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AIAA Aviation Forum

作者： Sundaram, Prasannabalaji Mendez, Julio C. Sengupta, Aditi Sengupta, Tapan K. IIT Dept Mech Engn ISM Dhanbad 826004 Jharkhand India Corrdesa LLC Tyrone GA 30290 USA CERFACS 42 Ave Gaspard Coriolis F-31100 Toulouse Occitanie France

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

In this study, we investigate the onset process of Rayleigh-Taylor Instability (RTI), which arises when two fluids of different densities mix upon removing the non-conductive interface separating them. Prior research [1] demonstrated this process to be initiated by infra and ultra-sonic acoustic waves, achieved by solving the compressible Navier-Stokes equation using 4.19 billion grid points and a refined timestep of 7.69 x 10(-8) s. The interface was set at y* = 0.5L, following Read's (1984) experiments [2]. We expand upon this work by relocating the interface to y* = 0.3L. To create the density discrepancy, air masses of distinct temperatures are initially divided by an impermeable partition. Upon impulsive removal at t = 0, the resulting instability is calculated using a parallel algorithm with error-free sub-domain closure to examine the compressible enstrophy transport equation when modifying the interface location. The high-performance computing (HPC) algorithm employs exceptional resolution to accurately capture acoustic disturbances and flow instability. We report on the parallel performance and efficiency of the HPC with the specialized sub-domain boundary closure, utilizing up to 262k cores.

关键词： High Performance Computing Direct Numerical Simulation Acoustic Attenuation Navier Stokes Equations parallel algorithm Wave Number Coefficient of Viscosity Vorticity Boussinesq Approximation Transition to Turbulence

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parallel and Scalable Heat Methods for Geodesic Distance Computation

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2021年第2期43卷 579-594页

作者： Tao, Jiong Zhang, Juyong Deng, Bailin Fang, Zheng Peng, Yue He, Ying Univ Sci & Technol China Sch Math Sci Hefei 230026 Anhui Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF10 3AT Wales Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore

In this paper, we propose a parallel and scalable approach for geodesic distance computation on triangle meshes. Our key observation is that the recovery of geodesic distance with the heat method [1] can be reformulated as optimization of its gradients subject to integrability, which can be solved using an efficient first-order method that requires no linear system solving and converges quickly. Afterward, the geodesic distance is efficiently recovered by parallel integration of the optimized gradients in breadth-first order. Moreover, we employ a similar breadth-first strategy to derive a parallel Gauss-Seidel solver for the diffusion step in the heat method. To further lower the memory consumption from gradient optimization on faces, we also propose a formulation that optimizes the projected gradients on edges, which reduces the memory footprint by about 50 percent. Our approach is trivially parallelizable, with a low memory footprint that grows linearly with respect to the model size. This makes it particularly suitable for handling large models. Experimental results show that it can efficiently compute geodesic distance on meshes with more than 200 million vertices on a desktop PC with 128 GB RAM, outperforming the original heat method and other state-of-the-art geodesic distance solvers.

关键词： Optimization Linear systems Memory management Computational modeling Heat recovery Approximation algorithms Heat method heat diffusion poisson equation scalability parallel algorithm

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Two parallel algorithms for finding all minimal maximum subsequences

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JOURNAL OF COMPUTER AND SYSTEM SCIENCES 2019年 104卷 216-243页

作者： Dai, H. K. Wang, Z. Oklahoma State Univ Dept Comp Sci Stillwater OK 74078 USA

A minimal maximum subsequence is a minimal subsequence with maximum cumulative sum. We present two parallel algorithms that find all successive minimal maximum subsequences: one on the parallel random-access model in logarithmic time with linear work, and the other with overlapping domain decomposition on cluster systems. We confirm the efficacy and efficiency of the latter algorithm for random sequences via: (1) an application of random-walk theory that derives an approximate probabilistic length upper bound for overlapping subsequences - thus facilitating concurrent/independent computations of all minimal maximum subsequences in hosting processors, and (2) an empirical study with normally-distributed random sequences. (C) 2018 Elsevier Inc. All rights reserved.

关键词： All maximum subsequences parallel algorithm parallel random-access machine model Theory of random walk Message Passing Interface

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Research on Service Scanning Supervision Technology Based on Improved Association algorithm 3

Research on Service Scanning Supervision Technology Based on...

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3rd IEEE International Conference on Electrical Engineering, Big Data and algorithms, EEBDA 2024

作者： Shang, Qingqing Liu, Chao Li, Jiwei Ren, Liwei Zhang, Xinyi Jia, Zhen Jiang, Yuchen Liu, Haitao State Grid Information & Telecommunication Branch Beijing China State Grid Zhejiang Electric Power Co. Ltd. Lishui Power Supply Company Zhejiang Lishui China

ISBN: (纸本)9798350380989

The scale of data in the Internet environment is becoming increasingly large and complex. Its heterogeneous, dynamic and distributed information characteristics have caused the existing business scanning and monitoring models to be unable to meet its application needs. This project proposes an improved iterative algorithm based on multiple servers. By introducing the idea of local operation of memory nodes, a service scanning and monitoring model that can support concurrency is constructed. A cluster analysis method based on genetic algorithm based on cluster analysis is proposed. It is then synthesized to correlate the largest set of recurring items to achieve data association, extend the iterative service scanning supervision algorithm, and obtain user-interested implicit patterns from server logs. At the same time, personalized service purposes are achieved by recommending implicit interest collections to users. Simulation results show that this method can effectively improve the performance of business search monitoring, and has the characteristics of enhancement as the data size increases. © 2024 IEEE.

关键词： iterative algorithm local computing parallel algorithm Web data association

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A Spark-Based parallel Fuzzy c-Means Segmentation algorithm for Agricultural Image Big Data

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IEEE ACCESS 2019年 7卷 42169-42180页

作者： Liu, Bin He, Songrui He, Dongjian Zhang, Yin Guizani, Mohsen Northwest A&F Univ Coll Informat Engn Xianyang 712100 Peoples R China Northwest A&F Univ Key Lab Agr Internet Things Minist Agr & Rural Affairs Xianyang 712100 Peoples R China Northwest A&F Univ Shaanxi Key Lab Agr Informat Percept & Intelligen Xianyang 712100 Peoples R China Northwest A&F Univ Coll Mech & Elect Engn Xianyang 712100 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Safety Engn Wuhan 430073 Hubei Peoples R China Anhui Prov Key Lab Network & Informat Secur Wuhu 241002 Peoples R China Univ Idaho Elect & Comp Engn Dept Moscow ID 83844 USA

With the explosive growth of image big data in the agriculture field, image segmentation algorithms are confronted with unprecedented challenges. As one of the most important images segmentation technologies, the fuzzy c-means (FCMs) algorithm has been widely used in the field of agricultural image segmentation as it provides simple computation and high-quality segmentation. However, due to its large amount of computation, the sequential FCM algorithm is too slow to finish the segmentation task within an acceptable time. This paper proposes a parallel FCM segmentation algorithm based on the distributed memory computing platform Apache Spark for agricultural image big data. The input image is first converted from the RGB color space to the lab color space and generates point cloud data. Then, point cloud data are partitioned and stored in different computing nodes, in which the membership degrees of pixel points to different cluster centers are calculated and the cluster centers are updated iteratively in a data-parallel form until the stopping condition is satisfied. Finally, point cloud data are restored after clustering for reconstructing the segmented image. On the Spark platform, the performance of the parallel FCMs algorithm is evaluated and reaches an average speedup of 12.54 on ten computing nodes. The experimental results show that the Spark-based parallel FCMs algorithm can obtain a significant increase in speedup, and the agricultural image testing set delivers a better performance improvement of 128% than the Hadoop-based approach. This paper indicates that the Spark-based parallel FCM algorithm provides faster speed of segmentation for agricultural image big data and has better scale-up and size-up rates.

关键词： Fuzzy C-means image segmentation image big data Apache Spark parallel algorithm

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A Fast Multicore-based Window Entropy algorithm

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2022年第11期13卷 241-247页

作者： Shokr, Suha S. A. Bahig, Hazem M. Univ Hail Coll Comp Sci & Engn Informat & Comp Sci Dept Hail 81481 Saudi Arabia

Malware analysis is a major challenge in cybersecurity due to the regular appearance of new malware and its effect in cyberspace. The existing tools for malware analysis enable reverse engineering to understand the origin, purpose, attributes, and potential consequences of malicious software. An entropy method is one of the techniques used to analyze and detect malware, which is defined as a measure of information encoded in a series of values based upon the probability of those values appearing. The window entropy algorithm is one of the methods that can be applied to calculate entropy values in an effective manner. However, it requires a significant amount of time when the size of the file is large. In this paper, we solve this problem in two ways. The first way of improvement is determining the best window size that leads to minimizing the running time of the window entropy algorithm. The second way of improvement is by parallelizing the window entropy algorithm on a multicore system. The experimental studies using artificial data show that the improved sequential algorithm can reduce the window entropy method's running time by 79% on an average. Also, the proposed parallel algorithm outperforms the modified sequential algorithm by 77% and has super-linear speed up.

关键词： Entropy window method malware analysis parallel algorithm multicore

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Billion-scale Detection of Isomorphic Nodes

Billion-scale Detection of Isomorphic Nodes

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37th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Cappelletti, Luca Fontana, Tommaso Reese, Justin Bader, David A. Univ Milan Dept Comp Sci Milan Italy Berkeley Lab EGSB Berkeley CA USA New Jersey Inst Technol Dept Data Sci Newark NJ USA

ISBN: (纸本)9798350311990

This paper presents an algorithm for detecting attributed high-degree node isomorphism. High-degree isomorphic nodes seldom happen by chance and often represent duplicated entities or data processing errors. By definition, isomorphic nodes are topologically indistinguishable and can be problematic in graph ML tasks. The algorithm employs a parallel, "degree-bounded" approach that fingerprints each node's local properties through a hash, which constrains the search to nodes within hash-defined buckets, thus minimising the number of comparisons. This method scales on graphs with billions of nodes and edges. Finally, we provide isomorphic node oddities identified in real-world data.

关键词： graph node isomorphism parallel algorithm

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parallel Fast Pencil Drawing Generation algorithm Based on GPU

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IEEE ACCESS 2019年 7卷 83543-83555页

作者： Qiu, Jiyan Liu, Bin He, Jinrong Liu, Chaoyang Li, Yuancheng Northwest A&F Univ Coll Informat Engn Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Key Lab Agr Internet Things Minist Agr & Rural Affairs Yangling 712100 Shaanxi Peoples R China Northwest A&F Univ Shaanxi Key Lab Agr Informat Percept & Intelligen Yangling 712100 Shaanxi Peoples R China Yanan Univ Coll Math & Comp Sci Yanan 712100 Peoples R China Xian Univ Sci & Technol Sch Comp Sci & Technol Xian Shaanxi Peoples R China

With the development of image processing technology, pencil drawing has been widely used in video games and mobile phone applications. However, the existing pencil drawing algorithms require a large amount of time to convert a real picture into a pencil drawing;hence, it is difficult to apply them to real-time systems. This paper proposes a parallel fast pencil drawing generation algorithm based on the graphics processing unit (GPU) to accelerate the real-time rendering process of sketch painting. The parallelism of the pencil drawing generation algorithm is identified via a theoretical analysis at first. Then, sub-algorithms of the sequential algorithm are designed in parallel using the compute unified device architecture (CUDA) programming model and executed via thread-level parallel techniques. Furthermore, an optimal cache pattern of data that reduce the access time of the most frequently used data is structured using shared memory and constant memory. Finally, task-level parallelism is achieved by the CUDA stream technology, which overlaps independent sub-tasks for further acceleration. On the CUDA platform, the experimental results demonstrate that the proposed parallel algorithm can achieve a significant increase in speedup. The proposed algorithm achieves a performance improvement of 448.59 times compared with the sequential algorithm, on 2560 x 1920-resolution images, and maintains a high degree of similarity with the real pencil paintings. Hence, the proposed algorithm is suitable for real-time pencil drawing rendering and has promising application prospects in non-photorealistic rendering.

关键词： Non-photorealistic rendering pencil drawing parallel algorithm GPU platform convolution operation CUDA

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Distributed Sparse Random Projection Trees for Constructing K-Nearest Neighbor Graphs 37

Distributed Sparse Random Projection Trees for Constructing ...

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37th IEEE International parallel and Distributed Processing Symposium (IPDPS)

作者： Ranawaka, Isuru Rahmant, Md Khaledur Azad, Ariful Indiana Univ Bloomington IN 47405 USA Meta Inc Menlo Pk CA USA

ISBN: (纸本)9798350337662

A random projection tree that partitions data points by projecting them onto random vectors is widely used for approximate nearest neighbor search in high-dimensional space. We consider a particular case of random projection trees for constructing a k-nearest neighbor graph (KNNG) from highdimensional data. We develop a distributed-memory Random Projection Tree (DRPT) algorithm for constructing sparse random projection trees and then running a query on the forest to create the KNN graph. DRPT uses sparse matrix operations and a communication reduction scheme to scale KNN graph constructions to thousands of processes on a supercomputer. The accuracy of DRPT is comparable to state-of-the-art methods for approximate nearest neighbor search, while it runs two orders of magnitude faster than its peers. DRPT is available at https://***/HipGraph/DRPT.

关键词： distributed memory algorithms k nearest neighbor graph parallel algorithm

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A parallel branch-and-bound algorithm to compute a tighter tardiness bound for preemptive global EDF

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REAL-TIME SYSTEMS 2019年第2期55卷 349-386页

作者： Leoncini, Mauro Montangero, Manuela Valente, Paolo Univ Modena & Reggio Emilia Dipartimento Sci Fis Informat & Matemat Via Campi 213-B Modena Italy CNR Ist Informat & Telemat Via Moruzzi 1 Pisa Italy

In this paper we present a parallel exact algorithm to compute an upper bound to tardiness of preemptive global EDF (G-EDF) schedulers, named harmonic bound, which has been proved to be up to 30% tighter than previously proposed bounds. Tightness is a crucial property of tardiness bounds: a too loose bound may cause a feasible soft real-time application to be mistakenly deemed unfeasible. Unfortunately, no polynomial-time algorithm is known to date to compute the harmonic bound. Although there is no proof of hardness of any sort either, the complex formula of the bound apparently provides no hints to devise algorithms with sub-exponential worst-case cost. In this paper we address this issue by proposing a parallel, exact, branch-and-bound algorithm to compute the harmonic bound, called harm-BB, which proves to be extremely fast in a large number of experiments. More specifically, we compare its execution times with those of existing polynomial-time algorithms for other known tardiness bounds on 630,000 random task sets. harm-BB outperforms, or is comparable to, the competitor algorithms in all scenarios but the ones with the highest number of processors (7 and 8) and tasks (similar to 50). In the latter scenarios harm-BB is indeed slower than the other algorithms;yet, it was still feasible, as it takes only about 2.8 s to compute the bound on a commodity dual-core CPU. Even better, we show that harm-BB has a high parallel efficiency, thus its execution time may be largely cut down on highly-parallel platforms.

关键词： Global EDF scheduler Tardiness Branch-and-bound parallel algorithm

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