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Research on Generation Algorithm of parallel Particle Swarm Algorithm Combination Test Cases 7

Research on Generation Algorithm of Parallel Particle Swarm ...

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7th international conference on Intelligent Computing and Signal processing, ICSP 2022

作者： He, Dandan Dalian University of Science and Technology Dalian116026 China

ISBN: (纸本)9781665478571

At this stage, software testing is the core means to ensure software quality and improve software reliability. At the same time, improving and generating the degree of automation corresponding to test cases is also the key to improving the automation level of software testing. In order to further improve the automation of generating test cases, it is necessary to reasonably optimize the inherent algorithm of the basic particle swarm. the inherent particle swarm algorithm takes more time to generate combined test cases. In response to this situation, a new parallel particle swarm algorithm is proposed to generate a pairwise combination test case method, and the big data platform spark is used as the basis to group all the use cases that need to be covered in the form of pairwise combination processing. the final result is sent to the corresponding cluster node for continued optimization. the one-test-at-a-time strategy is combined with the adaptive particle swarm algorithm to find the optimal solution. After the optimization operation of all nodes is completed, spark can be used to collect all the results produced. At this time, the collected and processed results can be simplified and processed through the use case set. the algorithm proposed in this paper mainly improves the automatic test case generation algorithm, so that the iterative prompting and average running time required for automatic stress generation are better than the traditional algorithm to achieve the purpose of improving the degree of automatic test generation. © 2022 IEEE.

关键词： Big data

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Domain Decomposition Preconditioners for Unstructured Network Problems in parallel Vector architectures 21

Domain Decomposition Preconditioners for Unstructured Networ...

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50th international conference on parallel processing (ICPP)

作者： Maldonado, Daniel Adrian Schanen, Michel Pacaud, Francois Anitescu, Mihai Argonne Natl Lab Lemont IL 60439 USA

ISBN: (纸本)9781450384414

In this paper we present our experience implementing domain decomposition preconditioners on vector architectures. In particular, we will focus on the solution of unstructured network equations arising from electrical power systems by preconditioning iterative algorithms with the Additive Schwarz Method (ASM). the implementation will be carried out using the Julia programming language, which allows for easy prototyping and interfacing with GPU architectures thanks to its multiple dispatch features. In our experiments, we will show the trade-off between device throughput and convergence of the iterative algorithm as the size of the domain varies, and determine optimal fronts of computational performance.

关键词： Iterative methods

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A Deep Learning Method for Ship-Radiated Noise Recognition Based on MFCC Feature 7

A Deep Learning Method for Ship-Radiated Noise Recognition B...

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7th IEEE international conference on Transportation Information and Safety, ICTIS 2023

作者： Hu, Fucai Fan, Jinyang Kong, Yiyang Zhang, Linke Guan, Xiaoxi Yu, Yongsheng Wuhan University of Technology School of Naval Architecture Ocean and Energy Power Engineering Wuhan China Transportation College of Jilin University Jilin University Changchun China Wuhan University of Technology State Key Laboratory of Silicate Materials for Architectures Wuhan China

ISBN: (纸本)9798350308532

Underwater target recognition is one of the most challenging tasks in underwater signal processing. Previous deep learning methods have relied on fusing more acoustic features, ignoring the rich information contained in the time-frequency features of underwater acoustics. Furthermore, fusing features that are less relevant to the target may result in redundancy and affect the recognition performance of the model. In this paper, a novel method based on Multi-Scale feature extraction, Attention mechanism for feature fusion and Convolutional Recurrent Neural Network (MACRN) is proposed to fully exploit the deep features of Mel-Frequency Cepstral Coefficients (MFCC) for underwater target recognition. In comparative experiments conducted on the open-source dataset ShipsEar, the proposed method achieves an average recognition accuracy of 98.1% © 2023 IEEE.

关键词： Underwater acoustics

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An Efficient LRnLA Algorithm and Data Structure for Manycore and Multicore Computers with Hierarchical Cache 17th

An Efficient LRnLA Algorithm and Data Structure for Manyco...

引用

17th international Scientific conference on parallel Computational Technologies, PCT 2023

作者： Levchenko, Vadim Perepelkina, Anastasia Keldysh Institute of Applied Mathematics Moscow Russia

ISBN: (纸本)9783031388637

According to the Roofline model, low arithmetic intensity is the main performance bottleneck for multidimensional simulation with stencil data access. this bottleneck can be eliminated using LRnLA (locally recursive nonlocally asynchronous) algorithms, which take advantage of all levels of cache. We introduce the new algorithm FArShFold, a development of previously published Torre-type LRnLA algorithms by using data arrays aligned with a space-time wavefront. the novelty is in the distribution of data between threads and the decomposition in the time axis for parallelism. In this paper, we present the implementation of the algorithm for the solution of fluid dynamics problems with the LBM method and its scaling on AMD zen2/3 processor architectures. the obtained performance for single precision D3Q19 LBM is more than 1.4 GLUps on the AMD Ryzen R9 5950X processor. © 2023, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Graphics processing unit

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Impact of AVX-512 Instructions on Graph Partitioning Problems 21

Impact of AVX-512 Instructions on Graph Partitioning Problem...

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50th international conference on parallel processing (ICPP)

作者： Hossain, Md Maruf Saule, Erik Univ North Carolina Charlotte Charlotte NC 28223 USA

ISBN: (纸本)9781450384414

Graph analysis now percolates society with applications ranging from advertising and transportation to medical research. the structure of graphs is becoming more complex every day while they are getting larger. the increasing size of graph networks has made many of the classical algorithms reasonably slow. Fortunately, CPU architectures have evolved to adjust to new and more complex problems in terms of core-level parallelism and vector-level parallelism (SIMD-level). In this paper, we are exploring how the modern vector architecture of CPUs can help with community detection, partitioning, and coloring kernels by studying two representatives algorithms. We consider the Intel SkylakeX and Cascade Lake architectures, which support gather and scatter instructions on 512-bit vectors. the existing vectorized graph algorithms of classic graph problems, such as BFS and PageRank, do not apply well to community detection;we show the support of gather and scatter are necessary. In particular for the implementation of the reduce-scatter patterns. We evaluate the performances achieved on the two architectures and conclude that good hardware support for scatter instructions is necessary to fully leverage the vector processing for graph partitioning problems.

关键词： Graph Coloring Community Detection Vector processing AVX-512 Cascade Lake SkylakeX reduce-scatter

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A Mini-model can Make Machine Unlearning Better 24th

A Mini-model can Make Machine Unlearning Better

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Zhao, Mingkang Yu, Weng Zhu, Congcong China University of Geosciences Wuhan China Ministry of Education Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance School of Information Engineering Beijing China Hainan International College of Minzu University of China Li’an International Education Innovation pilot Zone Hainan572499 China City University of Macau 999078 China

ISBN: (纸本)9789819615247

In recent years, with the development of machine learning, plenty of personal data have been utilized in the training process of the models which incurs severe privacy leakage in the field. Current regulations mandate the removal of private user information from both databases and machine learning models upon specific deletion requests. While wiping data records from memory storage is a straightforward task, eliminating the influence of specific data samples from an already-trained model is challenging. Numerous studies in machine unlearning focus on mitigating the impact of target data by adjusting model parameters in deep learning. However, existing methods for data removal in deep learning often fall short of meeting real-world practicality. the intricacies of deep neural networks make unlearning in deep learning a less expedient process than retraining from scratch. For example, some methods require a ton of matrix calculations or even more complicated computations, and some algorithms need a lot of space, which ends up hindering their effectiveness in real-world applications. Accordingly, we’ve come up with a more practical solution. Our method just needs a small part of the validation dataset to pre-train a mini-model to update the parameters of the target model quickly. With this assistant model, we can quickly and accurately unlearn target classes and items. We prove that our proposed method is highly effective in unlearning tasks with not very large amounts of data. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information leakage

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DRStencil: Exploiting Data Reuse within Low-order Stencil on GPU 23

DRStencil: Exploiting Data Reuse within Low-order Stencil on...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and Systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： You, Xin Yang, Hailong Jiang, Zhonghui Luan, Zhongzhi Qian, Depei State Key Laboratory of Software Development Environment Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

ISBN: (纸本)9781665494571

Stencil computation is widely adopted in scientific applications as one of the most significant computation patterns. Although there are various optimizations proposed to accelerate the stencil computation, the low-order stencil still suffers from limited performance on GPU due to its low computation inten-sity. In this paper, we propose the fusion-partition optimization techniques to accelerate the low-order stencil computation and implement an effective code generation framework DRStencil to automatically generate optimized stencil codes with fusion-partition applied. Specifically, we adopt a four-stage optimization workflow such as time-fusion, partition, forward and backward computation. We also propose an auto-tuning method to deter-mine the optimal parameter settings of the generated stencil codes. We evaluate DRStencil with representative low-order stencils on Nvidia P100, V100, and A100 GPUs. Our evaluation results achieve 1.46 x, 1.59 x, and 1.10 x speedup on average for widely used low-order stencils compared to the state-of-the-art implementations on P100, V100, and A100 GPUs, respectively. © 2021 IEEE.

关键词： Codes Smart cities Graphics processing units Manuals parallel processing Computational efficiency Optimization

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Crop Classification Methods Based on Siamese CBMM-CNN architectures Using Hyperspectral Remote Sensing Data 24th

Crop Classification Methods Based on Siamese CBMM-CNN Archi...

引用

24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Xie, Bin Zhang, Jiahao Li, Yuling Li, Yusong Dong, Xinyu College of Computer and Cyber Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics and Data Security Hebei Normal University Hebei Shijiazhuang China

ISBN: (纸本)9789819615278

Amidst global population growth and escalating food demands, real-time agricultural monitoring is crucial for ensuring food security. During the initial stages of crop growth, however, it faces significant challenges in obtaining accurate ground truth data via remote sensing, thereby impeding effective crop classification. Traditional Convolutional Neural Network (CNN) models, although effective in image processing and feature extraction within computer vision, are notably limited in their ability to handle the high dimensionality and complex spectral information inherent in hyperspectral non-image remote sensing data. To address this problem, we designed an enhanced CNN model, CBM-CNN. the CBM-CNN incorporates additional convolutional layers (C), batch normalization (B), and MLP(M) to convert one-dimensional data into two-dimensional data for classification, thus improving the processing efficiency of highly nonlinear data features. Building on CBM-CNN, we add multiple attention mechanisms (CBMM-CNN) to enhance feature extraction capabilities. the Siamese CBMM-CNN (SCBMM-CNN) is employed for in-depth learning of data features, with supervised learning precisely adjusting the model to improve classification performance. Experimental results on hyperspectral remote sensing datasets demonstrate that SCBMM-CNN outperforms traditional machine learning models (PCA-RF, GBDT, GMO-SVM, Random Forest, Decision Tree) and deep learning models (CNN, CNN-LSTM) in terms of accuracy, recall rate, F1 score, and kappa coefficient. these results confirm that SCBMM-CNN significantly improves crop classification accuracy and provides a novel method for early crop growth classification. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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Acceleration of image processing algorithms based on a Single Board Computer and FPGA co-design 11

Acceleration of image processing algorithms based on a Singl...

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11th international conference on Modern Circuits and Systems Technologies (MOCAST)

作者： Kokotis, Petros Vourvoulakis, John Int Hellen Univ Dept Comp Informat & Telecommun Engn Serres Greece

ISBN: (数字)9781665467179

ISBN: (纸本)9781665467179

During recent years, various hardware platforms were developed, each one suitable for use in different kind of applications. Platforms based on FPGAs, DSPs, GPUs, Single Board Computers, microcontrollers extend processing capabilities and functionality in comparison with traditional personal computers based on a single CPU. Furthermore, co-design combines advantages from different types of processing units, rendering such architectures more attractive to researchers. In this paper, we achieve acceleration of image processing algorithms using a hardware platform based on a Raspberry Pi Single Board Computer and a custom designed FPGA HAT (Hardware Attached on Top) for RPi. the FPGA HAT consists of a Cyclone 10LP device the FPGA undertakes a computationally demanding load such as robotic vision algorithms exploiting parallelism, while the RPi can apply higher level operations such as running ROS (Robot Operating System). In order to overcome bottleneck in exchanging data between RPi and FPGA, a 16-bit parallel customized protocol was developed from scratch. the achieved transfer rate was about 50 Mbytes/sec when multi threaded software was implemented for the RPi. An image edge detector was implemented in order to verify the system performance. When only the RPi was used the processing rate was 48fps for images with resolution 512x512 pixels. RPi and FPGA co-design achieved processing rate 170fps for the same resolution images, which means an acceleration of about 350%. the proposed system was also evaluated in terms of power consumption.

关键词： fpga accelerator raspberry pi

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LSSM-SpMM: A Long-Row Splitting and Short-Row Merging Approach for parallel SpMM on PEZY-SC3s 24th

LSSM-SpMM: A Long-Row Splitting and Short-Row Merging Appro...

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24th international conference on algorithms and architectures for parallel processing, ICA3PP 2024

作者： Cao, Ligang Wang, Qinglin Yang, Shun Xia, Rui Guo, Weihao Liu, Jie Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China

ISBN: (纸本)9789819615506

Sparse Matrix-Dense Matrix Multiplication (SpMM) is a crucial kernel used in a wide range of fields including machine learning and linear algebra solvers. thus, enhancing the performance of SpMM is essential. the uneven distribution of non-zeros in sparse matrices and the tight data dependency between sparse and dense matrices make efficiently running SpMM on various hardware platforms challenging. To address these issues, optimisations are tailored according to the characteristics of the different hardware platforms. In this study, we propose a Long Row Split and Short Row Merge (LSSM) approach on the new MIMD computing platform PEZY-SC3s, utilising the standard Compressed Sparse Row (CSR) format to optimise SpMM. Specifically, LSSM divides the rows of the sparse matrix into short rows (rows with a number of non-zeros less than blockSize) and long rows (rows with a number of non-zeros greater than blockSize), applying splitting to the long rows and merging to the short rows to optimize their computations separately. Additionally, based on the hardware features of PEZY-SC3s, we employed the atomic cache for workload scheduling, SIMD instructions to accelerate computation, and Local Memory to reduce data read-write operations, addressing issues of poor data locality, workload imbalance, and vectorisation during SpMM execution. As the first study of SpMM on PEZY-SC3s, compared with BS-SpMM and RoDe-SpMM implemented on PEZY-SC3s, LSSM-SpMM offers up to 26.17× and 13.59× acceleration on the SuiteSparse and deep learning datasets, respectively, with geometric mean speedups of 1.56× and 1.71×. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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