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Comparative investigation of GPU-accelerated triangle-triangle intersection algorithms for collision detection

MULTIMEDIA TOOLS AND APPLICATIONS 2022年第3期81卷 3165-3180页

作者： Xiao, Lei Mei, Gang Cuomo, Salvatore Xu, Nengxiong China Univ Geosci Sch Engn & Technolgy Beijing Peoples R China Univ Naples Federico II Dept Math & Applicat R Caccioppoli Naples Italy

Efficient collision detection is critical in 3D geometric modeling. In this paper, we first implement three parallel triangle-triangle intersection algorithms on a GPU and then compare the computational efficiency of these three GPU-accelerated parallel triangle-triangle intersection algorithms in an application that detects collisions between triangulated models. The presented GPU-based parallel collision detection method for triangulated models has two stages: first, we propose a straightforward and efficient parallel approach to reduce the number of potentially intersecting triangle pairs based on AABBs, and second, we conduct intersection tests with the remaining triangle pairs in parallel based on three triangle-triangle intersection algorithms, i.e., the Moller's algorithm, Devillers' and Guigue's algorithm, and Shen's algorithm. To evaluate the performance of the presented GPU-based parallel collision detection method for triangulated models, we conduct four groups of benchmarks. The experimental results show the following: (1) the time required to detect collisions for the triangulated model consisting of approximately 1.5 billion triangle pairs is less than 0.5 s;(2) the GPU-based parallel collision detection method speedup over the corresponding serial version is 50x - 60x, and (3) Devillers' and Guigue's algorithm is comparatively and comprehensively the best of the three GPU-based parallel triangle-triangle intersection algorithms. The presented GPU-accelerated method is capable of efficiently detecting the potential collisions of triangulated models. Overall, the GPU-accelerated parallel Devillers' and Guigue's triangle-triangle intersection algorithm is recommended when performing practical collision detections between large triangulated models.

关键词： Collision detection Triangle-triangle intersection Graphics processing unit parallel algorithm

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Partitioned analysis of soil-structure interaction for Nuclear Island Buildings

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EARTHQUAKE ENGINEERING & STRUCTURAL DYNAMICS 2022年第10期51卷 2220-2247页

作者： Chen, Shaolin Lv, Hao Zhou, Guoliang Nanjing Univ Aeronaut & Astronaut Dept Civil & Airport Engn Nanjing Peoples R China Minist Environm Protect Nucl & Radiat Safety Ctr Beijing Peoples R China

The present methods for three-dimensional time-domain soil-structure interaction (SSI) analysis are often uneconomical because they are performed with a single time integration scheme and a single time step, which prohibits their application to large-scale SSI problems. In this study, a partitioned analysis of SSI (PASSI) is proposed for enhancing SSI computational efficiency. This is accomplished by partitioning the soil-foundation-structure system into the soil (foundation) and structure subsystems and implementing the continuity conditions of the displacements and reaction forces at the soil (foundation)-structure interface in a primal way. A lumped-mass explicit finite element method and a transmitting artificial boundary are used to model the unbounded soil, the structure is analyzed via the implicit finite element method, and the response of the rigid foundation is calculated through an explicit time integration scheme. The solution is separately advanced over time for each subsystem. Different time steps can be chosen for the explicit and implicit integration schemes, which can greatly improve efficiency. Interaction effects are accounted for by the transmission and synchronization of the coupled state variables. In addition, intrafield and interfield parallel procedures for PASSI are developed, and their theoretical efficiencies are analyzed. A simple example is provided to verify the performance of the partitioned approach with an explicit-implicit co-computation and to compare it to the fully explicit approach. Finally, the seismic response analysis of a nuclear power plant is presented to validate the feasibility and efficiency of the intrafield and interfield parallel procedures.

关键词： explicit-implicit integration scheme parallel algorithm soil-structure interaction the lumped mass explicit finite element method transmitting artificial boundary

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parallel Non-Monte Carlo Transient Noise Simulation

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IEEE MICROWAVE AND WIRELESS COMPONENTS LETTERS 2021年第6期31卷 634-637页

作者： Goulet, Alex Farhan, Mina Kassis, Marco T. Khazaka, Roni McGill Univ Dept Elect & Comp Engn Montreal PQ H3A 0E9 Canada Cadence Design Syst Inc San Jose CA 95134 USA

A novel parallel non-Monte Carlo (MC) transient noise analysis method for general nonlinear analysis is presented. Non-MC-based transient noise methods avoid expensive and lengthy MC iterations by describing the nonlinear system with a linear time-varying (LTV) system that can be used to calculate the variance of the waveform at every time point. However, the main bottleneck in these methods is that the LTV system is dense, strongly coupled, and significantly larger compared to the original system. To address this issue, the proposed method decouples the LTV system into homogeneous and inhomogeneous solutions that can be solved independently. The inhomogeneous solution is solved in parallel by partitioning the time interval into independent subintervals, while the complexity of the homogeneous system can be decreased significantly due to the linearity of the equations.

关键词： Mathematical model Integrated circuit modeling Wireless communication Transient analysis Nonhomogeneous media Thermal noise Microwave integrated circuits Circuit simulation noise analysis noise nonlinear circuits parallel algorithm

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BSN MOT: A Fast and Cost-efficient Optoelectronic Architecture

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IETE TECHNICAL REVIEW 2022年第1期39卷 37-48页

作者： Gupta, Ashish Sarkar, Bikash Kanti Birla Inst Technol Dept Comp Sci & Engn Ranchi 835215 Bihar India

Recently, an efficient recursive and symmetrical optoelectronic interconnection network based on biswapped framework named "Biswapped-Hyper Hexa-Cell (BSN HHC)" is reported. In spite of containing a prime advantage concerning network diameter compared to its competitive Biswapped-Mesh (BSN Mesh) optoelectronic architecture;a major limitation of the network is its high cost. In a search for a competitive alternative variation of this network, a fast and a cost-efficient member of biswapped family named "Biswapped-Mesh of Trees (BSN MOT)" is proposed here. The suggested BSN MOT optoelectronic network claims to be a better alternative of counter-part OTIS-Mesh of Trees (OTIS MOT) optoelectronic network, claims advantages concerning diameter, minimum node degree, and network total and optical cost (total cost drops about 10-30% and optical cost drops about 50%). More specifically, compared to competitive Biswapped-Mesh and BSN HHC optoelectronic networks, the proposed network has a major advantage concerning network diameter. Moreover, BSN MOT is a cost-efficient architecture, where total network cost drops about 30-45% than that of BSN HHC. Besides, it is a better alternative for processing numerical problems that require network with "low static maximum node degree". Thus, on the basis of above-mentioned properties, the proposed network claims to be the most economical and the fastest variation of biswapped framework.

关键词： Biswapped Hyper hexa-cell Mesh Mesh of trees Optoelectronic parallel algorithm

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Speeding up wheel factoring method

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JOURNAL OF SUPERCOMPUTING 2022年第14期78卷 15730-15748页

作者： Bahig, Hazem M. Nassr, Dieaa, I Mahdi, Mohammed A. Hazber, Mohamed A. G. Al-Utaibi, Khaled Bahig, Hatem M. Univ Hail Coll Comp Sci & Engn Informat & Comp Sci Dept Hail Saudi Arabia Ain Shams Univ Fac Sci Math Dept Comp Sci Div Cairo Egypt Univ Hail Coll Comp Sci & Engn Comp Engn Dept Hail Saudi Arabia

The security of many public key cryptosystems that are used today depends on the difficulty of factoring an integer into its prime factors. Although there is a polynomial time quantum-based algorithm for integer factorization, there is no polynomial time algorithm on a classical computer. In this paper, we study how to improve the wheel factoring method using two approaches. The first approach is introducing two sequential modifications on the wheel factoring method. The second approach is parallelizing the modified algorithms on a parallel system. The experimental studies on composite integers n that are a product of two primes of equal size show the following results. (1) The percentages of improvements for the two modified sequential methods compared to the wheel factoring method are almost 47% and 90%. (2) The percentage of improvement for the two proposed parallel methods compared to the two modified sequential algorithms is 90% on the average. (3) The maximum speedup achieved by the best parallel proposed algorithm using 24 threads is almost 336 times the wheel factoring method.

关键词： Integer factoring Wheel factorization parallel algorithm RSA Multicore

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SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDs

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IEEE ACCESS 2022年 10卷 62502-62519页

作者： Chon, Kang-Wook Yi, Eunjeong Kim, Min-Soo Korea Inst Sci & Technol Informat Div Natl Supercomp Daejeon South Korea Daegu Gyeongbuk Inst Sci & Technol Dept Informat & Commun Engn Daegu South Korea Korea Adv Inst Sci & Technol Sch Comp Daejeon South Korea

Frequent itemset mining is extensively employed as an essential data mining technique. Nevertheless, as the data size grows, the applicability of this method decreases owing to the relatively poor performance of the existing methods. Though numerous efficient sequential frequent itemset mining methods have been developed, the performance that can be achieved is clearly limited by the fact that they exploit only one thread. To overcome these limitations, a number of parallel methods using multi-core central processing units (CPUs), multiple machines or many-core graphic processing units (GPU) have been proposed. However, these methods are relatively slow in performance and have low scalability, mainly owing to large memory requirements for intermediate data, significant disk I/Os, and heavy computation. In this study, to resolve the aforementioned problems, we propose SGMiner, which is a new, fast, and scalable GPU-and disk-based method on a single machine equipped with multiple graphic processing units (GPUs) and multiple solid-state drives (SSDs) for extracting frequent patterns. It is based on an algorithm similar to the Apriori algorithm and neither has intermediate data nor large disk I/O overheads owing to its exploitation of SSDs. Moreover, we propose storing transaction databases, namely bitmap transaction chunks, in SSDs, streaming the chunks to GPU device memory via the main memory with reduced I/O overhead, and performing fast support counting with GPUs based on the chunks. In addition, when exploiting multiple GPUs and SSDs, it proposes a concept of replicating bitmap transaction chunks stored in SSDs to GPUs in a streaming fashion. This could allow an almost equal workload to be distributed evenly across multiple GPUs with reduced I/O overheads. The experiments we conducted demonstrate that SGMiner outperforms the existing methods in terms of scalability and performance with enhanced robustness.

关键词： Big data frequent pattern mining parallel algorithm GPUs scalable algorithm disk-based algorithm

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Contour algorithm for Connectivity 30

Contour Algorithm for Connectivity

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30th International Conference on High Performance Computing, Data, and Analytics (HiPC)

作者： Du, Zhihui Rodriguez, Oliver Alvarado Li, Fuhuan Dindoost, Mohammad Bader, David A. New Jersey Inst Technol Dept Data Sci Newark NJ 07102 USA

ISBN: (纸本)9798350383225

Finding connected components in a graph is a fundamental problem in graph analysis. In this work, we present a novel minimum-mapping based Contour algorithm to efficiently solve the connectivity problem. We prove that the Contour algorithm with two or higher order operators can identify all connected components of an undirected graph within O(log dmax) iterations, with each iteration involving O(m) work, where dmax represents the largest diameter among all components in the given graph, and m is the total number of edges in the graph. Importantly, each iteration is highly parallelizable, making use of the efficient minimum-mapping operator applied to all edges. To further enhance its practical performance, we optimize the Contour algorithm through asynchronous updates, early convergence checking, eliminating atomic operations, and choosing more efficient mapping operators. Our implementation of the Contour algorithm has been integrated into the open-source framework Arachne. Arachne extends Arkouda for large-scale interactive graph analytics, providing a Python API powered by the high-productivity parallel language Chapel. Experimental results on both real-world and synthetic graphs demonstrate the superior performance of our proposed Contour algorithm compared to state-of-the-art large-scale parallel algorithm FastSV and the fastest shared memory algorithm ConnectIt. On average, Contour achieves a speedup of 7.3x and 1.4x compared to FastSV and ConnectIt, respectively. All code for the Contour algorithm and the Arachne framework is publicly available on GitHub 1, ensuring transparency and reproducibility of our work.

关键词： connected components graph analytics big data parallel algorithm

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LSU factorization

LSU factorization

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Malaschonok, Gennadi Natl Univ Kyiv Mohyla Acad Fac Comp Sci Kyiv Ukraine

ISBN: (纸本)9798350361513;9798350372304

The matrix LU factorization algorithm is a fundamental algorithm in linear algebra. We propose a generalization of the LU and LEU algorithms to accommodate the case of a commutative domain and its field of quotients. This algorithm decomposes any matrix A into a product of three matrices A=LSU, where each element of the triangular matrices L and U is a minor of matrix A. The number of non-zero elements in matrix S is equal to rank(A), and each of them is the inverse of the product of a specific pair of matrix A minors. The algorithm's complexity is equivalent to that of matrix multiplication.

关键词： matrix algorithm LU factorization LSU factorization complexity parallel algorithm

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Image Sobel edge extraction algorithm accelerated by OpenCL

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JOURNAL OF SUPERCOMPUTING 2022年第14期78卷 16236-16265页

作者： Xiao, Han Xiao, Shiyang Ma, Ge Li, Cailin Zhengzhou Normal Univ Sch Informat Sci & Technol Zhengzhou 450044 Peoples R China Southeast Univ Sch Civil Engn Nanjing 211189 Peoples R China Shandong Univ Technol Sch Civil & Architectural Engn Zibo 255000 Peoples R China

Aiming at the low processing speed of the Sobel edge detection algorithm and the equipment limitations of Compute Unified Device Architecture (CUDA) implementation algorithm acceleration, a Sobel edge detection parallel algorithm based on Open Computing Language (OpenCL) architecture is proposed. The algorithm uses the heterogeneous mode of CPU + GPU to achieve algorithm acceleration. According to the parallel structure and hardware characteristics of Graphics Processing Unit (GPU), the parallel algorithm adopts two acceleration technologies, multi-level storage technology, and vector access technology, which optimizes the data storage structure, improves the data access efficiency, and reduces the complexity of the algorithm. Unlike the CUDA implementation algorithm acceleration for NVIDIA graphics card devices, the OpenCL parallel improved algorithm has no device limitations. Experimental results show that compared with the CPU serial algorithm, the OpenMP parallel algorithm, and the CUDA parallel algorithm, the parallel algorithm has obtained 9.55 times, 2.23 times, and 1.17 times speedup, respectively. The parallel algorithm in this paper shows good data expansibility and platform portability and can provide technical support for the deep application of massive image data.

关键词： Sobel operator Edge detection Graphics Processing Unit (GPU) Open Computing Language (OpenCL) parallel algorithm

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IMToolkit: An Open-Source Index Modulation Toolkit for Reproducible Research Based on Massively parallel algorithms

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IEEE ACCESS 2019年 7卷 93830-93846页

作者： Ishikawa, Naoki Hiroshima City Univ Grad Sch Informat Sci Hiroshima 7313166 Japan

This paper presents a proposal of an open-source index modulation (IM) toolkit, which facilitates reproducible research and accelerates open innovation in IM studies. The proposed toolkit is implemented based on massively parallel algorithms that are designed for state-of-the-art graphics processing units (GPUs). Since high-performance GPUs are available at low cost, along with the intensive development in deep learning, this toolkit achieves large scale but significantly fast Monte Carlo simulations at low cost. Two large-tensor-based parallel algorithms are introduced for bit error ratio and average mutual information simulations. Additionally, the design of active indices is newly formulated into an integer linear programming problem that guarantees optimality, which is applicable to the generalized spatial modulation and subcarrier-index modulation schemes. Performance comparisons demonstrated that the proposed GPU-aided algorithms were up to 145 times faster than the conventional CPU-aided efficient counterparts. Furthermore, the designed active indices achieved the theoretical optimum performance in contrast to widely used conventional methods. A comprehensive database of these designed active indices is released online and is available to any researcher.

关键词： MIMO OFDM index modulation spatial modulation subcarrier-index modulation open-source software reproducible research GPGPU parallel algorithm integer linear programming

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