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Authenticated encryption with associated data (AEAD) has become prominent over time because it offers authenticity and confidentiality simultaneously. In 2018, the National Institute of Standards and Technology (NIST) initiated a competition to standardize lightweight AEAD and hash functions, with Ascon as the final winner among the 10 finalists. Numerous prior works evaluated their performance on FPGA and ASIC, but not on a parallel architecture like GPU, which is a common accelerator already found in many existing cloud servers. In this work, the first GPU implementation of the NIST AEAD finalists is proposed. Several GPU implementation techniques applicable to all AEAD schemes are presented, along with novel techniques for some specific schemes to enhance throughput performance. Experimental results show that all NIST AEAD finalists can achieve high throughput (up to 111.53M AEAD per second), approximately 142.19% and 72.65% improvement compared to unoptimized GPU version, and the investigated FPGA results respectively. [ABSTRACT FROM AUTHOR] more...

ABSTRACTTechniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time. more...

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors. [ABSTRACT FROM AUTHOR] more...

Solving a significant number of differential-algebraic equations is essential for conducting transient stability simulations on a complex and interconnected electrical power system. Because of the increasing size and intricacy of modern grids, traditional sequential simulation methods are becoming more computationally challenging and time-consuming to use for dynamic simulations. This research aims to examine parallelism techniques from an innovative algorithm viewpoint to the hardware level by utilizing multiple graphics processing units as the main computational tool designed to simulate the dynamic behaviour of power systems on a large scale. Our approach features a novel hybrid algorithm to improve the dynamic simulations of grids with significant renewable energy integration. The algorithm employs a Schwarz-based approach in its initial decomposition phase to isolate synchronous machines and renewable sources from the grid. This separation enhances the algorithm's ability to manage these components effectively. Subsequently, each subsystem undergoes further division into subdomains in the second stage, with separate computations carried out using the Schur-complement technique. The simulations were conducted on various test systems, with a maximum of 25,000 buses, 8,000 synchronous generators, and 256 PV farms all modelled in detail. We introduce a GPU-oriented preprocessing and vectorization parallelization method for the implementation of our two-stage algorithm. This approach provides the flexibility to tailor parallelization techniques, including nested for loops and kernel pragmas, to craft a hardware solution that enhances the performance of our dynamic domain decomposition algorithm. Simulation outcomes have verified that this approach can yield a notable 7.8x acceleration in speed when executed on an NVIDIA GeForce RTX 2070 SUPER GPU. more...

In this study, we proposed a novel GPU-based solution for modelling two-dimensional inviscid and viscous compressible supersonic/hypersonic flows. Texture and surface pointers are used to access GPU memory locations. For effective and efficient use of surface pointers, we grouped multiple 2D arrays referenced and indexed by a single 3D surface pointer. To enable the proposed solver for double-precision calculations, two consecutive 32-bit memory locations were grouped to maintain the efficiency of surface pointers while taking advantage/accuracy of 64-bit calculations. Resolving data and computation dependencies for parallel applications is another complex task that is the focus of this study. Computation dependencies have been solved by using multiple mutually synchronized GPU kernels and executing them sequentially using the GPU default stream to ensure that all relevant data is available to the threads or computed before they actually use it. Consequently, there is no intra-core data dependency in our proposed approach, while inter-core data dependency is successfully solved by stringing multiple kernels together. Using NVIDIA GTX 660 GPUs, we achieved 20x speedup compared to traditional Core i5® computers. This speedup is the result of the Surface Pointer's GPU capabilities for double precision computations. The simulation results are also consistent with the experimental and numerical results of this study. [ABSTRACT FROM AUTHOR] more...

Dynamic contingency analysis (DCA) for modern power systems is fundamental to help researchers and operators look ahead of potential issues, arrange operational plans, and improve system stabilities. However, since the system size and the number of contingency scenarios continue to increase, pursuing more effective computational performance faces many difficulties, such as slow algorithms and limited computing resources. This research accelerates the intensive computations of massive DCAs by implementing a two-level hierarchical computing architecture on graphical processing units (GPUs). The performance of the designed method is examined using four test systems of different sizes and compared with a CPU-based parallel approach. The results show up to 2.8x speedup using one GPU and 4.2x speedup using two GPUs, respectively. More accelerations can be observed once more GPUs are configured. It demonstrates that the proposed architecture can significantly enhance the overall computational performance of massive DCAs while maintaining a strong scaling capability under various resource configurations. more...

Recently, renewable energy has emerged as an attractive means to reduce energy consumption costs for deep learning (DL) job processing in modern GPU-based clusters. In this paper, we propose a novel Renewable-Aware Frequency Scaling (RA-FS) approach for energy-efficient DL clusters. We have developed a real-time GPU core and memory frequency scaling method that finely tunes the training performance of DL jobs while maximizing renewable energy utilization. We introduce quantitative metrics: Deep Learning Job Requirement (DJR) and Deep Learning Job Completion per Slot (DJCS) to accurately evaluate the service quality of DL job processing. Additionally, we present a log-transformation technique to convert our non-convex optimization problem into a solvable one, ensuring the rigorous optimality of the derived solution. Through experiments involving deep neural network (DNN) model training jobs such as SqueezeNet, PreActResNet, and SEResNet on NVIDIA GPU devices like RTX3060, RTX3090, and RTX4090, we validate the superiority of our RA-FS approach. The experimental results show that our approach significantly improves performance requirement satisfaction by about 71% and renewable energy utilization by about 31% on average, compared to recent competitors. more...

Techniques for the semantic segmentation of remotely sensed imageries for building footprint identification have been widely studied and several supervised and unsupervised techniques have been proposed. The ability to perform online mapping and accurate segmentation on a large scale by taking into account the multifariousness inherent in aerial images has important implications. In this paper we propose a new method for building footprint identification using multiresolution analysis-based self-attention technique. The scheme is promising to be robust in the face of variability inherent in remotely sensed images by virtue of the capability to extract features at multiple scales and focusing on areas containing meaningful information. We demonstrate the robustness of the proposed method by comparing it against several state-of-the-art techniques using aerial imagery with varying spatial resolution and building clutter and it achieves better accuracy around 95% even under widely disparate image characteristics. We also evaluate the ability for online mapping on an embedded graphic processing unit (GPU) and compare it against different compute engines and it is found that the proposed method on GPU outperforms the other methods in terms of accuracy and processing time. [ABSTRACT FROM AUTHOR] more...

Introduction: Wearable assistive devices for the visually impaired whose technology is based on video camera devices represent a challenge in rapid evolution, where one of the main problems is to find computer vision algorithms that can be implemented in low-cost embedded devices.Objectives and Methods: This work presents a Tiny You Only Look Once architecture for pedestrian detection, which can be implemented in low-cost wearable devices as an alternative for the development of assistive technologies for the visually impaired.Results: The recall results of the proposed refined model represent an improvement of 71% working with four anchor boxes and 66% with six anchor boxes compared to the original model. The accuracy achieved on the same data set shows an increase of 14% and 25%, respectively. The F1 calculation shows a refinement of 57% and 55%. The average accuracy of the models achieved an improvement of 87% and 99%. The number of correctly detected objects was 3098 and 2892 for four and six anchor boxes, respectively, whose performance is better by 77% and 65% compared to the original, which correctly detected 1743 objects.Discussion: Finally, the model was optimized for the Jetson Nano embedded system, a case study for low-power embedded devices, and in a desktop computer. In both cases, the graphics processing unit (GPU) and central processing unit were tested, and a documented comparison of solutions aimed at serving visually impaired people was performed.Conclusion: We performed the desktop tests with a RTX 2070S graphics card, and the image processing took about 2.8 ms. The Jetson Nano board could process an image in about 110 ms, offering the opportunity to generate alert notification procedures in support of visually impaired mobility. more...

We present a new model called GPUFLOW for the modeling and simulation of geophysical flows. GPUFLOW, which is based on the cellular automaton paradigm, features a physical model for the thermal and rheological evolution of lava flows (including temperature-dependent emissivity and cooling by radiation and air convection), support for debris flows without thermal dependency, a parallel implementation on graphic processing units (GPUs), and a simpler and computationally more efficient solution to the grid bias problem. Here, we describe the physical–mathematical model implemented in GPUFLOW and estimate the influence of input data on the flow emplacement through different synthetic test cases. We also perform a validation using two real applications: a debris flow that occurred in July 2006 in the Dolomites (Italy) and the December 2018 lava flow from the Etna volcano. GPUFLOW's reliability prediction is accomplished by fitting the simulation with the actual flow fields, obtaining average values between ~55% and 75%. [ABSTRACT FROM AUTHOR] more...

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Passive seismic exploration is an environmentally friendly, economical, and highly accessible exploration method that is widely used in different-scale subsurface imaging. Retrieving reflections from ambient noise is a newly developed passive seismic method that can be used in many fields such as mineral exploration and near-surface imaging, but the interferometry calculation is time-consuming because it requires a longer period of data acquisition to improve the signal-to-noise ratio of the retrieved reflections. In this study, we introduced a graphical processing unit (GPU)- based implementation of seismic interferometry for retrieving reflection responses from passive source seismic recordings. Because all traces are involved in the computation process, but the size of passive source data often exceeds available memory, repeated disk reads lead to a decrease in computational efficiency. We design a strategy of grouping computations followed by stacking to minimize disk input and output, simultaneously keeping the memory requirements low. Passive source data is read and written only once, and there is no requirement for the memory size to be greater than the data size. Additionally, acceleration technologies such as asynchronous execution, asynchronous memory transfer, and GPU-accelerated libraries are used. We test the efficiency using short data where the number of sampling points per trace is on the order of 30,000, and long data, where the number of sampling points per trace is on the order of 30,000,000. For short data, the average speedup is 4 compared with a multi-core central processing unit (CPU); for long data, the speedup can reach 24. The GPU-based implementation of interferometry greatly reduces the calculation time for retrieving reflections from passive source seismic recordings, providing a solution to the problem of large calculation in three-dimensional (3D) passive source reflection exploration. • Seismic interferometry was designed using a GPU to retrieve reflection responses. • The average speedup was more than 20. • The correctness was verified through numerical simulation and field data. [ABSTRACT FROM AUTHOR] more...

Several causes make brain cancer identification a challenging task for neurosurgeons during the surgical procedure. The surgeons' naked eye sometimes is not enough to accurately delineate the brain tumor location and extension due to its diffuse nature that infiltrates in the surrounding healthy tissue. For this reason, a support system that provides accurate cancer delimitation is essential in order to improve the surgery outcomes and hence the patient's quality of life. The brain cancer detection system developed as part of the “HypErspectraL Imaging Cancer Detection” (HELICoiD) European project meets this requirement exploiting a non-invasive technique suitable for medical diagnosis: the hyperspectral imaging (HSI). A crucial constraint that this system has to satisfy is providing a real-time response in order to not prolong the surgery. The large amount of data that characterizes the hyperspectral images, and the complex elaborations performed by the classification system make the High Performance Computing (HPC) systems essential to provide real-time processing. The most efficient implementation developed in this work, which exploits the Graphic Processing Unit (GPU) technology, is able to classify the biggest image of the database (worst case) in less than three seconds, largely satisfying the real-time constraint set to 1 minute for surgical procedures, becoming a potential solution to implement hyperspectral video processing in the near future. more...

We report on the acceleration of annual daylighting simulations for fenestration systems in the Radiance ray-tracing program. The algorithm was optimized to reduce both the redundant data input/output operations and the floating-point operations. To further accelerate the simulation speed, the calculation for matrix multiplications was implemented using parallel computing on a graphics processing unit. We used OpenCL, which is a cross-platform parallel programming language. Numericalexperiments show that the combination of the above measures can speed up the annual daylighting simulations 101.7 times or 28.6 times when the sky vector has 146 or 2306 elements, respectively. more...

We present a new model called GPUFLOW for the modeling and simulation of geophysical flows. GPUFLOW, which is based on the cellular automaton paradigm, features a physical model for the thermal and rheological evolution of lava flows (including temperature-dependent emissivity and cooling by radiation and air convection), support for debris flows without thermal dependency, a parallel implementation on graphic processing units (GPUs), and a simpler and computationally more efficient solution to the grid bias problem. Here, we describe the physical–mathematical model implemented in GPUFLOW and estimate the influence of input data on the flow emplacement through different synthetic test cases. We also perform a validation using two real applications: a debris flow that occurred in July 2006 in the Dolomites (Italy) and the December 2018 lava flow from the Etna volcano. GPUFLOW’s reliability prediction is accomplished by fitting the simulation with the actual flow fields, obtaining average values between ~55% and 75%. more...

This paper presents a GPU based parallel implementation for the non-uniform illumination image restoration method, which uses a retinex based algorithm to decompose the original image into brightness and reflectance components, and adjusts the brightness value through an adaptive gamma correction and nonparametric mapping to achieve the restoration. Specifically, we parallelize the improved retinex algorithm on GPU to extract the brightness value of each pixel. After that, the probability of different brightness range is counted through each block to the entire image to reduce the competition of memory access. Finally, we use two different parallel reduce methods to calculate the probability density and cumulative density of brightness value and generate the mapping curve. The experiment conducted on three different GPUs and two CPUs with different resolution images shows that our method can process a 1024 × 2048 image in 1.024 ms on RTX2080Ti, indicates a great potential for real-time application. [ABSTRACT FROM AUTHOR] more...

The chemical-potential multiphase lattice Boltzmann method (CP-LBM) has the advantages of satisfying the thermodynamic consistency and Galilean invariance, and it realizes a very large density ratio and easily expresses the surface wettability. Compared with the traditional central difference scheme, the CP-LBM uses the Thomas algorithm to calculate the differences in the multiphase simulations, which significantly improves the calculation accuracy but increases the calculation complexity. In this study, we designed and implemented a parallel algorithm for the chemical-potential model on a graphic processing unit (GPU). Several strategies were used to optimize the GPU algorithm, such as coalesced access, instruction throughput, thread organization, memory access, and loop unrolling. Compared with dual-Xeon 5117 CPU server, our methods achieved 95 times speedup on an NVIDIA RTX 2080Ti GPU and 106 times speedup on an NVIDIA Tesla P100 GPU. When the algorithm was extended to the environment with dual NVIDIA Tesla P100 GPUs, 189 times speedup was achieved and the workload of each GPU reached 96%. [ABSTRACT FROM AUTHOR] more...

The rapid expansion in scale of power systems and the emergence of new smart grid technologies continuously increase computational complexity of power system simulations. Graphic processing unit (GPU), which features massive concurrent threads and excellent floating-point performance, brings new opportunities into power system simulations. This paper introduces an advanced GPU-CPU based parallel power flow (PF) approach by adopting vectorization parallelization and sparse techniques. Specifically, the root cause behind sparsity property of PF and its impacts on the first two steps of Newton-Raphson (NR) based PF calculation, i.e., forming nodal power mismatch vector and updating Jacobian matrix, are quantitatively analyzed. Moreover, a novel GPU-CPU based parallel PF approach is presented, which effectively integrates advanced GPU-based vectorization parallelization and sparse techniques to accelerate performance of PF calculations. Numerical studies validate the effectiveness of various customized parallel schemes for individual key steps of the proposed NR-based parallel PF approach. [ABSTRACT FROM AUTHOR] more...

Privacy amplification is an indispensable step in postprocessing of continuous-variable quantum key distribution (CV-QKD), which is used to distill unconditional secure keys from identical corrected keys between two distant legal parties. The processing speed of privacy amplification has a significant effect on the secret key rate of a CV-QKD system. We report the high-speed parallel implementation of the length-compatible privacy amplification algorithm based on a graphic processing unit. The length-compatible algorithm is used to satisfy the security requirements of privacy amplification at different transmission distances when considering finite-size effect. We achieve the speed of privacy amplification over 1 Gb/s at arbitrary input length and the speed is one to two orders of magnitude faster than previous demonstrations, which supports the high-speed real-time CV-QKD system and ensures the security of privacy amplification. more...

The development of smart grid and the increasing scale of power system bring much pressure to the electromagnetic transient simulation of a power system. The graphic processing unit (GPU), which features the massive concurrent threads and excellent floating point performance, brings a new chance to the area of power system simulation. This study introduces a parallel lower triangular and upper triangular decomposition algorithm and calculation strategy of electromagnetic transient simulation based on GPU. In this scheme, the GPU is mainly used to do the computationally intensive part of the simulation in parallel on its built-in multiple processing cores, and the CPU is assigned for updating history terms and flow control of the simulation. By comparing with the results simulating by the CPU-only implementations, the validity and efficiency of the proposed method are verified. more...

Kirchhoff-based depth migration is in great demand for imaging at large scales and high-density seismic data exploration. A Graphic Processing Unit, or GPU, can compose a heterogeneous hardware that contains a traditional Central Processing Unit (CPU), and it can greatly accelerate migration calculations. An improved acceleration method is proposed here that uses overlapped ray tracing, which provides two-way travel times, and an imaging kernel. In our method, ray tracing is calculated by the CPU due to its complicated divergences. The imaging kernel is passed to the GPU by unlooping the imaging volume. Streams and asynchronous calculation functions are used to overlap the imaging kernel of a previous trace to the ray tracing of the next trace. The nvprof profiling tool, along with the elapsed time, are used to measure the efficiency of the accelerated work, and it is found that the GPU code results in a speedup factor of 60 relative to single thread CPU code, and when using the overlapped computation, the GPU efficiency is improved by 30%. This method shows great potential for further application in large scale seismic data imaging with GPU. • The Kirchhoff depth migration was accelerated by Graphic processing unit. • The imaging kernel run on GPU and the ray tracing run on CPU. •. The 3D texture memory was used to travel time interpolation and transfer. • The imaging kernel and ray tracing were overlapped by stream and asynchronous function. [ABSTRACT FROM AUTHOR] more...

The development of smart grid and the increasing scale of power system bring much pressure to the electromagnetic transient simulation of a power system. The graphic processing unit (GPU), which features the massive concurrent threads and excellent floating point performance, brings a new chance to the area of power system simulation. This study introduces a parallel lower triangular and upper triangular decomposition algorithm and calculation strategy of electromagnetic transient simulation based on GPU. In this scheme, the GPU is mainly used to do the computationally intensive part of the simulation in parallel on its built-in multiple processing cores, and the CPU is assigned for updating history terms and flow control of the simulation. By comparing with the results simulating by the CPU-only implementations, the validity and efficiency of the proposed method are verified. [ABSTRACT FROM AUTHOR] more...

This paper presents a novel fast neighbor searching method. By using this method,fluid simulation based on smooth particle hydrodynamics(SPH) can be parallelized easily and run on graphic processing unit(GPU) with high efficiency. The neighbor searching method can search two or more kinds of particles,while saving their information in the same background grid. Ghost boundary particles are introduced to improve the accuracy of boundaries,which can enhance the fidelity of the fluid simulation. Experiments show that the proposed method is more efficient than the traditional SPH method based on GPU. more...

Artificial intelligence (AI) is one of the core drivers of industrial development and a critical factor in promoting the integration of emerging technologies, such as graphic processing unit, Internet of Things, cloud computing, and the blockchain, in the new generation of big data and Industry 4.0. In this paper, we construct an extensive survey over the period 1961-2018 of AI and deep learning. The research provides a valuable reference for researchers and practitioners through the multi-angle systematic analysis of AI, from underlying mechanisms to practical applications, from fundamental algorithms to industrial achievements, from current status to future trends. Although there exist many issues toward AI, it is undoubtful that AI has become an innovative and revolutionary assistant in a wide range of applications and fields. [ABSTRACT FROM AUTHOR] more...

Parallel discovery of inherent clusters using massively threaded architectures is the solution for handling computational challenges raised by fat datasets in cluster analysis. The Graphics Processing Unit and Compute Unified Device Architecture form a convincing platform to parallelize clustering algorithms. The parallel K-means algorithm aims at increasing the speedup, but often faces the hitch of falling into local minima. The heuristic search procedure to discover the global optima in the solution space is known as Tabu Search. The K-means clustering solution is fine-tuned by applying parallel implementation of Tabu Search K-means clustering in order to increase efficacy. The aim is to combine optimization characteristic of Tabu Search for calculation of centroids with clustering attitude of K-means and to enhance the solution using processing power of GPU. The parallelization strategy used exhibits increase in speedup. The parallel Tabu-KM algorithm is tested on standard datasets, and performance is compared with sequential K-means, parallel K-means and sequential Tabu K-means algorithms. The experimental results confirm yet another parallelization technique to unravel data clustering problems. [ABSTRACT FROM AUTHOR] more...

Differential interferometric synthetic aperture radar (DInSAR) is an effective technique to measure the surface displacement caused by strong earthquakes. However, in the area with respect to the most significant deformation along the fault zone, the coherence between pre- and post-earthquake SAR images is completely lost because of the earthquake-induced violent and chaotic destruction on the land surface and consequently, no surface displacement data can be measured. In our previous work, anadaptive local kriging(ALK) was proposed to solve this problem. However, according to the spatial distribution of the reference points for the interpolation procedure of ALK, using moving window with different sizes through all the pixels in the image is time consuming. Thus, ahigh-performance computing(HPC) is needed. As a result, a parallel image interpolation approach, referred as thegraphics processing unit(GPU) based ALK method, to retrieving slant-range surface motion maps derived by DInSAR is proposed in this study. The proposed GPU-ALK makes use of the parallelism of the original ALK architectures to retrieve the loss of data through the fault zone. In this paper, an HPC GPU-ALK is proposed to speed up the interpolation. It makes use of the performance profiling to analyze the serial version of ALK and perform a parallel GPU computation for reducing the computation time efficiently. By employing one NVIDIA TITAN GPU, the proposed GPU-ALK achieves a speedup of 104.32× compared to its CPU counterpart. [ABSTRACT FROM AUTHOR] more...

Privacy amplification is an indispensable step in postprocessing of continuous-variable quantum key distribution (CV-QKD), which is used to distill unconditional secure keys from identical corrected keys between two distant legal parties. The processing speed of privacy amplification has a significant effect on the secret key rate of a CV-QKD system. We report the high-speed parallel implementation of the length-compatible privacy amplification algorithm based on a graphic processing unit. The length-compatible algorithm is used to satisfy the security requirements of privacy amplification at different transmission distances when considering finite-size effect. We achieve the speed of privacy amplification over 1 Gb/s at arbitrary input length and the speed is one to two orders of magnitude faster than previous demonstrations, which supports the high-speed real-time CV-QKD system and ensures the security of privacy amplification. [ABSTRACT FROM AUTHOR] more...

Machine learning model tackles traffic classification effectively. But, it consumes considerable computing resources and computing time, resulting in the difficulty to accommodate large-scale network. In the presented study, GPU-accelerated Support Vector Machines (SVM) is proposed for traffic classification. GPU is used to parallelly calculate the kernel matrix and process the grid traversal of iterative-tuning scheme, in order to accelerate the training and parameters optimization procedure of SVM. Parallel traffic classification is applied to accelerate the classification procedures through the single instruction multiple data paradigm, multithreading and the shared memory of the threads. The experimental results show that the presented method achieves the similar accuracy comparing to the existing CPU-based LibSVM. Furthermore, it ramps up the training speed to 1.53 times and the classification speed to 24 times, which is suitable for the real time classification of high speed backbone networks. [ABSTRACT FROM AUTHOR] more...

Market demands for high-tech products constantly evolve by product specification. To be competitive, a production system must be flexible and reconfigurable when facing mass customization. Flexible assembly line (FAL) enables mixed production with high efficiency. One example is graphic processing unit (GPU) cards. FAL requires comprehensive system design and scheduling to fully utilize the resources. This study adopts agent-based simulation (ABS) modelling for a FAL because of the abilities of ABS in flexibility and scalability. The proposed framework consists of three parts: real environment, virtual environment, and evaluation and analysis. This study uses agent-based simulation modelling to elaborate on sequencing and scheduling performances in the GPU-card assembly line. The Pareto frontier analysis is conducted to resolve conflicts between part tardiness and throughput. • Develop an agent-based simulation (ABS) model for smart flexible assembly lines. • Elaborate on sequencing and scheduling performances by Pareto frontier analysis. • Be flexible to adopt to various manufacturing systems and applications. [ABSTRACT FROM AUTHOR] more...

This study presents an innovative real-time urban microclimate simulation strategy using a large-eddy simulation (LES) solver deployed on a multi-GPU architecture. The present LES solver is developed using a monolithic projection-based method with staggered time discretization, ensuring efficient computation and a high CFL number. It is hosted on a multi-GPU platform using CUDA Fortran and CUDA-aware MPI, which enhances its performance. To capture the complexity of urban geometries, we utilized a building-resolved, wall-modeled LES augmented by an immersed boundary method. First, we validated the results in an isolated building by comparing them with wind tunnel profiles. Next, we evaluated the developed LES solver using an idealized street array. A set of evaluation metrics, namely FAC2 and hit rate, was employed to determine the optimal grid configuration that produces physically valid results. Moreover, we proposed a real-time indicator that provides insight into the interplay among grid resolution, simulation domain size, and the number of GPUs. This facilitates feasibility assessments for real-time simulations. The performance of the LES solver was further tested using real urban geometry, conducting simulations over an area of 10.49 km 2 in Seoul, with a grid resolution of 4 m. The simulation results efficiently highlighted variations in wind patterns with altitude, demonstrating its potential to provide useful wind information for pedestrian-level wind comfort assessment and urban air mobility. [Display omitted] • We developed a real-time urban microclimate simulator with a high-fidelity model. • Wall-modeled large-eddy simulations using immersed boundary method with GIS data. • PaScaL_TDMA and DCT algorithms boost parallel efficiency on multi-GPUs. • A validation & verification study with AIJ data, providing real-time indicators. • Successful real-time simulations of a 10.49 km2 urban area at 4 m resolution. [ABSTRACT FROM AUTHOR] more...

The element by element parallel finite element method (EbE-PFEM) applied to engineering eddy current problem is presented in this paper. Unlike classical finite element method (FEM), only element matrix is needed to store for EbE method. Thereby more storage memory saved. Element by element conjugated gradient (EbE-CG) method is used to solve the equations which are discretized from elements level. Considering the ill-conditioned character of system equations, highly parallel Jacobi preconditioned (JP) method is used to accelerate the convergence. Besides, the process of dealing with boundary condition based on EbE theory is introduced. To validate the method, a 2D eddy current problem in complex frequency domain is used. The numerical analysis is carried out on the graphic processing units (GPU) with a compute unified device architecture (CUDA) parallel programming model to accelerate the convergence. And the results demonstrate that the JP method and GPU platform are effective in solving eddy current field with improved convergence. [ABSTRACT FROM AUTHOR] more...

This paper proposes a hybrid gate-level leakage model for the use with the Monte Carlo (MC) analysis approach, which combines a lookup table (LUT) model with a first-order exponential-polynomial model (first-order model, herein). For the process parameters having strong nonlinear relationships with the logarithm of leakage current, the proposed model uses the LUT approach for the sake of modeling accuracy. For the other process parameters, it uses the first-order model for increased efficiency. During the library characterization for each type of logic gates, the proposed approach determines the process parameters for which it will use the LUT model. And, it determines the number of LUT data points, which can maximize analysis efficiency with acceptable accuracy, based on the user-defined threshold. The proposed model was implemented for gate-level MC leakage analysis using three graphic processing units. In experiments, the proposed approach exhibited the average errors of ¡5% in both mean and standard deviation with reference to SPICE-level MC leakage analysis. In comparison, MC analysis with the first-order model exhibited more than 90% errors. In CPU times, the proposed hybrid approach took only two to five times longer runtimes. In comparison with the full LUT model, the proposed hybrid model was up to one hundred times faster while increasing the average errors by only 3%. Finally, the proposed approach completed a leakage analysis of an OpenSparc T2 core of 4.5 million gates with a runtime of . more...

With the advancement in the device technology and parallel architecture, field-programmable gate arrays (FPGAs) can well perform the speech processing operation. FPGAs have very impressive results, despite their low operating frequency, by completely extracting the parallelism. Nevertheless, recent central processing unit and graphic processing unit (GPU) have also an inherent feature for high performance. In fact, recent GPUs enable dramatic increases in computing performance by harnessing great number of cores. In this context, we seek to analyze the performance of the linear prediction coding algorithm implementation on two different platforms: one based on the GPU NVIDIA GeForce GTX 480 and another on the FPGA Spartan-6. Subsequently, we try to apply several optimization strategies on those platforms. The experimental results highlight the relative robustness or weakness of both these platforms. The tests prove that, for several samples, GPU manages speedups of up to 4× compared to the FPGA and around 48× compared to a sequential execution. [ABSTRACT FROM AUTHOR] more...

To shorten the parameter optimization time of support vector machine(SVM) and improve the efficiency of parameter optimization, the optimization algorithm of SVM parallel parameters was proposed based on graphic processing unit (GPU). The parallel possibility was analyzed for grid search (GS) algorithm and particle swarm optimization (PSO) algorithm. Based on GPU, the parallel methods of GS and PSO were proposed. The experiments were executed on GeForce GT 650M GPU card to conduct 5-cross validation. The results show that compared with the original optimization algorithms, the proposed parallel optimization algorithm can obtain the same optimum performance with greatly reduced execution time. The speed-up ratio can reach 26.85 for parallel PSO algorithm, and the SVM parameter optimization performance is improved greatly. [ABSTRACT FROM AUTHOR] more...

This paper presents an approach based on software-based fault tolerance techniques applied at low abstraction level to detect SEU faults in register files of Graphics Processing Units. SEU faults have a major influence on such architectures, especially affecting register files and cache memory. In order to harden the system's register files, software-based techniques are presented and tuned to detect faults in vector, address, and predicate registers. A fault injection campaign at Register Transfer Level is performed on the register files using a G80 general purpose graphics processing unit running four case-study applications. Results show reduction in errors up to 100% and overhead costs in execution time up to 1.78 times the original values. [ABSTRACT FROM AUTHOR] more...

Adaptive subtraction is an important link for removing surface-related multiples in the wave equation-based method. In this paper, we propose an adaptive multichannel subtraction method based on the L1-norm. We achieve enhanced compensation for the mismatch between the input seismogram and the predicted multiples in terms of the amplitude, phase, frequency band, and travel time. Unlike the conventional L2-norm, the proposed method does not rely on the assumption that the primary and the multiples are orthogonal, and also takes advantage of the fact that the L1-norm is more robust when dealing with outliers. In addition, we propose a frequency band extension via modulation to reconstruct the high frequencies to compensate for the frequency misalignment. We present a parallel computing scheme to accelerate the subtraction algorithm on graphic processing units (GPUs), which significantly reduces the computational cost. The synthetic and field seismic data tests show that the proposed method effectively suppresses the multiples. [ABSTRACT FROM AUTHOR] more...

This paper implements a real-time and directional counting algorithm using the Graphic Processing Unit (GPU) Programming for the purpose of detecting and counting people. We use the Compute Unified Device Architecture (CUDA) as the environment of the GPU programming. The proposed algorithm is implemented for detecting and counting people employing the single virtual line and two virtual lines, respectively, using three video streams and two GPU graphic cards GeForce GT 630 and GeForce GTX 550Ti. We first test the video streams on the algorithm by using GeForce GT 630 together with applying the single virtual line and two virtual lines, respectively. Then, we repeat the same procedures for the GPU graphic card GeForce GTX 550Ti. The obtained experimental results show that our proposed algorithm running on GPU can be successfully programmed and implemented for people detecting and counting problems. [ABSTRACT FROM AUTHOR] more...

Medical imaging has played an important role in helping physicians to make clinical diagnoses. Magnetic resonance imaging technology has been used to image the anatomy of the brain. Typically, image segmentation is utilized to observe the brain's anatomical structures and its changes, and to identify pathological regions. In this paper, we propose an efficient parallel fuzzy c-means clustering algorithm for segmenting images on multiple embedded graphic processing unit systems, NVIDIA TK1. The experimental results demonstrate that the maximum speedups of the proposed algorithm on 15 TK1s greater than 12 times and 7 times than that of fuzzy c-means algorithm with single ARM and Intel Xeon CPUs, respectively. These experimental results show that the proposed algorithm can significantly address the complexity and challenges of the brain magnetic resonance imaging segmentation problem. [ABSTRACT FROM AUTHOR] more...

In recent years, the use of ubiquitous computing has increased continuously so that studies on ubiquitous computing have been conducted in various life fields. Ubiquitous computing provides any services anywhere and anytime through networks conveniently to improve the quality of life of users. Ubiquitous computing is constructed via a variety of invisible sensors, networks, and computing environments. For multi-purpose sensors, deployment of the topology and topology lifetime are very important depending on wired or wireless sensors. Uneven energy consumption due to integrated routing sensors according to topology deployment types is a factor that degrades the quality of service (QoS) about user convenience services and lifetime of total topology. As a result, a number of studies on maximization of topology lifetime have been conducted. However, previous studies focused on deployment environments and limited sensors so that they cannot be deployed to real sites. Therefore, this study proposes an adaptive power management scheme (APMS) that manages sensor power adaptively according to deployment environments to maximize topology lifetime. The APMS maximizes topology lifetime by changing routing paths according to lifetime log to manage sensor power. Furthermore, active responses for optimum topology can be achieved by deploying sensors via simulation prior to sensors deployed to the real environments by users. [ABSTRACT FROM AUTHOR] more...

We present a performance analysis of a parallel implementation for both preconditioned conjugate gradient and preconditioned bi-conjugate gradient solvers running on graphic processing units (GPUs) with CUDA programming model. The solvers were mainly optimized for the solution of sparse systems of algebraic equations at complex entries, arising from the three-dimensional edge-finite element analysis of the electromagnetic phenomena involved in the open-bound earth diffusion of currents under time-harmonic excitation. We used a shifted incomplete Cholesky (IC) factorization as preconditioner. Results show a significant speedup by using either a single-GPU or a multi-GPU device, compared to a serial central processing unit (CPU) implementation, thereby allowing the simulations of large-scale problems in low-cost personal computers. Additional experiments of the optimized solvers show that its use can be extended successfully to other complex systems of equations arising in electrical engineering, such as those obtained in power–system analysis. [ABSTRACT FROM AUTHOR] more...

The development of smart grid and the increasing scale of power system brings more and more pressure to the conventional power system simulators. The graphic processing unit which features the massive concurrent threads and excellent floating point performance brings a new chance to the area of power system simulation. This paper introduces a hierarchical parallel LU decomposition algorithm based on stratified elimination tree, and develops a graphic processing unit (GPU)-based parallel linear system solver on the algorithm. To boost the effectiveness of the solver, a hybrid improved matrix ordering algorithm is proposed which can reduce the height of the elimination tree. On the basis of the solver, this paper implements a coarse-grained task parallelism which can analyze several contingencies of static security assessment (SSA) in parallel, and develops a two-layered parallel SSA program. The performance of the proposed parallel SSA program is benchmarked on a Tesla C2050 GPU using several real world cases, whose scale is up to 21801 nodes, compared with a serial SSA program which runs on a Xeon E5620 CPU. The results of the case studies show that the introduced method’s speedup reaches 9.2 times, which can greatly accelerate SSA solution. [ABSTRACT FROM PUBLISHER] more...

Phase-field models are widely employed to simulate microstructure evolution during processes such as solidification or heat treatment. The resulting partial differential equations, often strongly coupled together, may be solved by a broad range of numerical methods, but this often results in a high computational cost, which calls for advanced numerical methods to accelerate their resolution. Here, we quantitatively test the efficiency and accuracy of semi-implicit Fourier spectral-based methods, implemented in Python programming language and parallelized on a graphics processing unit (GPU), for solving a phase-field model coupling Cahn–Hilliard and Allen–Cahn equations. We compare computational performance and accuracy with a standard explicit finite difference (FD) implementation with similar GPU parallelization on the same hardware. For a similar spatial discretization, the semi-implicit Fourier spectral (FS) solvers outperform the FD resolution as soon as the time step can be taken 5 to 6 times higher than afforded for the stability of the FD scheme. The accuracy of the FS methods also remains excellent even for coarse grids, while that of FD deteriorates significantly. Therefore, for an equivalent level of accuracy, semi-implicit FS methods severely outperform explicit FD, by up to 4 orders of magnitude, as they allow much coarser spatial and temporal discretization. [Display omitted] [ABSTRACT FROM AUTHOR] more...

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. • We parallelize the CA-based indoor PM modeling approach. • The mechanism parallelization runs the PM mechanisms parallelly instead of serially. • The GPU-based cell parallelization executes each loop of a PM mechanism on GPUs. • The parallelized CA approach is as accurate as the Eulerian drift-flux models. • The parallelized CA approach achieves an efficiency enhancement up to 22–77 times. [ABSTRACT FROM AUTHOR] more...

Following the progressive development of IT technology, on-premise IT resources have been shifted to cloud computing environments. The principle reason for this change in IT resource-composing environments is that cloud computing services allow IT resources to be used as and when necessary, which means without buying hardware equipment. For this reason, studies on diverse aspects are being conducted for better security, rapidity, availability, reliability, and elasticity of cloud computing. Among the virtualization technologies that are basic for cloud computing, desktop storage virtualization (DSV) is composed of distributed legacy desktop personal computers. In DSV environments, clustering by unavailable state time and auto-scaling for storage provision as requested by users are considered very important. In addition, deferred processing for analysis of desktop PC performance states in DSV environments to select an appropriate desktop PC is directly connected to the quality of service (QoS). Although diverse algorithms and schemes for clustering and auto-scaling have been developed to this end, they have limited performance or have been made without considering DSV environments. Consequently, large amounts of deferred processing time are required. In the present paper, an efficient auto-scaling scheme (EAS) is proposed that minimizes deferred processing time in Internet of Things (IoT) environments by using many-cores of the GPU for clustering and auto-scaling in DSV environments. The EAS provides higher QoS to storage users compared to the CPU by mapping the information of numerous distributed desktop PCs on individual threads of the GPU and processing the information in parallel. [ABSTRACT FROM AUTHOR] more...

Blade is one of the core components of turbine machinery. The reliability of blade is directly related to the normal operation of plant unit. However, with the increase of blade length and flow rate, non-linear effects such as finite deformation must be considered in strength computation to guarantee enough accuracy. Parallel computation is adopted to improve the efficiency of classical nonlinear finite element method and shorten the blade design period. So it is of extraordinary importance for engineering practice. In this paper, the dynamic partial differential equations and the finite element method forms for turbine blades under centrifugal load and flow load are given firstly. Then, according to the characteristics of turbine blade model, the classical method is optimized based on central processing unit + graphics processing unit heterogeneous parallel computation. Finally, the numerical experiment validations are performed. The computation speed of the algorithm proposed in this paper is compared with the speed of ANSYS. For the rectangle plate model with mesh number of 10 k to 4000 k, a maximum speed-up of 4.31 can be obtained. For the real blade-rim model with mesh number of 500 k, the speed-up of 4.54 times can be obtained. [ABSTRACT FROM AUTHOR] more...

Two fundamental challenges of simulating biologically relevant systems are the rapid calculation of the energy of solvation and the trajectory length of a given simulation. The Generalized Born model with a Simple sWitching function (GBSW) addresses these issues by using an efficient approximation of Poisson- Boltzmann (PB) theory to calculate each solute atom's free energy of solvation, the gradient of this potential, and the subsequent forces of solvation without the need for explicit solvent molecules. This study presents a parallel refactoring of the original GBSW algorithm and its implementation on newly available, low cost graphics chips with thousands of processing cores. Depending on the system size and nonbonded force cutoffs, the new GBSW algorithm offers speed increases of between one and two orders of magnitude over previous implementations while maintaining similar levels of accuracy. We find that much of the algorithm scales linearly with an increase of system size, which makes this water model cost effective for solvating large systems. Additionally, we utilize our GPU-accelerated GBSW model to fold the model system chignolin, and in doing so we demonstrate that these speed enhancements now make accessible folding studies of peptides and potentially small proteins. [ABSTRACT FROM AUTHOR] more...

The contemporary large scale measuring systems in the real-time environment make extensive use of histogramming as a tool for the experimental data quality monitoring. The processing of a large number of data channels requires a suitable computing power where the graphical processors seem to be well suited. Histogramming operations run on the central and graphics processing units are discussed. Results of the performance measurements including various configurations of the allocation of the histograms in various parts of the memory of used devices are presented. [ABSTRACT FROM AUTHOR] more...

The cell mapping methods were originated by Hsu in 1980s for global analysis of nonlinear dynamical systems that can have multiple steady-state responses including equilibrium states, periodic motions, and chaotic attractors. The cell mapping methods have been applied to deterministic, stochastic, and fuzzy dynamical systems. Two important extensions of the cell mapping method have been developed to improve the accuracy of the solutions obtained in the cell state space: the interpolated cell mapping (1CM) and the set-oriented method with subdivision technique. For a long time, the cell mapping methods have been applied to dynamical systems with low dimension until now. With the advent of cheap dynamic memory and massively parallel computing technologies, such as the graphical processing units (GPUs), global analysis of moderate- to highdimensional nonlinear dynamical systems becomes feasible. This paper presents a parallel cell mapping method for global analysis of nonlinear dynamical systems. The simple cell mapping (SCM) and generalized cell mapping (GCM) are implemented in a hybrid manner. The solution process starts with a coarse cell partition to obtain a covering set of the steady-state responses, followed by the subdivision technique to enhance the accuracy of the steady-state responses. When the cells are small enough, no further subdivision is necessary. We propose to treat the solutions obtained by the cell mapping method on a sufficiently fine grid as a database, which provides a basis for the 1CM to generate the pointwise approximation of the solutions without additional numerical integrations of differential equations. A modified global analysis of nonlinear systems with transient states is developed by taking advantage of parallel computing without subdivision. To validate the parallelized cell mapping techniques and to demonstrate the effectiveness of the proposed method, a low-dimensional dynamical system governed by implicit mappings is first presented, followed by the global analysis of a three-dimensional plasma model and a six-dimensional Lorenz system. For the six-dimensional example, an error analysis of the 1CM is conducted with the Hausdotff distance as a metric. [ABSTRACT FROM AUTHOR] more...

This paper presents a hybrid preconditioning technique for Conjugate Gradient method and discusses its parallel implementation on Graphic Processing Unit (GPU) for solving large sparse linear systems arising from application of interior point methods to conic optimization problems in the context of nonlinear Finite Element Limit Analysis (FELA) for computational Geomechanics. For large 3D problems, the use of direct solvers in general becomes prohibitively expensive due to exponentially growing memory requirements and computational time. Besides, the so-called saddle-point systems resulting from use of optimization framework is not an exemption. On the other hand, although preconditioned iterative methods have moderate storage requirements and therefore can be applied to much larger problems than direct methods, they usually exhibit high number of iterations to reach convergence. In present paper, we show that this problem can be effectively tackled using the proposed hybrid preconditioner along with an elaborate implementation on GPU. Furthermore, numerical results verify the robustness and efficiency of the proposed technique. [ABSTRACT FROM AUTHOR] more...