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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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

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...

The quest continues for microelectronic implementations with higher throughput and reduced power consumption, particularly for digital signal processing, graphic processing unit and CPU portable applications. This Letter focuses on the digital multiplier circuit, which is a key component in determining the power‐delay‐product for numerous battery powered applications. A proposed radix‐4 8 × 8 Booth multiplier is implemented using four stages with a unique optimised stage‐1 architecture. Instead of using adder/subtractor in stage‐1, it is replaced with a novel binary‐to‐2's complement converter and a 2:1 MUX to reduce the delay and power consumption by 7.08 and 49.46%, respectively, compared to the other stages. The proposed design is implemented using CMOS 90 nm technology with 1.2 V supply to demonstrate performance. [ABSTRACT FROM AUTHOR] more...

To display massive data accurately and in real time, a visualisation algorithm based on the viewpoint motion model is proposed. A Hilbert‐R tree is constructed to index massive data. Further, the massive data is divided into a visible area, a potential area, and an unloading area using the viewpoint motion model. According to the index, corresponding data blocks are loaded into the memory, and data blocks of the visible area further transferred into the graphic processing unit (GPU) memory for rendering. When the viewpoint is changed, the data block of the potential area in the memory is directly loaded into the GPU. Experimental results show that under the premise of ensuring the quality of rendering, massive data can be displayed interactively, and the speed of displaying increases by 36–78%. [ABSTRACT FROM AUTHOR] more...

In this paper, the lattice Boltzmann method (LBM) is extended to study the filtering and contour detection of natural images, and a new lattice Boltzmann model is proposed for more complicated image processing model, like the Ambrosio and Tortorelli (A-T) model that contains two coupled nonlinear partial differential equations. The numerical results of image filtering and contour detection show that the noises in the image can be removed greatly, and simultaneously, important contours of the image are protected well. To improve the computational efficiency, we implement the developed lattice Boltzmann model on Graphic Processing Unit (GPU), and find that, compared to the CPU based algorithm, the GPU based LBM can gain more than 25 × speedup, which is very important in the further lattice Boltzmann study of large-scale image processing problems. And finally, these numerical results also show that the LBM is a feasible and efficient approach for filtering and contour detection of the natural images. [ABSTRACT FROM AUTHOR] more...

Background The extent of data in a typical genome-wide association study (GWAS) poses considerable computational challenges to software tools for gene-gene interaction discovery. Exhaustive evaluation of all interactions among hundreds of thousands to millions of single nucleotide polymorphisms (SNPs) may require weeks or even months of computation. Massively parallel hardware within a modern Graphic Processing Unit (GPU) and Many Integrated Core (MIC) coprocessors can shorten the run time considerably. While the utility of GPU-based implementations in bioinformatics has been well studied, MIC architecture has been introduced only recently and may provide a number of comparative advantages that have yet to be explored and tested. Results We have developed a heterogeneous, GPU and Intel MIC-accelerated software module for SNP-SNP interaction discovery to replace the previously single-threaded computational core in the interactive web-based data exploration program SNPsyn. We report on differences between these two modern massively parallel architectures and their software environments. Their utility resulted in an order of magnitude shorter execution times when compared to the single-threaded CPU implementation. GPU implementation on a single Nvidia Tesla K20 runs twice as fast as that for the MIC architecture-based Xeon Phi P5110 coprocessor, but also requires considerably more programming effort. Conclusions General purpose GPUs are a mature platform with large amounts of computing power capable of tackling inherently parallel problems, but can prove demanding for the programmer. On the other hand the new MIC architecture, albeit lacking in performance reduces the programming effort and makes it up with a more general architecture suitable for a wider range of problems. [ABSTRACT FROM AUTHOR] more...

Abstract: Membrane computing is a theoretical model of computation that inspired from the structure and functioning of cells. Membrane computing models naturally have parallel structure. Most of the simulations of membrane computing have been done in a serial way on a machine with a central processing unit (CPU). This has neglected the advantage of parallelism in membrane computing. This paper uses Graphic Processing Unit (GPU) as a parallel tool to implement membrane computing. The method minimizes data transferring which is time consuming procedure between the device and the host by processing all computing on GPU and transfer only the final results to CPU. Simulations show that speed increases up to 15 times compared to sequential simulation and using of shared memory increases speed, up to 38 times. [Copyright &y& Elsevier] more...

An efficient and highly scalable bond-order potential code has been developed for the molecular dynamics simulation of bulk silicon, reaching 1.87 Pflops (floating point operations per second) in single precision on 7168 graphic processing units (GPUs) of the Tianhe-1A system. Furthermore, by coupling GPUs and central processing units, we also simulated surface reconstruction of crystalline silicon at the sub-millimeter scale with more than 110 billion atoms, reaching 1.17 Pflops in single precision plus 92.1 Tflops in double precision on the entire Tianhe-1A system. Such simulations can provide unprecedented insight into a variety of microscopic behaviors or structures, such as doping, defects, grain boundaries, and surface reactions. [ABSTRACT FROM PUBLISHER] more...

Many-core processors, such as graphic processing units (GPUs), are promising platforms for intrinsic parallel algorithms such as the lattice Boltzmann method (LBM). Although tremendous speedup has been obtained on a single GPU compared with mainstream CPUs, the performance of the LBM for multiple GPUs has not been studied extensively and systematically. In this article, we carry out LBM simulation on a GPU cluster with many nodes, each having multiple Fermi GPUs. Asynchronous execution with CUDA stream functions, OpenMP and non-blocking MPI communication are incorporated to improve efficiency. The algorithm is tested for two-dimensional Couette flow and the results are in good agreement with the analytical solution. For both the one- and two-dimensional decomposition of space, the algorithm performs well as most of the communication time is hidden. Direct numerical simulation of a two-dimensional gas-solid suspension containing more than one million solid particles and one billion gas lattice cells demonstrates the potential of this algorithm in large-scale engineering applications. The algorithm can be directly extended to the three-dimensional decomposition of space and other modeling methods including explicit grid-based methods. [ABSTRACT FROM AUTHOR] more...

A non‐algorithmic physical random bit generator is introduced, using a graphic processing unit (GPU) as a random source. The generator takes randomness from parallel threads executing atomic instructions. The generator in terms of pass rates from the National Institute of Standards and Technology (NIST)'s statistical test suite and bit generation throughput is evaluated. The generator compares favourably with pseudorandom bit generators known to have fine statistical properties. The test bed Nvidia's GPU GTX 680 can generate random bits at the rate of hundreds of megabits per second. The cost‐effectiveness of GPUs as random key generators can be increased, as GPUs are now readily available in most desktop computers, and even in smart phones. [ABSTRACT FROM AUTHOR] more...