A Novel Method for Fast Kernel Minimum Noise Fraction Transformation in Hyperspectral Image Dimensionality Reduction

Accelerating population balance-Monte Carlo simulation for coagulation dynamics from the Markov jump model, stochastic algorithm and GPU parallel computing

Computing speed is a significant issue of large-scale flood simulations for real-time response to disaster prevention and mitigation. Even today, most of the large-scale flood simulations are generally run on supercomputers due to the massive amounts of data and computations necessary. In this work, a two-dimensional shallow water model based on an unstructured Godunov-type finite volume scheme was proposed for flood simulation. To realize a fast simulation of large-scale floods on a personal computer, a Graphics Processing Unit (GPU)-based, high-performance computing method using the OpenACC application was adopted to parallelize the shallow water model. An unstructured data management method was presented to control the data transportation between the GPU and CPU (Central Processing Unit) with minimum overhead, and then both computation and data were offloaded from the CPU to the GPU, which exploited the computational capability of the GPU as much as possible. The parallel model was validated using various benchmarks and real-world case studies. The results demonstrate that speed-ups of up to one order of magnitude can be achieved in comparison with the serial model. The proposed parallel model provides a fast and reliable tool with which to quickly assess flood hazards in large-scale areas and, thus, has a bright application prospect for dynamic inundation risk identification and disaster assessment.

For a phase-sensitive optical time-domain reflectometer (ϕ-OTDR) distributed sensor system, space-frequency analysis can reduce the false alarm by analyzing the frequency distribution compared with the traditional difference value method. We propose a graphics processing unit (GPU)-based parallel computing method to perform multichannel fast Fourier transform (FFT) and realize the real-time space-frequency analysis. The experiment results show that the time taken by the multichannel FFT decreased considerably based on this GPU parallel computing. The method can be completed with a sensing fiber up to 16 km long and an entry-level GPU. Meanwhile, the GPU can reduce the computing load of the central processing unit from 70% down to less than 20%. We carried out an experiment on a two-point space-frequency analysis, and the results clearly and simultaneously show the vibration point locations and frequency components. The sensor system outputs the real-time space-frequency spectra continuously with a spatial resolution of 16.3 m and frequency resolution of 2.25 Hz.

Aiming at the problems of over stacking, warping deformation and rapid adjustment of layer thickness in electron beam additive manufacturing, the 3D printing slicing algorithm and partition scanning strategy for numerical control systems are studied. The GPU (graphics processing unit) is used to slice the 3D model, and the STL (stereolithography) file is calculated in parallel according to the normal vector and the vertex coordinates. The voxel information of the specified layer is dynamically obtained by adjusting the projection matrix to the slice height. The MS (marching squares) algorithm is used to extract the coordinate sequence of the binary image, and the ordered contour coordinates are output. In order to avoid shaking of the electron gun when the numerical control system is forming the microsegment straight line, and reduce metal overcrowding in the continuous curve C0, the NURBS (non-uniform rational b-splines) basis function is used to perform curve interpolation on the contour data. Aiming at the deformation problem of large block components in the forming process, a hexagonal partition and parallel line variable angle scanning technology is adopted, and an effective temperature and deformation control strategy is formed according to the European-distance planning scan order of each partition. The results show that the NURBS segmentation fits closer to the original polysurface cut line, and the error is reduced by 34.2% compared with the STL file slice data. As the number of triangular patches increases, the algorithm exhibits higher efficiency, STL files with 1,483,132 facets can be cut into 4488 layers in 89 s. The slicing algorithm involved in this research can be used as a general data processing algorithm for additive manufacturing technology to reduce the waiting time of the contour extraction process. Combined with the partition strategy, it can provide new ideas for the dynamic adjustment of layer thickness and deformation control in the forming process of large parts.

Hyperspectral image (HSI) classification is an essential means of the analysis of remotely sensed images. Remote sensing of natural resources, astronomy, medicine, agriculture, food health, and many other applications are examples of possible applications of this technique. Since hyperspectral images contain redundant measurements, it is crucial to identify a subset of efficient features for modeling the classes. Kernel-based methods are widely used in this field. In this paper, we introduce a new kernel-based method that defines Hyperplane more optimally than previous methods. The presence of noise data in many kernel-based HSI classification methods causes changes in boundary samples and, as a result, incorrect class hyperplane training. We propose the optimized kernel non-parametric weighted feature extraction for hyperspectral image classification. KNWFE is a kernel-based feature extraction method, which has promising results in classifying remotely-sensed image data. However, it does not take the closeness or distance of the data to the target classes. Solving the problem, we propose optimized KNWFE, which results in better classification performance. Our extensive experiments show that the proposed method improves the accuracy of HSI classification and is superior to the state-of-the-art HIS classifiers.

Polar Fourier analysis can extract orthogonal and rotation invariant features and demonstrate superior performances in many image processing tasks. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this research presents GPU accelerated polar Fourier analysis. Proposed parallel algorithm is based on mathematical properties of polar Fourier analysis and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result proposed method is over 170 times faster. Wide range of applications that using polar Fourier analysis will inspired from this work.

The real-time capability of multi-camera visual simultaneous localization and mapping (SLAM) in embedded systems is vital for robotic autonomous navigation. However, owing to the incredibly time-consuming feature extraction, multi-camera visual SLAM has high computational complexity and is difficult to run in real-time in embedded systems. This study proposes a central processing unit and graphics processing unit (CPU–GPU) combination acceleration strategy for multi-camera visual SLAM to solve the computational complexity problem, improve computational efficiency, and realize real-time running in embedded systems. First, the GPU-based feature extraction acceleration algorithm is introduced for multi-camera visual SLAM to accelerate the time-consuming feature extraction by using compute unified device architecture to parallelize feature extraction algorithm. Then, a CPU-based multi-threading pipelining method that conducts image reading, feature extraction, and tracking concurrently is proposed to improve the computational efficiency of multi-camera visual SLAM by solving the load imbalance problem caused by GPU use and improving the use of computing resources. Extensive experiment results demonstrate that the improved multi-camera visual SLAM has a speed of 15 frames per second in embedded systems and meets the real-time requirement. Moreover, the improved multi-camera visual SLAM is three times faster than the original CPU-based method. Our open-source code can be found online: https://github.com/CASHIPS-ComputerVision.

Angular radial transform (ART) is a shape descriptor in MPEG-7 for image representation and image analysis. For real world applications, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU) in image processing tasks, this paper presents GPU based angular radial transform (GART). Proposed parallel GART is based on mathematical properties of ART and optimization techniques of GPU. Optimal parameter selections for GPU execution are also evaluated. In our experiments, with same computation result GART is over 1800 times faster than ART. Wide range of applications that using ART will inspired from this work.

This paper presents a real-world pricing kernel for financial derivatives and evaluates the language and compiler tool chain that would allow expressive, hardware-neutral algorithm implementation and efficient execution on graphics-processing units (GPU). The language issues refer to preserving algorithmic invariants, e.g., inherent parallelism made explicit by map-reduce-scan functional combinators. Efficient execution is achieved by manually; applying a series of generally-applicable compiler transformations that allows the generated-OpenCL code to yield speedups as high as 70x and 540x on a commodity mobile and desktop GPU, respectively.Apart from the concrete speed-ups attained, our contributions are twofold: First, from a language perspective;, we illustrate that even state-of-the-art auto-parallelization techniques are incapable of discovering all the requisite data parallelism when rendering the functional code in Fortran-style imperative array processing form. Second, from a performance perspective;, we study which compiler transformations are necessary to map the high-level functional code to hand-optimized OpenCL code for GPU execution. We discover a rich optimization space with nontrivial trade-offs and cost models. Memory reuse in map-reduce patterns, strength reduction, branch divergence optimization, and memory access coalescing, exhibit significant impact individually. When combined, they enable essentially full utilization of all GPU cores.Functional programming has played a crucial double role in our case study: Capturing the naturally data-parallel structure of the pricing algorithm in a transparent, reusable and entirely hardware-independent fashion; and supporting the correctness of the subsequent compiler transformations to a hardware-oriented target language by a rich class of universally valid equational properties. Given the observed difficulty of automatically parallelizing imperative sequential code and the inherent labor of porting hardware-oriented and -optimized programs, our case study suggests that functional programming technology can facilitate high-level; expression of leading-edge performant portable; high-performance systems for massively parallel hardware architectures.

The fuzzy vault algorithm is mainly used for data privacy protection in fault-tolerant environments, It binds biometric features and secret data together safely to form a vault, which can realize "fuzzy" unlocking. in the real-life, fuzzy vault is mainly used in biometric authentication, image capture, recognition, etc. However, At present, most fuzzy vault schemes have the problems of high computational complexity and low communication efficiency. To solve the problems of high computational complexity and low communication efficiency in identity authentication, we based on the fuzzy vault scheme and construct a multi-secret sharing fuzzy vault scheme. which splits the big secret value into multiple sub-secret values and uses the RS code multi-secret sharing decoding method to improve the efficiency of biological identity authentication. Firstly, splits the big secret value into multiple sub-secret values. Then, We construct a multi-secret sharing scheme. our scheme supports the sharing of multi-secret information. Finally, we analyze the computational complexity and communication complexity of the scheme. The analysis shows that our scheme can reduce the computational complexity and communication complexity by an order of magnitude.

The three-dimensional numerical parametrical study for movement of spherical shell in annular boundary with the effect of particle medium shear flow is researched based on GPU (Graphic Processing Unit) parallel computing. The method of three-dimensional periodic boundary is presented and reliability demonstration for results of numerical simulation is carried out through a quarter of annular periodic boundary. With the object-oriented programming approach, CUDA is applied to improve calculation efficiency. The results show that the algorithm of three-dimensional periodical boundary is feasible and effectually to enhance computational efficiency. With specific parameters, spherical shell can emerge on surface of particle medium due to shear flow.

With the rising number of application accelerators, developers are looking for ways to evaluate new and competing platforms quickly, fairly, and early in the development cycle. As high-performance computing (HPC) applications increase their demands on application acceleration platforms, graphics processing units (GPUs) provide a potential solution for many developers looking for increased performance. Device performance metrics, such as Computational Density (CD), provide a useful but limited starting point for device comparison. The authors developed the Realizable Utilization (RU) metric and methodology to quantify the discrepancy between theoretical device performance shown by CD and the performance developers can achieve. As the RU score increases, the application is achieving a larger percentage of the computational power the device can provide. The authors survey technical publications about GPUs and use this data to analyze the RU scores for several arithmetic application kernels that are frequently accelerated in GPUs. The RU concepts presented in this paper are a first step towards a formalized comparison framework for diverse devices such as CPUs, FPGAs, GPUs and other novel architectures. GPU kernels for matrix multiplication, matrix decomposition, and N-body simulations show RU scores ranging from almost 0% to approaching 99% depending on the application, but all kernel areas show a significant decrease in RU as the computational capacities increase. Additionally, the RU scores show the higher realized performance of the GeForce 8 Series GPUs versus newer GPU architectures. This paper shows that applications running on GPUs with higher computational density report significantly lower RU scores than more mature GPUs with lower computational density. This trend implies that while the raw performance available is still increasing with newer GPUs, the achieved performance is not keeping pace with the theoretical capacities of the devices.

In this paper, we present an efficient parallel implementation of Mel-frequency Cepstral Coefficient (MFCC)-based feature extraction and describe the optimizations required for effective throughput on Graphics Processing Units (GPU) processors. We demonstrate that the feature extraction process in automatic speech recognition is well suited for GPUs and a substantial reduction in computation time can be obtained by performing feature extraction on these platforms. Using a single Nvidia GTX580 GPU our proposed approach is shown to be approximately 90x faster than a sequential CPU implementation, enabling feature extraction to be performed at under 0.01% real-time. This is significantly faster than prior reported results implemented on GPUs, DSPs and FPGAs. Furthermore we demonstrate that multiple MFCC features can be generated for a set of predefined Vocal-Tract-Length-Normalization (VTLN) alpha parameters with little degradation in throughput. Using the approach described in this paper MFCC features were extracted in 0.05% and 0.09% realtime, for 11 and 21 VTLN parameters respectively. (4 pages)

Magnetic resonance imaging simulation with spin-lock preparations to detect tiny oscillatory magnetic fields

Nowadays, Graphics Processing Units (GPUs) dominate in a wide spectrum of computing realms with massive parallel processing capabilities. However, as resources are continuously integrated into GPUs, traditional serial execution often leads to underutilization. Prior studies have shown that allowing multiple kernels to run concurrently and share GPU resources can effectively improve both resource utilization and system throughput. Nonetheless, existing methods for solely automating concurrent kernel execution either schedule kernels within individual applications (i.e., kernel-level) or enable task concurrency across multiple applications (i.e., task-level), thus leaving substantial GPU capacity underexploited. Moreover, they inevitably introduce new programming frameworks, which incur cumbersome manual efforts and further impose substantial programming burdens on developers. To address these limitations, we propose HuntKTm , a hybrid scheduling and automatic management method that cooperates kernel-level and task-level concurrency to enhance system throughput with minimal code modification. Specifically, HuntKTm comprises a stream scheduler to assign kernels, a task scheduler to dispatch tasks onto GPUs, and a memory manager to reduce memory footprint. The stream scheduler applies a strategy to dispatch kernels to hardware queues and adopts a novel algorithm to remove redundant synchronizations in computational flow. Then, the task scheduler automatically issues tasks based on resource requirements and availability to support GPU sharing among uncooperative applications. Finally, the memory manager reduces memory footprint for tasks by limiting the lifetimes of memory objects, which thereby enables more tasks to execute simultaneously. Experimental results demonstrate that HuntKTm improves system throughput by 33.2% over the existing state-of-the-art CASE framework on a single machine equipped with four NVIDIA A100 GPUs and reaches 13.8% higher application-level performance over Taskflow, even with lessened programming efforts.