CUDA Research Articles

Two different parallel approaches for a hybrid fractional order Coronavirus model

In this paper, two different parallel approaches for a hybrid fractional order Coronavirus (2019-nCov) mathematical model are presented. Both parallel approaches are implemented using Julia high level language. Parallel algorithm implementations are developed for the HPC cluster using Message Passing Interface (MPI) technology and general-purpose computing on GPUs (GPGPU) using Compute Unified Device Architecture (CUDA) based on hardware environments. The algorithm implementation are used to solve the real-world problem of the hybrid fractional order Coronavirus (2019-nCov) mathematical model and to study the parallel efficiency. The introduced hybrid fractional order derivative is defined as a linear combination of the integral of Riemann-Liouville and the fractional order Caputo derivative. A parallel algorithm is designed based on the predictor-corrector method with the discretization of the Caputo proportional constant fractional hybrid operator for solving the model problem numerically. Simulation results show that, both the new parallel approaches achieve significant efficiency.

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.

DEM–SPH Coupling Method for Landslide Surge Based on a GPU Parallel Acceleration Technique

A high-performance DEM–SPH coupling method for landslide surge is proposed based on a graphics processing unit (GPU) parallel acceleration technique. A GPU-based simulation program, namely, G-DeSp, is developed using C++ and Compute Unified Device Architecture language, which can simulate 3D landslide surge with millions of particles precisely and efficiently. A two-phase flow dam-break test is adopted to validate the accuracy and efficiency of G-DeSp. Three-dimensional landslide surge simulations are conducted with the different diameter of solid particles and the maximum climb height of the surge by granular slide and rigid slide is analyzed. Results show that G-DeSp accurately simulates landslide surge, such as the movement and deformation of the landslide, the generation and propagation of surge waves, and the violent interaction of the fluid–solid. The velocity and displacement of the granular slides and surge decrease with the increase in the diameter of solid particles. The maximum climbing height of surge by the rigid slide is 6.5 times that of surge by the granular slide with the same volume, which indicates that the surge hazard induced by the rigid slide is much more serious than that induced by the granular slide.

GPU-acceleration of tensor renormalization with PyTorch using CUDA

We show that numerical computations based on tensor renormalization group (TRG) methods can be significantly accelerated with PyTorch on graphics processing units (GPUs) by leveraging NVIDIA's Compute Unified Device Architecture (CUDA). We find improvement in the runtime (for a given accuracy) and its scaling with bond dimension for two-dimensional systems. Our results establish that utilization of GPU resources is essential for future precision computations with TRG.

MRT-LB simulation and response surface analysis of natural convection of non-Newtonian ferrofluid in an enclosure with non-uniformly heated radiator through GPU computing

The multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) has been applied to numerically analyze the natural convection of non-Newtonian ferrofluid in an enclosure with a horizontal plate or radiator that is not uniformly heated. The left and right walls of the enclosure are cold (Tc), while the bottom and upper walls are assumed to be adiabatic. Simulations using numerical models are run for a variety of dimensionless variables, including Rayleigh numbers (Ra=104,105,106), Hartmann numbers (Ha=0,20,40,60,80), power-law index (n=0.6,0.8,1.0,1.2,1.4), magnetic field inclination angles (γ=0,π/4,π/2,3π/4), and the maximum ferroparticle volume fractions is 6% (ϕ=0.06). The numerical results are reported in the form of isotherms, streamlines, velocity, temperature, mean Nusselt number, and entropy production. The study found that the Nu¯ rises with the increment of the Rayleigh number but decreases with the enhancement of the Ha, n, and ϕ. The temperature declines rapidly when the magnetic field inclination angle values increase to γ=90°, then gains temperature. The velocity and temperature both increase as the ϕ increases. For increasing the power-law index, the Nu¯ grows by 1.6% in the case of Ra=104 but falls by 14.56% and 54.87% when Ra=105 and Ra=106, respectively. The entropy profile is discussed in detail. The Be number is more significant than 0.5 for various n at Ra=104 and 105, suggesting that entropy formation is primarily due to heat gradients. Response surface methodology (RSM) also describes the numerical results with variable Ra numbers (104, 105, 106), n=0.6,1.0,1.4), angles (γ=0,π/2,3π/4), Ha=40 and ϕ=0.06. Finally, the residual plots, 2D and 3D plots are described. All the simulations are conducted by the GPU (Graphics Processing Unit) computing with the NVIDIA CUDA C/C++ platform.

GPU-Accelerated Interaction-Aware Motion Prediction

Before their massive deployment, autonomous vehicles need to prove in complex scenarios such that they can reach human driving proficiency while guaranteeing higher safety levels. One of the most important human traits to negotiating traffic is the ability to predict the future behavior of surrounding vehicles as a basis for agile and safe navigation. This capability is particularly challenging for an autonomous system in highly interactive driving situations, such as intersections or roundabouts. In this paper, a set of techniques to bring a computationally expensive state-of-the-art motion prediction algorithm to real-time execution are presented with the goal of meeting a standard motion-planning algorithm execution frequency of 5 Hz, which is the primary consumer of motion predictions. This is achieved by applying novel and existing parallelization algorithms that take advantage of graphic processing units (GPUs) through the compute unified device architecture (CUDA) programming language and managing to produce an average 5× speedup over raw C++ in the cases studied. The optimizations are then evaluated in public datasets and a real vehicle on a test track.

QUASI-MONTE CARLO METHODS FOR CALCULATING DERIVATIVES SENSITIVITIES ON THE GPU

The calculation of option Greeks is vital for risk management. Traditional pathwise and finite-difference methods work poorly for higher-order Greeks and options with discontinuous payoff functions. The Quasi-Monte Carlo-based conditional pathwise method (QMC-CPW) for options Greeks allows the payoff function of options to be effectively smoothed, allowing for increased efficiency when calculating sensitivities. Another way to increase efficiency is to utilize the increased computational speed by applying GPUs to highly parallelisable finance problems such as calculating Greeks. We pair QMC-CPW with simulation on the GPU using the CUDA platform. We estimate the delta, vega and gamma Greeks of three exotic options: arithmetic Asian, binary Asian, and look-back. The increased computational speed through usage of the GPU is also shown with achieved speedups over sequential CPU implementations of more than 200x for our most accurate method.

Slightly Off-Axis Digital Holography Using a Transmission Grating and GPU-Accelerated Parallel Phase Reconstruction

Slightly off-axis digital holography is proposed using transmission grating to obtain quantitative phase distribution. The experimental device is based on an improved 4f optical system in which a two-window input plane is used to form the object beam and reference beam. Then, the two beams are diffracted into multiple orders by the transmission grating placed at the Fourier plane. By applying a modified Michelson configuration, the interference patterns can be generated by the object and reference beams from different diffraction orders. After translating the grating, a random phase shift can be introduced to the hologram. To demonstrate the feasibility of our method, both thick and thin phase specimens are retrieved using two carrier phase-shifting holograms. Furthermore, we use the phase reconstruction algorithm based on the NVIDIA CUDA programming model to reduce the retrieval time. Meanwhile, we optimize the discrete cosine transform (DCT)-based least-squares unwrapping algorithm to unwrap the phase. By porting the entire phase reconstruction process to the graphics processing unit (GPU), the phase retrieval acceleration and execution efficiency significantly improve. To demonstrate the feasibility of our method, it is found that our method can measure the surface profiles of standard elements, such as a plano-convex cylinder lens and a microlens array, with a relative error of about 0.5%. For holograms with a different phase shift, the root-mean-square (RMS) value of the phase difference for the main imaging region is about 0.2 rad. By accelerating the phase reconstruction with GPU implementation, a speedup ratio of about 20× for the thick phase specimen and a speedup ratio of about 15× for the thin-phase specimen can be obtained for holograms with a pixel size of 1024 × 1024.

Calculation of the Magnetic Field of a Current-Carrying System

With regard to the study of the characteristics of electrovortex flows occurring indirect current electric arcs and electroslag smelting furnaces, a method has been developed for calculating the magnetic field in a current-carrying medium based on the calculation of the Biot–Savart–Laplace integral. The developed technique is focused on the use of unstructured grids and does not require a priori information about the shape of the computational domain. The technique has been tested on problems that have an analytical solution, i.e., the calculation of the distribution of the magnetic field in the cylinder and the calculation of the magnetic field of the ring with the current. The distributions of the magnetic field are obtained for the two-dimensional and three-dimensional cases. We used NVIDIA CUDA technology on graphic processor units (GPUs) to speed up calculations. A comparison of the calculation times on various CPUs and GPUs is given.

3D indoor Propagation Simulator using Ray Tracing for 10-20 GHz frequency Band on GPU with NVIDIA-Optix under Unity graphic Engine

In this paper a novel Ray Tracing 3D indoor propagation simulator at frequencies of 10-20 GHz using GPU is presented. To carry out the simulation, software tools as Nvidia CUDA 10.0, CMake 3.19.4, Nvidia OPTIX 6.5, Microsoft Visual Studio Community 2017 and Unity 2018.4.21f1 was used. Two indoor with line-of-sight (LOS) scenarios were defined within the Faculty of Informatics and Electronics al the Escuela Superior Politécnica de Chimborazo (ESPOCH), one in the Microwave Laboratory, with minimum and maximum distances between Transmitter and Receiver of 0.34m (far field) and 7m, respectively; and the second stage is located in the hall on the ground floor of the FIE building with minimum and maximum distances of 6 and 12m, respectively. In those scenarios, both, real and simulated measurements were made with frequency hops of 1GHz. For the simulation, a ray tracing propagation technique under GPU using NVIDEA-Optix was used. By comparing simulated and measurement power, it was observed a difference of 10 dB approximately.

GPU parallelization of particulate matter concentration modeling in indoor environment with cellular automata framework

Nowadays, the CFD approaches for modeling turbulent airflow and particulate matter (PM) concentration distribution are matured despite they all suffer from heavy computational demands. Particularly, in indoor PM concentration modeling, a novel cellular automata (CA) approach in “Modeling particulate matter concentration in indoor environment with cellular automata framework” is developed to achieve almost the same accuracy with improved efficiency as the Eulerian approach. To further enhance its efficiency, this study proposes two parallelization procedures. With the mechanism parallelization, the four PM transport mechanisms (flow advection, turbulent diffusion, gravitational settling, and boundary deposition) are simulated simultaneously instead of sequentially. Besides, using the GPU-based cell parallelization by adopting OpenCL 2.1 under Nvidia CUDA, the execution of all the PM transport mechanisms is performed parallelly on GPUs instead of sequentially on the CPU. Three parallelized CA scenarios, i.e., the parallelized CA approach with only the mechanism parallelization, with only the GPU-based cell parallelization, and with both the two parallelization procedures, are evaluated through two indoor PM concentration experiments. The three parallelized CA scenarios are found to maintain the accuracy but enhance the efficiency by 174%–210%, 1780%–5730%, and 2427%–7695%, respectively. Thus, the GPU-based cell parallelization obtains more efficiency enhancement than the mechanism parallelization. Furthermore, despite the simulations are performed on an i9 PC with an Intel UHD Graphics 630 graphic card, the parallelized CA approach with both the two parallelization procedures can enhance its efficiency up to 24–77 times, proving its considerable potentials as a useful tool for real-time 3D indoor PM distribution modeling.

Lattice Boltzmann simulation of natural convection of ethylene glycol-alumina nanofluid in a C-shaped enclosure with MFD viscosity through a parallel computing platform and quantitative parametric assessment

There has been a strong interest to design and optimise thermally efficient heating and cooling equipment. The conventional fluids such as oil and water have limited thermal efficiency. Therefore, researchers have been seeking alternatives to the conventional fluids to improve the efficacy of heat exchanging and electronic cooling devices and nanofluid has been identified as a great option in this regard. Although nanofluid is a great alternative, certain specifications, geometries, and pertinent parameters need to be thoroughly investigated for a comprehensive understanding with the aid of robust computational technique. The aim of this study was to investigate the multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) to numerically analyse the effects of magnetic field dependent (MFD) viscosity on the natural convection of ethylene glycol (C2H6O2)-alumina (Al2O3) nanofluid in a side heated two-dimensional C-shaped enclosure using graphics processing unit (GPU) by a computing unified device architecture (CUDA) C parallel computing platform. Numerical simulations were performed at multifarious Rayleigh numbers, Hartmann numbers, and the different magnetic field inclination angles to study the heat transfer and various flow patterns under magnetic field-dependent (MFD) viscosity, solutions were presented by varying volume fraction of nanoparticles, Rayleigh numbers, viscous parameters, magnetic inclination angles, and Hartman numbers on streamlines, isotherms, local and average Nusselt number and temperature. Further correlation developments were conducted through Levenberg-Marquardt data-driven algorithm to investigate the influence of all the parameters on average Nusselt numbers, entropy generation, and fluid irreversibility parameter. The findings demonstrated that as the Rayleigh numbers augmented, the average Nusselt number increased significantly due to the influence of buoyancy, whereas under the influence of Hartmann numbers, average Nusselt numbers decreased due to the dominance of magnetic field strength and Lorentz force. However, the heat transfer continued to improve if the concentration of the nanoparticles increased, thus showcasing the importance of hybrid nanofluid. In addition, the entropy generation impact across the cavity for the ethylene glycol-alumina nanofluid was greatly enhanced by a stronger buoyancy influence.

Biomedical-named entity recognition using CUDA accelerated KNN algorithm

Biomedical named entity recognition (Bio-NER) is a highly complex and time-consuming research domain using natural language processing (NLP). It’s widely used in information retrieval, knowledge summarization, biomolecular event extraction, and discovery applications. This paper proposes a method for the recognition and classification of named entities in the biomedical domain using machine learning (ML) techniques. Support vector machine (SVM), decision trees (DT), K-nearest neighbor (KNN), and its kernel versions are used. However, recent advancements in programmable, massively parallel graphics processing units (GPU) hold promise in terms of increased computational capacity at a lower cost to address multi-dimensional data and time complexity. We implement a novel parallel version of KNN by porting the distance computation step on GPU using the compute unified device architecture (CUDA) and compare the performance of all the algorithms using the BioNLP/NLPBA 2004 corpus. Results demonstrate that CUDA-KNN takes full advantage of the GPU’s computational capacity and multi-leveled memory architecture, resulting in a 35× performance enhancement over the central processing unit (CPU). In a comparative study with existing research, the proposed model provides an option for a faster NER system for higher dimensionality and larger datasets as it offers balanced performance in terms of accuracy and speed-up, thus providing critical design insights into developing a robust BioNLP system.

GPU-HADVPPM V1.0: a high-efficiency parallel GPU design of the piecewise parabolic method (PPM) for horizontal advection in an air quality model (CAMx V6.10)

Abstract. With semiconductor technology gradually approaching its physical and thermal limits, graphics processing units (GPUs) are becoming an attractive solution for many scientific applications due to their high performance. This paper presents an application of GPU accelerators in an air quality model. We demonstrate an approach that runs a piecewise parabolic method (PPM) solver of horizontal advection (HADVPPM) for the air quality model CAMx on GPU clusters. Specifically, we first convert the HADVPPM to a new Compute Unified Device Architecture C (CUDA C) code to make it computable on the GPU (GPU-HADVPPM). Then, a series of optimization measures are taken, including reducing the CPU–GPU communication frequency, increasing the data size computation on the GPU, optimizing the GPU memory access, and using thread and block indices to improve the overall computing performance of the CAMx model coupled with GPU-HADVPPM (named the CAMx-CUDA model). Finally, a heterogeneous, hybrid programming paradigm is presented and utilized with GPU-HADVPPM on the GPU clusters with a message passing interface (MPI) and CUDA. The offline experimental results show that running GPU-HADVPPM on one NVIDIA Tesla K40m and an NVIDIA Tesla V100 GPU can achieve up to a 845.4× and 1113.6× acceleration. By implementing a series of optimization schemes, the CAMx-CUDA model results in a 29.0× and 128.4× improvement in computational efficiency by using a GPU accelerator card on a K40m and V100 cluster, respectively. In terms of the single-module computational efficiency of GPU-HADVPPM, it can achieve 1.3× and 18.8× speedup on an NVIDIA Tesla K40m GPU and NVIDIA Tesla V100 GPU, respectively. The multi-GPU acceleration algorithm enables a 4.5× speedup with eight CPU cores and eight GPU accelerators on a V100 cluster.

GPU acceleration of Swendsen–Wang dynamics

When simulating a lattice system near its critical temperature, local algorithms for modeling the system’s evolution can introduce very large autocorrelation times into sampled data. This critical slowing down places restrictions on the analysis that can be completed in a timely manner of the behavior of systems around the critical point. Because it is often desirable to study such systems around this point, a new algorithm must be introduced. Therefore, we turn to cluster algorithms, such as the Swendsen–Wang algorithm and the Wolff clustering algorithm. They incorporate global updates which generate new lattice configurations with little correlation to previous states, even near the critical point. We look to accelerate the rate at which these algorithm are capable of running by implementing and benchmarking a parallel implementation of each algorithm designed to run on GPUs under NVIDIA’s CUDA framework. A 17 and 90 fold increase in the computational rate was, respectively, experienced when measured against the equivalent algorithm implemented in serial code.

CUDA-Optimized GPU Acceleration of 3GPP 3D Channel Model Simulations for 5G Network Planning

The simulation of massive multiple-input multiple-output (MIMO) channel models is becoming increasingly important for testing and validation of fifth-generation new radio (5G NR) wireless networks and beyond. However, simulation performance tends to be limited when modeling a large number of antenna elements combined with a complex and realistic representation of propagation conditions. In this paper, we propose an efficient implementation of a 3rd Generation Partnership Project (3GPP) three-dimensional (3D) channel model, specifically designed for graphics processing unit (GPU) platforms, with the goal of minimizing the computational time required for channel simulation. The channel model is highly parameterized to encompass a wide range of configurations required for real-world optimized 5G NR network deployments. We use several compute unified device architecture (CUDA)-based optimization techniques to exploit the parallelism and memory hierarchy of the GPU. Experimental data show that the developed system achieves an overall speedup of about 240× compared to the original C++ model executed on an Intel processor. Compared to a design previously accelerated on a datacenter-class field programmable gate array (FPGA), the GPU design has a 33.3% higher single-precision performance but a 7.5% higher power consumption. The proposed GPU accelerator can provide fast and accurate channel simulations for 5G NR network planning and optimization.

Dual-wavelength off-axis digital holography inImageJ: toward real-time phase retrieval usingCUDA streams.

An ImageJ plug-in is developed to realize automatic real-time phase reconstruction for dual-wavelength digital holography (DH). This plug-in assembles the algorithms, including automatic phase reconstruction based on the division algorithm and post-processing. These algorithms are implemented and analyzed using a CPU and GPU, respectively. To hide the CPU-to-GPU data transfer latency, an optimization scheme using Compute Unified Device Architecture (CUDA) streams is proposed in ImageJ. Experimental results show that the proposed plug-in can perform faster reconstruction for dual-wavelength DH, resulting in frame rates up to 48fps even for one-megapixel digital holograms on a normal PC. In other words, the proposed plug-in can realize real-time phase reconstruction for dual-wavelength digital holographic videos.

Full-waveform inversion by model extension: Practical applications

Producing reliable acoustic subsurface velocity models still remains the main bottleneck of the oil and gas industry’s traditional imaging sequence. In complex geologic settings, the output of conventional ray-based or wave-equation-based tomographic methods may not be sufficiently accurate for full-waveform inversion (FWI) to converge to a geologically satisfactory earth model. We create a new method referred to as full-waveform inversion by model extension (FWIME) in which a wave-equation migration velocity analysis (WEMVA) technique is efficiently paired with a modified version of FWI. We find that our method is more powerful than applying WEMVA and FWI sequentially, and that it can converge to accurate solutions without the use of a good initial guess or low-frequency energy. We determine FWIME’s potential on five realistic and challenging numerical examples that simulate complex geologic scenarios often encountered in hydrocarbon exploration. We guide the reader step by step throughout the optimization process. We find that our method can simultaneously invert all wave types with the same simple mechanism and without the need for a user-intensive hyperparameter tuning process. In an open-source online repository, we provide our C++/compute unified device architecture (CUDA) numerical implementation accelerated with graphics processing units, encapsulated in a Python interface. All the numerical examples developed are accessible through Python notebooks and fully reproducible.

Dynamic GPU Scheduling With Multi-Resource Awareness and Live Migration Support

In clouds and data centers, GPU servers with multiple GPUs are widely deployed. Current state-of-the-art GPU scheduling policies are “static” in assigning applications to different GPUs. These policies usually ignore the dynamics of the GPU utilization and are often inaccurate in estimating resource demand before assigning/running applications, so there is a large opportunity to further balance the loads and improve GPU utilization. Based on CUDA (Compute Unified Device Architecture), we develop a runtime system called DCUDA which supports <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“dynamic”</i> scheduling of running applications between multiple GPUs. In particular, DCUDA takes into consideration multidimensional resources, including computing cores, memory usage, and energy consumption. It first provides a real-time and lightweight method to accurately monitor the resource demand of applications and GPU utilization. Furthermore, it provides a universal migration facility to migrate <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">“running applications”</i> between GPUs with negligible overhead. More importantly, DCUDA transparently supports all CUDA applications without changing their source code. Experiments with our prototype system show that DCUDA can reduce 78.3% of overloaded time of GPUs on average. As a result, for different workloads consisting of a wide range of applications we studied, DCUDA can reduce the average execution time of general applications by up to 42.1%, and even up to 67% for memory-intensive tasks. Besides, DCUDA also reduces 13.3% of energy in light-load scenarios.

VOLUMETRIC LIGHT TRANSFER IN FUNCTIONALLY DEFINED SCENES

In computer graphics, the main goals are high realism and rendering speed. In terms of realism, physically correct rendering is important, which calculates the transmission of light from the source to the observer. A method of modeling global illumination is presented, which takes into account light coming directly from sources, as well as light redirected through other parts of the scene. This allows you to create various optical effects, such as the flow of color from one surface to another, indirect lighting and caustics. The photon display method is designed for functionally defined scenes using graphics processors, thereby achieving an interactive rendering mode. The aim of the work is to implement a method for displaying photons in interactive rendering mode using graphics processors. It was necessary to find a compromise between physical accuracy and the speed of displaying scenes. As the number of parallel computing cores increases, traditional programming models become less efficient. GPUs based on the parallel processing model are being expanded by adding improved support for general-purpose computing to their cores. CUDA is a multicore computing platform. GPU-enabled hardware provides low-cost access to many computations. The method presented in this paper extends the photon-mapping algorithm for the CUDA platform to volumetric photon mapping. The beam brightness estimate used to efficiently calculate the density is recovered with the correct physical parameters. Photons are traced and bandwidth is selected. Performance increases due to calculations on multiple cores and the use of large compacted blocks.