Computing On Graphics Processing Units Research Articles

High-performance computing for computational modelling in built environment-related studies – a scientometric review

PurposeThis paper aims to present the result of a scientometric analysis conducted using studies on high-performance computing in computational modelling. This was done with a view to showcasing the need for high-performance computers (HPC) within the architecture, engineering and construction (AEC) industry in developing countries, particularly in Africa, where the use of HPC in developing computational models (CMs) for effective problem solving is still low.Design/methodology/approachAn interpretivism philosophical stance was adopted for the study which informed a scientometric review of existing studies gathered from the Scopus database. Keywords such as high-performance computing, and computational modelling were used to extract papers from the database. Visualisation of Similarities viewer (VOSviewer) was used to prepare co-occurrence maps based on the bibliographic data gathered.FindingsFindings revealed the scarcity of research emanating from Africa in this area of study. Furthermore, past studies had placed focus on high-performance computing in the development of computational modelling and theory, parallel computing and improved visualisation, large-scale application software, computer simulations and computational mathematical modelling. Future studies can also explore areas such as cloud computing, optimisation, high-level programming language, natural science computing, computer graphics equipment and Graphics Processing Units as they relate to the AEC industry.Research limitations/implicationsThe study assessed a single database for the search of related studies.Originality/valueThe findings of this study serve as an excellent theoretical background for AEC researchers seeking to explore the use of HPC for CMs development in the quest for solving complex problems in the industry.

Direct numerical simulations of supersonic compression-expansion slope with a multi-GPU parallel algorithm

In order to efficiently perform the direct numerical simulation for shock wave boundary layer interference, a large grid computing scale is needed, and a single graphics processing unit cannot meet the urgent needs of computing. This paper proposes a parallel algorithm based on the architecture of multiple graphics processing units to effectively deal with the direct numerical simulation for shock wave boundary layer interference. The algorithm achieves a high overlap between graphics processing unit computation and boundary data exchange to improve the parallel efficiency when the grid computing scale is large. The working principle of the multi-graphics-processing-unit parallel algorithm is introduced, and the accuracy and feasibility of the algorithm are verified. Finally, through the application of the algorithm in the research of the compression-expansion slope boundary layer, fine flow structures such as large-scale vortex structure, shocklet, and attached shock wave can be captured. As a conclusion, the multi-graphics-processing-unit parallel algorithm proposed in this paper can accurately display the flow field of shock wave boundary layer interference.

Assessment of the Impact of Sand Mining on Bottom Morphology in the Mekong River in An Giang Province, Vietnam, Using a Hydro-Morphological Model with GPU Computing

Sand mining, among the many activities that have significant effects on the bed changes of rivers, has increased in many parts of the world in recent decades. Numerical modeling plays a vital role in simulation in the long term; however, computational time remains a challenge. In this paper, we propose a sand mining component integrated into the bedload continuity equation and combine it with high-performance computing using graphics processing units to boost the speed of the simulation. The developed numerical model is applied to the Mekong river segment, flowing through Tan Chau Town, An Giang Province, Vietnam. The 20 years from 1999 to 2019 is examined in this study, both with and without sand mining activities. The results show that the numerical model can simulate the bed change for the period from 1999 to 2019. By adding the sand mining component (2002–2006), the bed change in the river is modeled closely after the actual development. The Tan An sand mine in the area (2002–2006) caused the channel to deviate slightly from that of An Giang and created a slight erosion channel in 2006 (−23 m). From 2006 to 2014, although Tan An mine stopped operating, the riverbed recovered quite slowly with a small accretion rate (0.25 m/year). However, the Tan An sand mine eroded again from 2014–2019 due to a lack of sand. In 2014, in the Vinh Hoa communes, An Giang Province, the Vinh Hoa sand mine began to operate. The results of simulating with sand mining incidents proved that sand mining caused the erosion channel to move towards the sand mines, and the erosion speed was faster when there was no sand mining. Combined with high-performance computing, harnessing the power of accelerators such as graphics processing units (GPUs) can help run numerical simulations up to 23x times faster.

GPU acceleration of MPAS microphysics WSM6 using OpenACC directives: Performance and verification

In this study, we accelerated a microphysics scheme embedded within the Model for Prediction Across Scales (MPAS), using OpenACC directives. As one of the most time-consuming physics parameterization schemes, we focused on parallelizing the Weather Research and Forecasting (WRF) single-moment 6-class microphysics scheme (WSM6) onto a graphics processing unit (GPU). We applied several essential methodologies to optimize the performance of WSM6 computation on the GPU, to minimize data transfer between the central processing unit (CPU) and GPU and to reduce the waste of GPU threads during computation. As a result, we achieved GPU runs using 1 T V100 that were 2.38 times faster than 48 message passing interface processes runs, on average. When porting the whole model onto the GPU, we achieved a ×5.71 speed-up in WSM6 computation, except in I/O communication. In addition, the precise verification method distinguished nonlinear chaotic error growth from differences introduced by GPU computation, considering the characteristics of the major output variables from WSM6. We then compared the difference between the CPU and the GPU runs to the difference between CPU runs with different compilers. Moreover, we examined bias in these differences, which can distort the climatology of model simulation. Our approach successfully passed the verification process, and this represents the successful application of GPU acceleration to realistic full-model integration of MPAS.

Hybrid MPI and CUDA Parallelization for CFD Applications on Multi-GPU HPC Clusters

Graphics processing units (GPUs) have a strong floating-point capability and a high memory bandwidth in data parallelism and have been widely used in high-performance computing (HPC). Compute unified device architecture (CUDA) is used as a parallel computing platform and programming model for the GPU to reduce the complexity of programming. The programmable GPUs are becoming popular in computational fluid dynamics (CFD) applications. In this work, we propose a hybrid parallel algorithm of the message passing interface and CUDA for CFD applications on multi-GPU HPC clusters. The AUSM + UP upwind scheme and the three-step Runge–Kutta method are used for spatial discretization and time discretization, respectively. The turbulent solution is solved by the K−ω SST two-equation model. The CPU only manages the execution of the GPU and communication, and the GPU is responsible for data processing. Parallel execution and memory access optimizations are used to optimize the GPU-based CFD codes. We propose a nonblocking communication method to fully overlap GPU computing, CPU_CPU communication, and CPU_GPU data transfer by creating two CUDA streams. Furthermore, the one-dimensional domain decomposition method is used to balance the workload among GPUs. Finally, we evaluate the hybrid parallel algorithm with the compressible turbulent flow over a flat plate. The performance of a single GPU implementation and the scalability of multi-GPU clusters are discussed. Performance measurements show that multi-GPU parallelization can achieve a speedup of more than 36 times with respect to CPU-based parallel computing, and the parallel algorithm has good scalability.

Optimizing non-coalesced memory access for irregular applications with GPU computing

General purpose graphics processing units (GPGPUs) can be used to improve computing performance considerably for regular applications. However, irregular memory access exists in many applications, and the benefits of graphics processing units (GPUs) are less substantial for irregular applications. In recent years, several studies have presented some solutions to remove static irregular memory access. However, eliminating dynamic irregular memory access with software remains a serious challenge. A pure software solution without hardware extensions or offline profiling is proposed to eliminate dynamic irregular memory access, especially for indirect memory access. Data reordering and index redirection are suggested to reduce the number of memory transactions, thereby improving the performance of GPU kernels. To improve the efficiency of data reordering, an operation to reorder data is offloaded to a GPU to reduce overhead and thus transfer data. Through concurrently executing the compute unified device architecture (CUDA) streams of data reordering and the data processing kernel, the overhead of data reordering can be reduced. After these optimizations, the volume of memory transactions can be reduced by 16.7%–50% compared with CUSPARSE-based benchmarks, and the performance of irregular kernels can be improved by 9.64%–34.9% using an NVIDIA Tesla P4 GPU.

The Immersed Boundary-Lattice Boltzmann Method Parallel Model for Fluid-Structure Interaction on Heterogeneous Platforms

Immersed boundary-lattice Boltzmann method (IB-LBM) has become a popular method for studying fluid-structure interaction (FSI) problems. However, the performance issues of the IB-LBM have to be considered when simulating the practical problems. The Graphics Processing Units (GPUs) from NVIDIA offer a possible solution for the parallel computing, while the CPU is a multicore processor that can also improve the parallel performance. This paper proposes a parallel algorithm for IB-LBM on a CPU-GPU heterogeneous platform, in which the CPU not only controls the launch of the kernel function but also performs calculations. According to the relatively local calculation characteristics of IB-LBM and the features of the heterogeneous platform, the flow field is divided into two parts: GPU computing domain and CPU computing domain. CUDA and OpenMP are used for parallel computing on the two computing domains, respectively. Since the calculation time is less than the data transmission time, a buffer is set at the junction of two computational domains. The size of the buffer determines the number of the evolution of the flow field before the data exchange. Therefore, the number of communications can be reduced by increasing buffer size. The performance of the method was investigated and analyzed using the traditional metric MFLUPS. The new algorithm is applied to the computational simulation of red blood cells (RBCs) in Poiseuille flow and through a microchannel.

Soil Monitor: an internet platform to challenge soil sealing in Italy

AbstractThis work proposes a new type of web‐based geospatial decision support system—called Soil Monitor—which provides a multidisciplinary operational tool useful for a multi‐stakeholder community to challenge soil sealing. Various technological and technical features of Soil Monitor are presented and discussed with particular reference to the combination of WebGIS with on‐the‐fly geospatial processing based on GPU computing, specifically designed to allow real‐time requests. In addition, we present three quantitative analysis of soil sealing at three different scales. The study at national level illustrated that the type of land use strongly affected soil‐sealing dynamics. Indeed, the class complex cultivation pattern resulted the most fragile land use while forests resulted the most resilient one. The study at province level illustrated the key importance of having on‐the‐fly the map of rural fragmentation for any Italian province. This map—quantifying the landscape integrity of the rural and natural areas—enables to better locate both new green corridors or new urban developments minimizing the environmental impact of human activities. The study at municipality level demonstrated the importance of quantifying a pool of spatial planning indicators and illustrated that (a) the largest urban dispersion occurs in hilly touristic municipalities close to the sea, (b) the increase in soil sealing in rural sites is not related to population growth. As a result, Soil Monitor is deemed useful both to support decision making at different spatial scales and to raise awareness in people, professionals, and experts of other fields. Keywords soil sealing, geospatial decision support system, compute unified device architecture, graphical processing unit computing, urban planning.

A GPU-Accelerated Filtered Density Function Simulator of Turbulent Reacting Flows

A new computational methodology is developed for large eddy simulation (LES) of turbulent reacting flows using graphic processing units (GPUs). The LES is based on the filtered density function (FDF) of the scalar-composition in conjunction with a discontinuous Galerkin (DG) discretisation scheme on a structured rectangular mesh. This hybrid solver is developed in a manner suitable for GPU computing. The simulator, as devised, is shown to be of the order of 200 times faster than the serial CPU-based calculations; facilitating the use of FDF for practical applications. The consistency and the accuracy of the methodology are demonstrated by simulations of a temporally developing mixing layer, under both non-reacting and reacting conditions.

Computational Opportunities In The Estimation Of Free-living Temporal Gait Parameters From Wearable Accelerometry Data

Wearable accelereometrs are widely used to monitor free-living physical activity (PA) in the variety of epidemiological and clinical studies. Due to a small size, long battery life and low costs their popularity grew exponentially in recent years. This motivates researchers to seek additional utility of free-living accelerometery outside traditional PA measurements. Arguably, the assessment of mobility and gait parameters is one of the most popular and exciting applications of wearable accelerometers. However, because of the sheer size of free-living, sub-second level accelerometry data, the choice of existing analytical methods and the development of new ones is highly limited by the computational capabilities of modern computers and workstations. In this work, we explore possible solutions to this challenge. Namely, multi-core and graphics processing unit (GPU) computing. As a motivating example, we use the Adaptive Empirical Pattern Transformation (ADEPT), an open-source, dictionary-based pattern recognition method dedicated to the segmentation of walking strides in high-density accelerometry data. PURPOSE: We propose to minimize the computational time of the ADEPT algorithm by application of the GPU computing using inexpensive, desktop workstation equipped with the consumer-grade GPU. METHODS: We use data collected on 5 healthy participants equipped with the wrist-worn tri-axial Actigraph GT3X monitor over 48-hour period at sampling frequency of 100Hz. Next, we deploy the ADEPT algorithm on single-core Intel I9-900K CPU, 8-core CPU and Nvidia RTX 2080 GPU and measure total computation time. RESULTS: Average single-core CPU computation time was equal to 607.70 (sd = 0.3), multicore CPU was 344.40 (sd = 0.8) and GPU was 25.52 (sd = 0.07) seconds. GPU enabled computing allowed for nearly 23 times faster computation with respect to single-core and 14 times with respect to multicore processing. CONCLUSIONS: GPU accelerated computing allows for the application of more complex and time-consuming methods in the statistical analysis of high-density accelerometry data collected in free-living settings over multiple days. Additionally, it creates possibilities for scaling-up computationally intensive methods to much larger samples enabling their application in large epidemiological studies.

Fine-Grained Allocation Algorithm for Sharing Heterogeneous Resources in Data Center

Data in a data center are stored dispersively. The data-oriented task computing disperses big data analysis tasks to different computing nodes. The extensive use of graphics processing unit (GPU) makes it urgent and important to study how to reasonably assign heterogeneous resources to different computing frameworks. We investigate the existing big data computing framework and the GPU computing. Based on the existing cluster resource management model and the GPU management model, we propose a hybrid heterogeneous resource management model that combines CPU resources with GPU resources. The computing nodes manage local resources and implement tasks; the resource management center concertedly manage various computing frameworks. We design and implement a hybrid domain resource sharing and allocation algorithm, which allocates the hybrid domain resources to computing frameworks according to the coordinated use of them so as to fairly share the hybrid domain resources among various computing frameworks and prevent the CPU from too many tasks but the GPU or CPU from resource "hunger". The experimental results show that the allocation algorithm can increase the use of heterogeneous resources and the number of completed tasks by around 15%.

Image Classification using Parallel CPU and GPU Computing

Image classification algorithms such as Convolutional Neural Network used for classifying huge image datasets takes a lot of time to perform convolution operations, thus increasing the computational demand of image processing. Compared to CPU, Graphics Processing Unit (GPU) is a good way to accelerate the processing of the images. Parallelizing multiple CPU cores is also another way to process the images faster. Increasing the system memory (RAM) can also decrease the computational time of image processing. Comparing the architecture of CPU and GPU, the former consists of a few cores optimized for sequential processing whereas the later has thousands of relatively simple cores clocked at approx. 1Ghz. The aim of this project is to compare the performance of parallelized CPUs and a GPU. Python’s Ray library is being used to parallelize multicore CPUs. The benchmark image classification algorithm used in this project is Convolutional Neural Network. The dataset used in this project is Plant Disease Image Dataset. Our results show that the GPU implementation achieves 80% speedup compared to the CPU implementation.

Free viewpoint image generation system using fisheye cameras and a laser rangefinder for indoor robot teleoperation

In robot teleoperation, a lack of depth information often results in collisions between the robots and obstacles in its path or surroundings. To address this issue, free viewpoint images can greatly benefit the operators in terms of collision avoidance as the operators are able to view the robot’s surrounding from the images at arbitrary points, giving them a better depth information. In this paper, a novel free viewpoint image generation system is proposed. One approach to generate free viewpoint images is to use multiple cameras and Light Detection and Ranging (LiDAR). Instead of using the expensive LiDAR, this study utilizes a cost-effective laser rangefinder (LRF) and a characteristic of man-made environments. In other words, we install multiple fisheye cameras and an LRF on a robot. Free viewpoint images are generated under the assumption that walls are perpendicular to the floor. Furthermore, an easy calibration for estimating the poses of the multiple fisheye cameras, the LRF, and the robot model is proposed. Experimental results show that the proposed method can generate free viewpoint images using cameras and an LRF. Finally, the proposed method is primarily implemented using OpenGL Shading Language to utilize a graphics processing unit computation to achieve a real-time processing of the multiple high-resolution images. Supplementary videos and our source code are available at our project page (https://matsuren.github.io/fvp).

Multi-GPU Island-Based Genetic Algorithm

Genetic algorithms are effective in solving many optimization tasks. However, the long execution time associated with it prevents its use in many domains. In this paper, we propose a new approach for parallel implementation of genetic algorithm on graphics processing units (GPUs) using CUDA programming model. This paper introduces a novel implementation of the genetic algorithm exploiting a multi-GPU cluster. The proposed implementation employs an island-based genetic algorithm where every GPU evolves a single island. Recently there has been a growing interest in developing parallel algorithms using graphic processing units (GPU) also referred as GPU computation. Advances in the video gaming industry have led to the production of low-cost, high performance graphics processing units that possess more memory bandwidth and computational capability than central processing units (CPUs). As GPUs are available in personal computers, and they are easy to use and manage through several GPU programming languages, graphics engines are being adopted widely in scientific computing.

GPU Computing with Python: Performance, Energy Efficiency and Usability

In this work, we examine the performance, energy efficiency, and usability when using Python for developing high-performance computing codes running on the graphics processing unit (GPU). We investigate the portability of performance and energy efficiency between Compute Unified Device Architecture (CUDA) and Open Compute Language (OpenCL); between GPU generations; and between low-end, mid-range, and high-end GPUs. Our findings showed that the impact of using Python is negligible for our applications, and furthermore, CUDA and OpenCL applications tuned to an equivalent level can in many cases obtain the same computational performance. Our experiments showed that performance in general varies more between different GPUs than between using CUDA and OpenCL. We also show that tuning for performance is a good way of tuning for energy efficiency, but that specific tuning is needed to obtain optimal energy efficiency.

Efficient ResNet Model to Predict Protein-Protein Interactions With GPU Computing

Protein-protein interactions (PPI) play an important role in the cell activities of organisms. The deep research about PPI can help humans understand the mechanism of life activities and apply protein functions better. Nowadays, PPI prediction algorithms based on amino acid sequences using the recurrent neural network (RNN) can overcome the disadvantages of traditional biological experimental methods and achieve high accuracy. However, these algorithms are usually time-consuming and cannot take full advantage of graphics processing units (GPU) with efficient computation performance to accelerate PPI prediction, because the RNN model considers the time series of sequences. In this paper, we propose an efficient algorithm based on the residual network (ResNet) model to predict PPI (ResPPI). Our algorithm uses the embedding method to represent amino acid sequences, combining the advantages of powerful feature extraction capabilities of the ResNet with deep layers and GPU performance. The experimental results show that the ResPPI algorithm can ensure high accuracy and reduce training time greatly. Based on the ordinary GPU device, compared with the state-of-the-art LSTM model, the speed of the ResPPI algorithm is five times faster than that of the LSTM, whereas the ResPPI algorithm can achieve similar accuracy to the LSTM. Besides, in the case of unbalanced datasets, the ResPPI algorithm can perform better.

Real-Time Lung Tumor Tracking Using a CUDA Enabled Nonrigid Registration Algorithm for MRI.

Objective: This study intends to develop an accurate, real-time tumor tracking algorithm for the automated radiation therapy for cancer treatment using Graphics Processing Unit (GPU) computing. Although a previous moving mesh based tumor tracking approach has been shown to be successful in delineating the tumor regions from a sequence of magnetic resonance image, the algorithm is computationally intensive and its computation time on standard Central Processing Unit (CPU) processors is too slow to be used clinically especially for automated radiation therapy system. Method: A re-implementation of the algorithm on a low-cost parallel GPU-based computing platform is utilized to accelerate this computation at a speed that is amicable to clinical usages. Several components in the registration algorithm such as the computation of similarity metric are inherently parallel which fits well with the GPU parallel processing capabilities. Solving a partial differential equation numerically to generate the mesh deformation is one of the computationally intensive components which has been accelerated by utilizing a much faster shared memory on the GPU. Results: Implemented on an NVIDIA Tesla K40c GPU, the proposed approach yielded a computational acceleration improvement of over 5 times its implementation on a CPU. The proposed approach yielded an average Dice score of 0.87 evaluated over 600 images acquired from six patients. Conclusion: This study demonstrated that the GPU computing approach can be used to accelerate tumor tracking for automated radiation therapy for mobile lung tumors. Clinical Impact: Accurately tracking mobile tumor boundaries in real-time is important to automate radiation therapy and the proposed study offers an excellent option for fast tumor region tracking for cancer treatment.

Elastic full-waveform inversion based on GPU accelerated temporal fourth-order finite-difference approximation

Elastic full-waveform inversion (FWI) estimates the subsurface velocity model by inverting multi-component seismic data. Most of the current elastic FWI solves the elastic wave equation by a temporal second-order finite-difference time-domain (FDTD) method. However, the traditional temporal second-order FDTD introduces large temporal dispersion in the case of a large time-stepping size. To suppress the temporal dispersion, a relatively small time step should be used, which increases the computational cost of FWI. We introduce a temporal fourth-order and spatial arbitrary even-order FDTD method for elastic wave modeling and inversion. The temporal fourth-order scheme can simulate a highly accurate wavefield, even when a large time step is used for temporal extrapolation. Compared with the traditional temporal second-order FDTD scheme, the temporal fourth-order FDTD enables a larger time step for its more relaxed stability condition and smaller dispersion. As a result, FWI with temporal fourth-order FDTD scheme can achieve high accuracy inversion without reducing much computational efficiency. We run our elastic FWI codes on a Graphic Processing Unit (GPU) computing platform, and to accelerate the GPU computing, we propose to use the GPU shared memory to reduce the data access delay. By transferring the data frequently accessed for the FDTD approximations, from the global memory to the shared memory, we realize the FDTD calculation in a more efficient way. Forward modeling and FWI examples are carried out to confirm the effectiveness of our elastic FWI.

Deep learning–driven permeability estimation from 2D images

Current micro-CT image resolution is limited to 1–2 microns. A recent study has identified that at least 10 image voxels are needed to resolve pore throats, which limits the applicability of direct simulations using the digital rock (DR) technology to medium-to-coarse–grained rocks (i.e., rocks with permeability > 100 mD). On the other hand, 2D high-resolution colored images such as the ones obtained from transmitted light microscopy delivers a much higher resolution (approximately 0.6 microns). However, reliable and efficient workflows to jointly utilize full-size 2D images, measured 3D core-plug permeabilities, and 2D direct pore-scale ow simulations on 2D images within a predictive framework for permeability estimation are lacking. In order to close this gap, we have developed a state-of-the-art deep learning (DL) algorithm for the direct prediction of permeability from 2D images. We take advantage of the computing graphics processing units (GPUs) in our implementation of this algorithm. The trained DL model predicts properties accurately within seconds, and therefore, provide a significant speeding up simulation workflow. A real-life dataset is used to demonstrate the applicability and versatility of the proposed method.

Interactive OCT-Based Tooth Scan and Reconstruction.

Digital dental reconstruction can be a more efficient and effective mechanism for artificial crown construction and period inspection. However, optical methods cannot reconstruct those portions under gums, and X-ray-based methods have high radiation to limit their applied frequency. Optical coherence tomography (OCT) can harmlessly penetrate gums using low-coherence infrared rays, and thus, this work designs an OCT-based framework for dental reconstruction using optical rectification, fast Fourier transform, volumetric boundary detection, and Poisson surface reconstruction to overcome noisy imaging. Additionally, in order to operate in a patient’s mouth, the caliber of the injector is small along with its short penetration depth and effective operation range, and thus, reconstruction requires multiple scans from various directions along with proper alignment. However, flat regions, such as the mesial side of front teeth, may not have enough features for alignment. As a result, we design a scanning order for different types of teeth starting from an area of abundant features for easier alignment while using gyros to track scanned postures for better initial orientations. It is important to provide immediate feedback for each scan, and thus, we accelerate the entire signal processing, boundary detection, and point-cloud alignment using Graphics Processing Units (GPUs) while streamlining the data transfer and GPU computations. Finally, our framework can successfully reconstruct three isolated teeth and a side of one living tooth with comparable precisions against the state-of-art method. Moreover, a user study also verifies the effectiveness of our interactive feedback for efficient and fast clinic scanning.