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In this paper, we consider the numerical implementation of the 2D wave equation in isotropic-heterogeneous media. The stability analysis of the scheme using the von Neumann stability method has been studied. We conducted a study on modeling the propagation of acoustic waves in a heterogeneous medium and performed numerical simulations in various heterogeneous media at different time steps. Developed parallel code using Compute Unified Device Architecture (CUDA) technology and tested on domains of various sizes. Performance analysis showed that our parallel approach showed significant speedup compared to sequential code on the Central Processing Unit (CPU). The proposed parallel visualization simulator can be an important tool for numerous wave control systems in engineering practice. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Shenzhen University Science & Engineering is the property of Editorial Department of Journal of Shenzhen University Science & Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

When performing high-resolution imaging with ground-based synthetic aperture radar (GB-SAR) systems, the data collected and processed are vast and complex, imposing higher demands on the real-time performance and processing efficiency of the imaging system. Yet a very limited number of studies have been conducted on the real-time processing method of GB-SAR monitoring data. This paper proposes a real-time imaging scheme based on parallel processing models, optimizing each step of the traditional  ω K imaging algorithm in parallel. Several parallel optimization schemes are proposed for the computationally intensive and complex interpolation part, including dynamic parallelism, the  G r o u p - N s t r e a m  processing model, and the  F t h r e a d - G r o u p - N s t r e a m  processing model. The  F t h r e a d - G r o u p - N s t r e a m  processing model utilizes  F t h r e a d ,  G r o u p , and  N s t r e a m  for the finer-grained processing of monitoring data, reducing the impact of the nested depth on the algorithm's performance in dynamic parallelism and alleviating the issue of serial execution within the  G r o u p - N s t r e a m  processing model. This scheme has been successfully applied in a synthetic aperture radar imaging system, achieving excellent imaging results and accuracy. The speedup ratio can reach 52.14, and the relative errors in amplitude and phase are close to 0, validating the effectiveness and practicality of the proposed schemes. This paper addresses the lack of research on the real-time processing of GB-SAR monitoring data, providing a reliable monitoring method for GB-SAR deformation monitoring. [ABSTRACT FROM AUTHOR]

Graphic Processing Units (GPUs) are becoming very important in the present day. Their high computational capabilities with high speed and accuracy are making them a very strong force in communication engineering. In recent times, their need has increased tremendously due to the increasing range of applications. Video surveillance is an important field where very heavy computations are needed to be done on videos to perfectly detect the motion of an object in suspicious situations. The various analyses on video can be used to extract information and process data to generate actionable intelligent conclusions. However, CPUs fail to deliver real time results when it comes to high-resolution videos from a large number of cameras simultaneously. Thankfully, there is a lot of graphic hardware available nowadays, which comprises powerful hardware processors often intended to process data in parallel and so greatly accelerates the processes being done on them. An accelerated algorithm is required for processing petabytes of data from security cameras and video surveillance satellites and that in real time. In this paper, we propose a method of using GPUs in detecting the motion of an object at different junctions in video surveillance. The results show a great gain in performance when the proposed method runs on GPUs and CPUs in terms of speed as well as accuracy. The new parallel processing approaches are developed on each phase of the algorithm to enhance the efficiency of the system. Proposed algorithm achieved an average speed up of 50.094x for lower resolution video frames (320 × 240,720 × 480,1024 × 768) and 38.012x for higher resolution video frames (1360 × 768,1920 × 1080) on GPU, which is superior to CPU processing. [ABSTRACT FROM AUTHOR]

Zero-knowledge proof (ZKP) is a cryptographic primitive that enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities, its most practical type, called zero-knowledge Succinct Non-interactive ARgument of Knowledge (zkSNARK), has been widely deployed in various privacypreserving applications such as cryptocurrencies and verifiable computation. Although state-of-the-art zkSNARKs are highly efficient for the verifier, the computational overhead for the prover is still orders of magnitude too high to warrant use in many applications. This overhead arises from several time-consuming operations, including large-scale matrix-vector multiplication (MUL), number-theoretic transform (NTT), and especially the multi-scalar multiplication (MSM) which constitutes the largest proportion. Therefore, further efficiency improvements are needed. In this paper, we focus on comprehensive optimization of running time and storage space required by the MSM algorithm on GPUs. Specifically, we propose a novel, modular and adaptive parameter configuration technique—elastic MSM to enable us to adjust the scale of MSM according to our own wishes by performing a corresponding amount of preprocessing. This technique enables us to fully unleash the potential of various efficient parallel MSM algorithms. We have implemented and tested elastic MSM over three prevailing parallel Pippenger algorithms on GPUs. Across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 1.90×, 1.08× and 1.36× (2.58×, 1.39× and 1.91×) speedup versus three state-of-the-art parallel Pippenger algorithms on GPUs, respectively. From another perspective, elastic MSM could also be regarded as a preprocessing technique over the well-known Pippenger algorithm, which is modular and could be used to accelerate almost all the most advanced parallel Pippenger algorithms on GPUs. Meanwhile, elastic MSM provides an adaptive trade-off between the running time and the extra storage space needed by parallel Pippenger algorithms on GPUs. This is the first preprocessing technique to retain the improved MSM computation brought by preprocessing under varying storage space limitations. Specifically, across various preprocessing space limitations (across various MSM scales), our constructions achieve up to about 192× and 223× (159× and 174×) speedup versus two state-ofthe- art preprocessing parallel Pippenger algorithms on GPUs, respectively.

Sparse matrix-vector multiplication (SpMV) is a crucial computing process in the field of science and engineering. CSR (compressed sparse row) format is one of the most commonly used storage formats for sparse matrix. In the process of implementing parallel SpMV on the graphics processing unit (GPU), it only stores non-zero elements of sparse matrix, avoiding computational redundancy caused by zero element filling, and saving storage space. But there is a problem of load imbalance, which wastes computing resources. To address the aforementioned issues, storage formats with good performance in recent years have been studied, and a row by row decomposition and reorganization storage format—TEB (threshold-exchangeorder block) format has been proposed. The format first uses a heuristic threshold selection algorithm to determine the appropriate segmentation threshold, and combines the row merging algorithm based on reordering to reconstruct and decompose the sparse matrix, so that the number of non-zero elements between blocks is as close as possible. Furthermore, combined with CUDA (computer unified device architecture) thread technology, a parallel SpMV algorithm between sub blocks based on TEB storage format is proposed, which can reasonably allocate computing resources and solve the problem of load imbalance, thus improving the parallel computing efficiency of SpMV. In order to verify the effectiveness of the TEB storage format, experiments are conducted on the NVIDIA Tesla V100 platform. The results show that compared to PBC (partition-block-CSR), AMF-CSR (adaptive multi-row folding of CSR), CSR-Scalar (compressed sparse row-scalar), and CSR5 (compressed sparse row 5) storage formats, TEB can improve SpMV time performance by an average of 3.23×, 5.83×, 2.33×, and 2.21×. In terms of floating-point computing performance, the average improvement can be 3.36×, 5.95×, 2.29×, and 2.13×

This study focuses on the implementation of the spectral difference (SD) method on hexahedral elements to NVIDIA graphics processing units (GPUs) using the Compute Unified Device Architecture (CUDA) for aeroacoustic problems. Three problems were addressed in the implementation of this study: thread parallelism strategy optimization within the GPU, data access patterns management, and multi-GPU parallelization implementation. Computational speed testing showed that the three factors significantly affect the efficiency of the code on the GPU. The implemented GPU solver was validated using an inviscid problem and a viscous problem. The numerical results show that the GPU solver achieves the same level of accuracy as the CPU program, with remarkable speed improvements. Specifically, compared with a single CPU core with a turbo boost frequency of 3.2 GHz (Intel Xeon Silver 4210), the inviscid case tested on an RTX 2070 Super GPU achieved acceleration of 122.4× , and the viscous case conducted on an RTX 3090 GPU achieved acceleration of 229.7×. Additionally, the GPU solver exhibits a parallel efficiency exceeding 93% when performing parallel computing on a platform with multiple RTX 3090 GPU cards. Furthermore, the GPU-accelerated computational aeroacoustics solver was applied to compute the noise from a low-speed propeller. The computed results were compared with experimental data, and the excellent agreement demonstrated the effectiveness and feasibility of the GPU implementation of the SD solver. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper we present a scalable approach based on the latency-based linear multistep compound method to use graphic processing unit (GPU) to accelerate the power system simulations with electromagnetic transients.To balance between the computational load and accuracy, we modeled the power system using shifted frequency analysis. Then, to efficiently exploit the hardware, our computational approach is designed to utilize both data-parallel and task-parallel execution. In addition, a graph-based thread safety design is introduced to achieve high scalability in the paralleled component computations. Furthermore, our implementation introduces a translation layer for different heterogeneous computing frameworks during compile time, so that a wide range of GPU devices can be supported without losing performance. Finally, benchmark results show that our approach achieves faster-than-real-time capability for systems with hundreds to thousands of nodes.

Ising machines are domain-specific computers that solve combinatorial optimization problems (COPs). They utilize an Ising model to represent a COP and search for the optimal spin configuration of the Ising model to solve the COP. Most Ising machines are based on simulated annealing (SA) and update the spin configuration according to single-spin-flip Markov Chain Monte Carlo methods. However, supporting a multi-spin flip is important to enhance the search performance of SA-based Ising machines when constrained COPs are solved. In this paper, we extend the merge method, which was introduced to provide a multi-spin-flip capability for SA-based Ising machines, and formulate it as a series of matrix multiplications that can be executed on a graphics processing unit (GPU) efficiently. We then construct a GPU-based Ising machine that implements the GPU-oriented merge method together with an extended SA algorithm. We finally demonstrate its superiority over the state-of-the-art GPU-based Ising machine for quadratic knapsack problems.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Zhejiang University: Science A is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

R-tree is an important multi-dimensional data structure widely employed in many applications for storing and querying spatial data. As GPUs emerge as powerful computing hardware platforms, a GPU-based parallel R-tree becomes the key to efficiently port R-tree-related applications to GPUs. However, traditional tree-based data structures can hardly be directly ported to GPUs, and it is also a great challenge to develop highly efficient parallel tree-based data structures on GPUs. The difficulty mostly lies in the design of tree-based data structures and related operations in the context of many-core architecture that can facilitate parallel processing. We summarize our contributions as follows: (i) design a GPU-friendly data structure to store spatial data; (ii) present two parallel R-tree construction algorithms and one parallel R-tree query algorithm that can take the hardware characteristics of GPUs into consideration; and (iii) port the vector map overlay system from CPU to GPU to demonstrate the feasibility of parallel R-tree. Experimental results show that our parallel R-tree on GPU is efficient and practical. Compared with the traditional CPU-based sequential vector map overlay system, our vector map overlay system based on parallel R-tree can achieve nearly 10-fold speedup.

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ambient noise seismology uses the ambient noise signals recorded by seismic stations to calculate the cross-correlation between stations, and thereby derive information about geological structures. In recent years, it has been widely used in fields such as Earth structure and oil and gas exploration. Seismic noise data processing often requires preprocessing calculations to reduce interference from instruments and seismic signals, which involves various signal processing calculations. As seismic stations are increasingly deployed in China, the continuous accumulation of seismic waveform files has greatly increased the time required for preprocessing calculations. To address the issue of computational time, a parallel seismic noise preprocessing algorithm has been proposed based on a graphics processing unit (GPU) heterogeneous computing platform. The parallel algorithm designed a parallel computing framework in three dimensions: stations, time, and segments. It implemented computational kernel functions for the calculation process in preprocessing and achieved adaptive processing of large batches of files through batch calculations. Experimental results show that the parallel preprocessing algorithm achieved an acceleration ratio of about 95 times, with good acceleration ratio and parallelism. [ABSTRACT FROM AUTHOR]

With the increase in computational power in the last decade and the forthcoming Exascale supercomputers, a new horizon in computational modelling and simulation is envisioned in combustion science. Considering the multiscale and multiphysics characteristics of turbulent reacting flows, combustion simulations are considered as one of the most computationally demanding applications running on cutting-edge supercomputers. Exascale computing opens new frontiers for the simulation of combustion systems as more realistic conditions can be achieved with high-fidelity methods. However, an efficient use of these computing architectures requires methodologies that can exploit all levels of parallelism. The efficient utilization of the next generation of supercomputers needs to be considered from a global perspective, that is, involving physical modelling and numerical methods with methodologies based on High-Performance Computing (HPC) and hardware architectures. This review introduces recent developments in numerical methods for large-eddy simulations (LES) and direct-numerical simulations (DNS) to simulate combustion systems, with focus on the computational performance and algorithmic capabilities. Due to the broad scope, a first section is devoted to describe the fundamentals of turbulent combustion, which is followed by a general description of state-of-the-art computational strategies for solving these problems. These applications require advanced HPC approaches to exploit modern supercomputers, which is addressed in the third section. The increasing complexity of new computing architectures, with tightly coupled CPUs and GPUs, as well as high levels of parallelism, requires new parallel models and algorithms exposing the required level of concurrency. Advances in terms of dynamic load balancing, vectorization, GPU acceleration and mesh adaptation have permitted to achieve highly-efficient combustion simulations with data-driven methods in HPC environments. Therefore, dedicated sections covering the use of high-order methods for reacting flows, integration of detailed chemistry and two-phase flows are addressed. Final remarks and directions of future work are given at the end. [ABSTRACT FROM AUTHOR]

Copyright of Rare Metals is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%. [ABSTRACT FROM AUTHOR]

A population balance method based on weighted Monte-Carlo droplets is used to investigate breakup phenomena in single droplet combustion and spray flame synthesis (SFS). Particle shell formation in conjunction with superheating of liquid components is shown to be a plausible cause for droplet breakup. The breakage rate is calculated based on temperature and particle concentration profiles inside individual droplets, whereas the breakage function was determined using shadowgraphy imaging of droplets and image analysis. This enabled the simulation of consecutive breakup events in single droplet combustion and SFS. Furthermore, in the context of population balance simulations, an adaptive grid method is introduced for the calculation of particle accumulation at the droplet surface. This method is applicable to quickly evaporating droplets where particle transport by advection is fast compared to transport by radial diffusion. The new adaptive grid method is evaluated by comparison with seven single droplet combustion experiments from literature. More than that, parallel algorithms for a computationally efficient implementation using graphics processing units (GPUs) are discussed.

This work describes a data-level parallelization strategy to accelerate the discrete wavelet transform (DWT) which was implemented and compared in two multi-threaded architectures, both with shared memory. The first considered architecture was a multi-core server and the second one was a graphics processing unit (GPU). The main goal of the research is to improve the computation times for popular DWT algorithms for representative modern GPU architectures. Comparisons were based on performance metrics (i.e., execution time, speedup, efficiency, and cost) for five decomposition levels of the DWT Daubechies db6 over random arrays of lengths 10 3 , 10 4 , 10 5 , 10 6 , 10 7 , 10 8 , and 10 9 . The execution times in our proposed GPU strategy were around 1.2 × 10 − 5 s, compared to 3501 × 10 − 5 s for the sequential implementation. On the other hand, the maximum achievable speedup and efficiency were reached by our proposed multi-core strategy for a number of assigned threads equal to 32. [ABSTRACT FROM AUTHOR]

We propose the Convolution Hierarchical Deep-learning Neural Network (C-HiDeNN) that can be tuned to have superior accuracy, higher smoothness, and faster convergence rates like higher order finite element methods (FEM) while using only linear element's degrees of freedom. This is based on our newly developed convolution interpolation theory (Lu et al. in Comput Mech, 2023) and this article focuses on the deep-learning interpretation of C-HiDeNN with graphics processing unit (GPU) programming using JAX library in Python. Instead of increasing the degrees of freedom like higher order FEM, C-HiDeNN takes advantage of neighboring elements to construct the so-called convolution patch functions. The computational overhead of C-HiDeNN is reduced by GPU programming and the total solution time is brought down to the same order as commercial FEM software running on a CPU, however, with orders of magnitude better accuracy and faster convergence rates. C-HiDeNN is locking-free regardless of element types (even with 3-node triangular elements or 4-node tetrahedral elements). C-HiDeNN is also capable of r-h-p-mesh adaptivity like its predecessor HiDeNN (Zhang et al. in Comput Mech 67:207–230, 2021) with additional "a" (dilation parameter) adaptivity that stems from the convolution patch function and "p" adaptivity with higher accuracy and with the same degrees of freedom as that of the linear finite elements. C-HiDeNN potentially has myriad future applications in multiscale analysis, additive and advanced manufacturing process simulations, and high-resolution topology optimization. Details on these applications can be found in the companion papers (Lu et al. 2023; Saha et al. in Comput Mech, 2023; Li et al. in Comput Mech, 2023) published in this special issue. [ABSTRACT FROM AUTHOR]

Mediterranean temporary ponds are a priority habitat according to the Natura 2000 network of the European Union, and complete inventories of these ecosystems are therefore needed. Their small size, short hydroperiod, or severe disturbance make these ponds undetectable by most remote sensing systems. Here we show, for the first time, that the distributed hydrologic model IBER+ detects ephemeral and even extinct wetlands by fully exploiting the available digital elevation model and resolving many microtopographic features at drainage basin scales of about 1000 km2. This paper aims to implement a methodology for siting flood-prone areas that can potentially host a temporary wetland, validating the results with historical orthophotos and existing wetlands inventories. Our model succeeds in dryland endorheic catchments of the Upper Guadalquivir Basin: it has detected 89% of the previously catalogued wetlands and found four new unknown wetlands. In addition, we have found that 24% of the detected wetlands have disappeared because of global change. Subsequently, environmental managers could use the proposed methodology to locate wetlands quickly and cheaply. Finding wetlands would help monitor their conservation and restore them if needed. [ABSTRACT FROM AUTHOR]

The detection of anomalous subsequences in a time series is required today in a wide range of computationally intensive applications such as digital industry, the Internet of Things, personal medicine, etc. One of the most attractive approaches to solving this problem is the concept of discord—the subsequence of a time series with the greatest distance to its nearest neighbor—because it requires the analyst to set only one intuitive parameter (subsequence length). The previously proposed DRAG (discord range aware gathering) algorithm for finding discords is exact, but its parallel versions are absent for any hardware architecture. The paper proposes a new approach to parallelizing this algorithm for a graphics processing unit, which is called PD3 (parallel DRAG-based discord discovery). A data preprocessing phase was added to the PD3 to compute mean values and standard deviations of all subsequences of the time series. The results are used further at the candidate selection and refinement phases to calculate the distances between the subsequences of the time series. Each algorithm phase is parallelized separately on the basis of the data parallelism concept and using vector data structures. Experiments show that the PD3 is much ahead of competing parallel algorithms in terms of the average time taken to find one discord. [ABSTRACT FROM AUTHOR]

Ray tracing has been regarded as the next generation of mainstream image rendering technology for a long time because of the authenticity of its rendering effect, and it is a hot research point in the field of computer graphics. In recent years, academics and commercials have extensively researched real-time ray tracing. To promote the research of real-time ray tracing, this paper reviews, analyses, and summaries the related literature. Firstly, the concept, algorithms, and classification of acceleration structures are introduced. Three commercial graphics processing units (GPU) supporting ray tracing are introduced and the differences between them are compared. This paper summarizes the optimization of ray tracing from six aspects, ray packet, stackless traversal, ray reorder, wide BVH, denoising techniques, and real-time ray tracing combined with the artificial?neural network, and expounds on the advantages and disadvantages of the relevant specific methods. Based on the acceleration of the algorithms, the hardware acceleration method on GPU and the dedicated architectures are summarized. Finally, this paper makes a brief summary of the content, points out the difficulties that real-time ray tracing is still challenged, and looks forward to the future development direction. It can help researchers systematically understand real-time ray tracing status and provide follow-up research ideas.

Orthorectification reflects a large amount of real and objective information, such as the characteristics of images and the geometric accuracy of maps. Conducting a large batch of orthorectification is a process with high time cost owing to the pixelwise correction each image. A common approach is to use graphics processing unit (GPU) parallel computing to accelerate orthorectification processing. However, most of the existing GPU acceleration studies have adopted experimental testing methods to determine thread parameters, which are inapplicable to different GPUs and affect the GPU acceleration performance. We put forward an adaptive calculation method for GPU thread parameters based on the performance parameters of different GPUs and by simultaneously blocking the image automatically according to the GPU memory space. We used 112 ZY-3 images to test the adaptive GPU and compare it to a general GPU. The experimental results show the following: first, for a single ZY-3 image, the GPU acceleration by the adaptive calculation method presented in this article is 43.22% higher than that by the general GPU, and the correction time is 34.41 times faster than that of the central processing unit. The result of the automatic image blocking was the same as that of the artificial blocking. Second, the experimental performance on four different GPUs indicated that all GPUs exhibited a significant speed boost. Third, for large-batch images, the GPU acceleration by the adaptive GPU was 32.6% higher than that by the general GPU, which provides an adaptive optimization strategy for large-batch image orthorectification.

To enhance spectral unmixing performance, a large number of algorithms have simultaneously investigated spatial and spectral information in hyperspectral images. However, sophisticated algorithms with high computational complexity can be very time-consuming when a large amount of data are involved in processing hyperspectral images. In this article, we first introduce a group sparse graph-regularized unmixing method with superpixel structure, to promote piecewise consistency of abundances and reduce computational burden. Segmenting the image into several nonoverlapped superpixels also enables to decompose the unmixing problem into uncoupled subproblems that can be processed in parallel. An implementation for the proposed algorithm on graphics processing units (GPUs) is then developed based on the NVIDIA compute unified device architecture (CUDA) framework. The proposed scheme achieves parallelism at both the intrasuperpixel and intersuperpixel levels, where multiple concurrent streams have been used to enable multiple kernels to execute on the device simultaneously. Simulation results with a series of experiments demonstrate advantages of the proposed algorithm. The performance of the GPU implementation also illustrates that parallel scheme largely expedites the implementation.

Histogram statistics has important applications in the fields of image enhancement and target detection. However, with the increasing size of the image and the higher real-time requirements, the processing process of the histogram statistical local enhancement algorithm is slow and cannot reach the expected satisfactory speed. In view of this deficiency, this paper realizes the parallel processing of histogram statistical image enhancement algorithm on graphics processing unit (GPU) platform, which improves the processing speed of large format digital images. Firstly, the efficiency of data access is improved by making full use of compute unified device architecture (CUDA) active thread block and active thread to process different sub-image blocks and pixels in parallel. Then, the paralle-lization of histogram statistical image enhancement algorithm on GPU platform is realized by using kernel configu-ration parameter optimization and data parallel computing technology. Finally, the efficient data transmission mode between the host and the device is adopted, which further shortens the execution time of the system on the hetero-geneous computing platform. The results show that for images with different image sizes, the processing speed of the image histogram statistical parallel algorithm is two orders of magnitude higher than that of the CPU serial algorithm. It takes 787.11 ms to process an image with an image size of 3241×3685. The processing speed of the parallel algo-rithm is increased by 261.35 times. It lays a good foundation for the realization of real-time large-scale image processing.

The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU. [ABSTRACT FROM AUTHOR]

The major obstacle for the application of discontinuous deformation analysis (DDA) in engineering problems is the high computational cost and poor efficiency. In this paper, the main algorithms of disk‐based DDA are redesigned and implemented on graphics processing unit (GPU) to improve its performance. First, a contact pair‐wise scheme is proposed to assemble the stiffness matrix on GPU. Second, a buffer strategy and a GPU version of grid‐based contact detection algorithm are developed to improve the efficiency of contact detection. Third, for solving the simultaneous equations, two iterative methods are considered along with the direct solver method. The parallel performances of proposed strategies are tested and compared with the CPU counterparts. The results show that the maximum speedup ratio is 14 for the assembly of the stiffness matrix and 215 for contact detection. The speedup ratio for solving simultaneous equations depends on several factors, and the preconditioned conjugate gradients method (pcg) is suggested. Finally, the effectiveness and performance of the proposed GPU accelerated disk‐based DDA is further demonstrated by several examples, one of which consisted of over 500,000 particles. The results show that the proposed method can achieve a satisfactory speedup ratio, and is ready for large‐scale problems. [ABSTRACT FROM AUTHOR]

Copyright of Systems Engineering & Electronics is the property of Journal of Systems Engineering & Electronics Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In end milling, tool deflection can often be a main cause of machining error. Therefore, in our previous study, a method to simulate the machining error due to elastic deformation of the tool system was proposed because simulation is an effective tool to optimize cutting conditions and machining accuracy. However, the large computational time needed for machining error prediction is unacceptable for practical use. Thus, this study proposes a fast simulation of the machining error for end milling based on our conventional method. To accelerate the simulation and reduce the computational time, the multiple analysis steps that are sequentially calculated in our conventional method are parallelly processed using a graphics processing unit because each step can be treated as an independent phenomenon. In addition, to improve the efficiency of geometric calculation, the workpiece and tool are respectively represented by a dexel model and polygon model that correspond to the swept shape of cutting edges. To verify the validity and effectiveness of the proposed method, a machining error simulation was performed based on both our conventional and proposed methods. It was confirmed that the simulation results based on both methods are in good agreement, and the proposed method can reduce the calculation time significantly.

Kernel density estimation (KDE) is a commonly used method for spatial point pattern analysis, but it is computationally demanding when analyzing large datasets. GPU-based parallel computing has been adopted to address such computational challenges. The existing GPU-parallel KDE method, however, utilizes only one GPU for parallel computing. Additionally, it assumes that the input data can be held in GPU memory all at once for computation, which is unrealistic when conducting KDE analysis over large geographic areas at high resolution. This study develops a multi-GPU-parallel and tile-based KDE algorithm to overcome these limitations. It exploits multiple GPUs to speedup complex KDE computation by distributing computation across GPUs, and approaches density estimation with a tile-based strategy to bypass the memory bottleneck. Experiment results show that the parallel KDE algorithm running on multiple GPUs achieves significant speedups over running on a single GPU, and higher speedups are achieved on KDE tasks of a larger problem size. The tile-based strategy renders it feasible to estimate high-resolution density surfaces over large areas even on GPUs with only limited memory. Multi-GPU parallel computing and tile-based density estimation, while incurring very little computational overhead, effectively enable conducting KDE for large-scale spatial point pattern analysis on geospatial big data. [ABSTRACT FROM AUTHOR]

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The current state-of-the-art for the simulation of machining process is the application of the FEM (finite element method). Nevertheless, the cost of FEM analyses for such problems is high due to their complex multi-physics nature, mesh regeneration issues, and small time-length scales. In contrast, particle methods are an alternative numerical approach well-suited for large deformations as in material's chip formation. In this work, the key features and simulation capabilities of iMFREE are presented, which is a massively parallel and robust particle-based code for machining simulations developed at the ETH Zürich. Various applications of iMFREE are shown, where the efficiency is demonstrated in orthogonal cutting simulations for the optimisation of material removal rates, a chip formation study, the inverse identifications of friction law parameters and material constitutive law parameters. Further, the applicability to single and multiple grain grinding simulations of Ti6Al4V and silicon is shown. [ABSTRACT FROM AUTHOR]

Graphic processing units (GPU) concept, combined with CUDA and OpenCL programming models, offers new opportunities to reduce latency and power consumption of throughput-oriented workloads. GPU can execute thousands of parallel threads to hide the memory access latency. However, for some memory-intensive workloads, it is very likely in some time intervals that all threads of a core will be stalled while waiting for their data to be provided by the main memory. In this research, we aim to make GPU memory access latency shorter to increase the thread activity time and to decrease core underutilization. In order to improve non-optimal time of cores, we focus on the memory buffer and the interconnection network to prioritize the packets of the cores with the greatest number of stalled threads. As a result, more critical packets will receive the higher priority in arbitration and resource allocation, so their memory requests will be handled faster, and overall cores' stall time is reduced. 28% maximum and 12.5% average speed-up improvements among the used benchmarks, without significant effect on system area and power consumption, are reported. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems and allow for data-sampling with fewer acquisitions than classical imaging techniques, even under the Nyquist rate. However, compressive hyperspectral imaging requires a reconstruction algorithm in order to recover all the data from the raw compressed acquisition. The reconstruction process is one of the limiting factors for the spread of these devices, as it is generally time-consuming and comes with a high computational burden. Algorithmic and material acceleration with embedded and parallel architectures (e.g., GPUs and FPGAs) can considerably speed up image reconstruction, making hyperspectral compressive systems suitable for real-time applications. This paper provides an in-depth analysis of the required performance in terms of computing power, data memory and bandwidth considering a compressive hyperspectral imaging system and a state-of-the-art reconstruction algorithm as an example. The results of the analysis show that real-time application is possible by combining several approaches, namely, exploitation of system matrix sparsity and bandwidth reduction by appropriately tuning data value encoding. [ABSTRACT FROM AUTHOR]

The performance of reheating furnace is highly dependent on the slab temperatures which further determine energy consumption. In general, the oxide scale layer and skid marks have a huge impact on the temperature distributions of the slab. Thus, an optimal control problem with a 3-dimensional (3D) numerical model is proposed to reduce the generation of skid marks, oxide scale layer and energy losses for the reheating furnace. Firstly, a 3D numerical Partial Difference Equation (PDE) model is built by considering the formation of the oxide scale layer and skid marks. The proposed model is implied by the parallel implementation strategy of GPU to reduce the computational time. Secondly, the 3D PDE-constrained optimal control problem is developed for the reheating furnace and solved by the proposed modified conjugate gradient (MCG) method. Finally, the accuracy of the proposed 3D model is verified by the industry measurement data, and the fast convergence performance of the MCG method is also validated. This work can significantly reduce the generation of oxide scale layer and energy consumption. [ABSTRACT FROM AUTHOR]

The airborne hyperspectral remote sensing systems (AHRSSs) acquire images with high spectral resolution, high spatial resolution, and high temporal dimension. While the AHRSS captures more detailed information from the terrain objects, the computational complexity of data processing is greatly increased. As an important application technology in the hyperspectral domain, anomaly detection (AD) processing must be real-time and high-precision in many cases, such as post-disaster rescue, military battlefield search, and natural disaster detection. In this paper, the real-time AD technology for the push-broom AHRSS is studied, the mathematical model is established, and a novel implementation framework is proposed. Firstly, the optimized kernel minimum noise fraction (OP-KMNF) transformation is employed to extract informative and discriminative features between the background and anomalies. Secondly, the Nyström method is introduced to reduce the computational complexity of OP-KMNF transformation by decomposing and extrapolating the sub-kernel matrix to estimate the eigenvector of the entire kernel matrix. Thirdly, the extracted features are transferred to hard disks for data storage. Then, taking the extracted features as input data, the background separation model-based CEM anomaly detector (BSM-CEMAD) is imported to detect anomalies. Finally, graphics processing unit (GPU) parallel computing is utilized in the Nyström-based OP-KMNF (NOP-KMNF) transformation and the BSM-CEMAD to improve the execution efficiency, and the real-time AD for the push-broom AHRSS could be realized. To test the feasibility of the implementation framework proposed in this paper, the experiment is carried out with the Airborne Multi-Modular Imaging Spectrometer (AMMIS) developed by the Shanghai Institute of Technical Physics as the data acquisition platform. The experimental results show that the proposed method outperforms many other state-of-the-art AD methods in anomalies detection and background suppression. Moreover, under the condition that the downlink data could retain most of the hyperspectral data information, the proposed method achieves real-time detection of pixel-level anomalies, with the initial delay not exceeding 1 s, the false alarm rate (FAR) less than 5%, and the true positive rate (TPR) close to 98%.

Sequence alignment remains a fundamental problem with practical applications ranging from pattern recognition to computational biology. Traditional algorithms based on dynamic programming are hard to parallelize, require significant amounts of memory, and fail to scale for large inputs. This work presents eWFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute the exact edit-distance sequence alignment based on the wavefront alignment algorithm (WFA). This approach exploits the similarities between the input sequences to accelerate the alignment process while requiring less memory than other algorithms. Our implementation takes full advantage of the massive parallel capabilities of modern GPUs to accelerate the alignment process. In addition, we propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast shared memory of a GPU. Our results show that our GPU implementation outperforms by 3- $9\times $ the baseline edit-distance WFA implementation running on a 20 core machine. As a result, eWFA-GPU is up to 265 times faster than state-of-the-art CPU implementation, and up to 56 times faster than state-of-the-art GPU implementations.