises a parallel signal processing scheme under graphic processing unit (GPU) architecture for computation-intensive tasks. The proposed scheme utilizes high-computing-power GPU as the hardware platform and compute unified device architecture (CUDA) as the software platform and optimizes the extensive cancellation algorithm batches (ECA-B), range Doppler and constant false alarm detection algorithms. The detection and tracking of a single target are realized on the passive bistatic radar dataset of natural scenarios, and experiments show that the design of this algorithm can achieve a maximum acceleration ratio of 113.13. Comparative experiments conducted with varying data volumes revealed that this method significantly enhances the signal processing rate for passive bistatic radar.

Physical vapor deposition simulator by graphical processor unit ray casting

This paper presents fast, accurate software for modeling physical vapor deposition systems over irregular surfaces. The model is implemented using graphics processing unit (GPU) ray casting. Applied models are viewed as a cross section of the area of interest. Given evaporation rate, time, and angular profiles in a vacuum system, an iterative time-step approach for calculating deposition profiles is calculated in the GPU architecture following a ballistic modeling approach. Thin-film technologies for the electronics industry will require evaporations on complex surfaces. Depending on the nature of the surface, a uniform thin film across the topology is wanted for various device parameters. The ray casting method is tested against various profiles. The code is freely distributed on GitHub (see https://github.com/adam-r-thomas/PVDS).

An Enhanced Python-Based Open-Source Particle Image Velocimetry Software for Use with Central Processing Units

Particle Image Velocimetry (PIV) is a widely used experimental technique for measuring flow. In recent years, open-source PIV software has become more popular as it offers researchers and practitioners enhanced computational capabilities. Software development for graphical processing unit (GPU) architectures requires careful algorithm design and data structure selection for optimal performance. PIV software, optimized for central processing units (CPUs), offer an alternative to specialized GPU software. In the present work, an improved algorithm for the OpenPIV–Python software (Version 0.25.1, OpenPIV, Tel Aviv-Yafo, Israel) is presented and implemented under a traditional CPU framework. The Python language was selected due to its versatility and widespread adoption. The algorithm was also tested on a supercomputing cluster, a workstation, and Google Colaboratory during the development phase. Using a known velocity field, the algorithm precisely captured the time-average flow, momentary velocity fields, and vortices.

Accelerating Coupled-Cluster Calculations with GPUs: An Implementation of the Density-Fitted CCSD(T) Approach for Heterogeneous Computing Architectures Using OpenMP Directives.

An algorithm is presented for the coupled-cluster singles, doubles, and perturbative triples correction [CCSD(T)] method based on the density fitting or the resolution-of-the-identity (RI) approximation for performing calculations on heterogeneous computing platforms composed of multicore CPUs and graphics processing units (GPUs). The directive-based approach to GPU offloading offered by the OpenMP application programming interface has been employed to adapt the most compute-intensive terms in the RI-CCSD amplitude equations with computational costs scaling as , , and (where NO and NV denote the numbers of correlated occupied and virtual orbitals, respectively) and the perturbative triples correction to execute on GPU architectures. The pertinent tensor contractions are performed using an accelerated math library such as cuBLAS or hipBLAS. Optimal strategies are discussed for splitting large data arrays into tiles to fit them into the relatively small memory space of the GPUs, while also minimizing the low-bandwidth CPU-GPU data transfers. The performance of the hybrid CPU-GPU RI-CCSD(T) code is demonstrated on pre-exascale supercomputers composed of heterogeneous nodes equipped with NVIDIA Tesla V100 and A100 GPUs and on the world's first exascale supercomputer named "Frontier", the nodes of which consist of AMD MI250X GPUs. Speedups within the range 4-8× relative to the recently reported CPU-only algorithm are obtained for the GPU-offloaded terms in the RI-CCSD amplitude equations. Applications to polycyclic aromatic hydrocarbons containing 16-66 carbon atoms demonstrate that the acceleration of the hybrid CPU-GPU code for the perturbative triples correction relative to the CPU-only code increases with the molecule size, attaining a speedup of 5.7× for the largest circumovalene molecule (C66H20). The GPU-offloaded code enables the computation of the perturbative triples correction for the C60 molecule using the cc-pVDZ/aug-cc-pVTZ-RI basis sets in 7 min on Frontier when using 12,288 AMD GPUs with a parallel efficiency of 83.1%.

H-Analysis and data-parallel physics-informed neural networks

We explore the data-parallel acceleration of physics-informed machine learning (PIML) schemes, with a focus on physics-informed neural networks (PINNs) for multiple graphics processing units (GPUs) architectures. In order to develop scale-robust and high-throughput PIML models for sophisticated applications which may require a large number of training points (e.g., involving complex and high-dimensional domains, non-linear operators or multi-physics), we detail a novel protocol based on h-analysis and data-parallel acceleration through the Horovod training framework. The protocol is backed by new convergence bounds for the generalization error and the train-test gap. We show that the acceleration is straightforward to implement, does not compromise training, and proves to be highly efficient and controllable, paving the way towards generic scale-robust PIML. Extensive numerical experiments with increasing complexity illustrate its robustness and consistency, offering a wide range of possibilities for real-world simulations.

Solution of nonlinear fractional-order models of nuclear reactor with parallel computing: Implementation on GPU platform

This paper present novel parallel computing algorithms for numerical solution of nonlinear fractional-order (FO) models of nuclear reactor. These FO models arise by virtue of considering neutron transport in nuclear reactor as subdiffusion. The numerical computation of these FO models is carried out by parallelizing the sequential steps in Adams–Bashforth–Moulton method. A comparison of parallel execution on MATLAB and CUDA platforms is also given. The parallel computation is executed by harnessing multicore architecture of the general purpose graphics processing unit (GPGPU). A detailed analysis of the proposed procedure is presented considering different values of fractional derivative order in the fractional differential equations (FDEs). It is shown with extensive computational exercise that the proposed methodology achieves substantial speed up for all the three FDEs models used in nuclear reactor.