Parallel Algorithm Research Articles

Overall plan optimization of BWB UAV based on comprehensive evaluation

Based on the shape and performance characteristics of the wing-body fusion layout UAV, a multidisciplinary comprehensive analysis model, comprehensive evaluation model and optimization model suitable for the overall scheme of the UAV in the conceptual design stage are established, and the corresponding tools are developed. The geometric, weight, aerodynamic and stealth characteristics of the overall scheme of the wing-body fusion layout UAV are analyzed by combining numerical analysis and engineering methods. The improved TOPSIS method is used to establish a comprehensive evaluation model from the perspective of the loading capacity, economy and adaptability of the UAV. The rationality of the analysis model is verified by taking multiple UAV schemes as examples. Using the parallel subset simulation optimization algorithm, the traditional multi-objective optimization is completed with the maximum cruise lift-to-drag ratio and the minimum forward RCS as the goal, and the scheme optimization based on comprehensive evaluation is completed with the strongest competitiveness as the goal. Based on all the schemes generated by the multi-objective optimization process, the parameter correlation analysis is completed, and the influence of the span, sweep angle and twist angle on the aerodynamic and stealth characteristics is verified. The RCS of the final multi-objective optimization scheme is reduced 41.6%, and the cruise lift-to-drag ratio is improved 3.5% ; the competitiveness score of the comprehensive evaluation optimization scheme is improved 44.0%.

Embedding of Hypercube into Fractal Cubic Network

The implementation of parallel algorithms and the simulation of interconnection networks can be modeled into a graph embedding problem. Several cost parameters are used to assess the quality of an embedding. The wirelength is one of these factors that is frequently taken into account. A fractal cubic network is a new variant of the hypercube graph. In this study, we compute the wirelength of an embedding of the hypercube [Formula: see text] into the fractal cubic network [Formula: see text] using [Formula: see text]-partition.

Triangle Centrality

Triangle centrality is introduced for finding important vertices in a graph based on the concentration of triangles surrounding each vertex. It has the distinct feature of allowing a vertex to be central if it is in many triangles or none at all. Given a simple, undirected graph \(G=(V,E)\) with \(n=|V|\) vertices and \(m=|E|\) edges, let \(\triangle(v)\) and \(\triangle(G)\) denote the respective triangle counts of \(v\) and \(G\) . Let \(N(v)\) be the neighborhood set of \(v\) . Respectively, \(N_{\triangle}(v)\) and \(N_{\triangle}[v]=\{v\}\cup N_{\triangle}(v)\) denote the set of neighbors that are in triangles with \(v\) and the closed set including \(v\) . Then the triangle centrality for a vertex \(v\) is \(\begin{align*}TC(v)=\frac{\frac{1}{3}\sum_{u\in N_{\triangle}[v]}\triangle(u)+\sum_{w\in\{N(v)\setminus N_{\triangle}(v)\}}\triangle(w)}{\triangle(G)}.\end{align*}\) We show experimentally that triangle centrality is broadly applicable to many different types of networks. Our empirical results demonstrate that 30% of the time triangle centrality identified central vertices that differed with those found by five well-known centrality measures, which suggests novelty without being overly specialized. It is also asymptotically faster to compute on sparse graphs than all but the most trivial of these other measures. We introduce optimal algorithms that compute triangle centrality in \(O(m\overline{\delta})\) time and \(O(m+n)\) space, where \(\overline{\delta}\leq O(\sqrt{m})\) is the average degeneracy introduced by Burkhardt, Faber, and Harris (2020). In practical applications, \(\overline{\delta}\) is much smaller than \(\sqrt{m}\) so triangle centrality can be computed in nearly linear time. On a Concurrent Read Exclusive Write (CREW) Parallel Random Access Machine (PRAM), we give a near work-optimal parallel algorithm that takes \(O(\log n)\) time using \(O(m\sqrt{m})\) CREW PRAM processors. In MapReduce, we show it takes four rounds using \(O(m\sqrt{m})\) communication bits and is therefore optimal. We also derive a linear algebraic formulation of triangle centrality which can be computed in \(O(m\overline{\delta})\) time on sparse graphs.

Parallel adaptive large neighborhood search based on spark to solve VRPTW

Aiming at the multi-objective vehicle path planning problem with time windows (VRPTW), a Spark-based parallel Adaptive Large Neighborhood Search algorithm (Spark-ALNS) is proposed to solve it. The main design of the 4-point strategy: (1) Design a new simulated annealing algorithm cooling strategy to achieve a better jump out of the local optimal solution. (2) Adopt CW initialization to accelerate the convergence speed. (3) Use three destruction operators and three repair operators to implement local path optimization. (4) A new parallel strategy is proposed to improve the algorithm’s accuracy and reduce the running time. To illustrate the algorithm’s effectiveness, the arithmetic example in Solomon is used as an example. The experimental results show that the proposed Spark-ALNS can find better solutions, get the known optimal solutions for 41 out of 56 instances, and find new optimal solutions for 31 algorithms, which outperforms other evolutionary algorithms. The runtime is 3–5 times better than other parallel algorithms and is able to solve VRPTW effectively.

A Novel Method for Aircraft Structural Dynamic Strain Trend Signal Processing via Optimized Parallel Computing

In this study, we investigate the underlying causes of drift in the time history curves of measured parameters obtained through strain electrical measurements and assess their impacts on load measurements. To address the challenge of efficiently processing large volumes of aircraft load data, we propose and analyze a multi-level parallel algorithm specifically designed for the data processing of aircraft load measurements. To achieve this objective, we discuss parallel processing at both medium- and fine-grained levels and develop two distinct parallel processing algorithms: one for coarse- and medium-grained aircraft-type data streams, and another for medium- and fine-grained takeoff and landing data streams. The efficacy of these algorithms is validated through the processing of load data measured on a specific aircraft wing. The results demonstrate that the proposed approach offers a novel technical pathway for large-scale scientific computations and enhances data processing efficiency in the domain of aircraft load spectrum analysis.

Skill acquisition framework in multi-robot precision assembly based on cooperative compliant control

Robotic assemblies are widely used in manufacturing processes. However, high-precision assembly remains challenging because of numerous uncertain disturbances. Current research mainly focuses on a single robot or weakly coupled multi-robot assembly. Nevertheless, more complex and uncertainty-filled tightly coupled multi-robot assemblies have been overlooked. This study proposes an efficient skill-acquisition framework to address this challenging task by improving learning efficiency. The framework integrates a dual-loop coupled force-position control (DLCFPC) algorithm, a parallel skill-learning algorithm, and collision detection. The DLCFPC was presented to address simultaneous motion and force control challenges. In addition, a parallel skill-learning algorithm was proposed to accelerate assembly skill acquisition. Simulations and experiments on a multi-robot cooperative peg-in-hole assembly confirm that the framework enables a multi-robot system to accomplish high-precision assembly tasks even without prior knowledge, demonstrating robustness against disturbances.

GPU parallel implementation of a finite volume lattice Boltzmann method for incompressible flows

This work presents a graphics processing units (GPU) parallel algorithm of a cell-centered finite volume lattice Boltzmann method (FVLBM) on unstructured meshes. In the present GPU parallel algorithm, the parallelization is performed in the physical space. To reduce the frequency of GPU memory accesses, this algorithm develops coalesced access to GPU memory. In addition, to avoid the race for resources leading to data anomalies, such as dirty read or phantom read etc., and the double counting for flux calculation, the efficient face-based data structure often used for flux calculation in cells in the central processing unit (CPU) version of FVLBM is modified into a face-based data structure used for the fluxes on all faces, followed by a cell-based loop for the final residuals in all cells. Therefore, the proposed GPU parallel algorithm does not need to use the resource lock and retains the high efficiency of the face-based data structure in the fluxes computation to enhance its’ parallel efficiency. Additionally, to demonstrate the computational efficiency of the proposed GPU parallel algorithm, various benchmark studies are performed in this work by the proposed parallel scheme on a double precision NVIDIA GeForce RTX 3090Ti GPU card, including (a) the lid-driven flow in a two-dimensional (2D) square cavity, (b) a 2D flow past a cylinder, and (c) the lid-driven flow in a three-dimensional (3D) cubic cavity. The numerical results show that the proposed GPU parallel algorithm can be as accurate as the original CPU serial scheme with 1 to 2 orders of speedup.

Parallel lightweight Block Cipher algorithm for Multicore CPUs

أصبحت حماية البيانات واحدة من أهم القضايا على الرغم من التقدم الكبير في الاتصالات والتكنولوجيا. لكي ترسل التكنولوجيا المستندة إلى الويب البيانات بسرعة وأمان ، يجب تشفير البيانات. التشفير هو عملية تحويل النص العادي إلى نص مشفر ، لا يستطيع الأشرار قراءته أو تغييره. تتطلب كل من إجراءات تحليل التشفير وفك التشفير قدرًا كبيرًا من الوقت من أجل الحفاظ على المستوى المطلوب من الأمان. ومع ذلك ، طور عدد من الباحثين نهج التشفير بالتوازي من أجل تقليل مقدار الوقت اللازم لإنهاء إجراءات التشفير وفك التشفير. أنتج التحقيق في هذه القضية عددًا من الحلول القابلة للتطبيق. تمكن الباحثون من تحقيق مستويات أداء محسّنة في تقنية التشفير باستخدام التوازي لزيادة الإنتاجية وتعزيز كفاءة طرق التشفير. لتحقيق أداء عالٍ ، تم تقديم خوارزميات تشفير البقعة خفيفة الوزن وتنفيذها على منصات وحدة المعالجة المركزية مع تحسينات مختلفة. وبالتالي ، في هذا العمل ، تم اقتراح مخطط تشفير خفيف الوزن يستخدم جولة واحدة فقط من تقنية تشفير الكتلة التي يتم تطبيقها بالتوازي على معالج متعدد النواة. تستخدم خوارزمية تشفير الرسائل المقترحة كتلتين فرعيتين من 128 بت من الرسائل العادية ومربع الاستبدال و splitmix64 PRNG لتشفير الرسالة العادية والحصول على كتلتين فرعيتين مشفرتين ، مما يجعلها تقنية سريعة لتشفير وفك تشفير كتل الرسائل. بالمقارنة مع الطريقة الحالية. وفقًا لنتائج الأداء ، فإنه قادر على الوصول إلى إنتاجية عالية للبيانات مقارنة ببعض الأساليب خفيفة الوزن الموجودة بالفعل ، مع معدل نقل أعلى من 25 كيكابت في الثانية على وحدة المعالجة المركزية Intel Core i7. تتفوق طريقة التشفير المقترحة على طريقة البقع المتوازية بمعدل 14.10 مرة أسرع عند تنفيذها على وحدة معالجة مركزية متعددة النواة. متوسط التسريع مقارنة بالنسخة التسلسلية للخوارزمية المقترحة والتنفيذ المتوازي لها هو 4.70. أيضًا ، توفر طريقة التشفير المقترحة قدرًا كبيرًا من العشوائية وتجتاز الاختبارات الإحصائية لـ PractRand. وبالتالي ، فإن الطريقة المقترحة هي منافس قوي للتنفيذ عالي الأمان على المعالجات متعددة النواة.

A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations

A unified consensus-based parallel algorithm for high-dimensional regression with combined regularizations

Effects of OpenCL-Based Parallelization Methods on Explicit Numerical Methods to Solve the Heat Equation

In recent years, the need for high-performance computing solutions has increased due to the growing complexity of computational tasks. The use of parallel processing techniques has become essential to address this demand. In this study, an Open Computing Language (OpenCL)-based parallelization algorithm is implemented for the Constant Neighbors (CNe) and CNe with Predictor–Corrector (CpC) numerical methods, which are recently developed explicit and stable numerical algorithms to solve the heat conduction equation. The CPU time and error rate performance of these two methods are compared with the sequential implementation and Euler’s explicit method. The results demonstrate that the parallel version’s CPU time remains nearly constant under the examined circumstances, regardless of the number of spatial mesh points. This leads to a remarkable speed advantage over the sequential version for larger data point counts. Furthermore, the impact of the number of timesteps on the crossover point where the parallel version becomes faster than the sequential one is investigated.

Parallel density clustering algorithm based on MapReduce and optimized cuckoo algorithm

Parallel density clustering algorithm based on MapReduce and optimized cuckoo algorithm

Algorithm Portfolios for Solving the Quadratic Assignment Problem

Introduction. The quadratic assignment problem is a well-established NP-hard problem in combinatorial optimization with applications in diverse fields like economics, archaeology, and chemistry. Due to its complexity, research on efficient solution methods remains active, including efforts for parallelization on multiprocessor computing systems. However, effective parallel algorithms are crucial to fully leverage these computational resources. In this context, algorithm unions (portfolios and teams) play a significant role in achieving parallel execution for solving such problems. Research objectives. This work investigates the application of portfolios constructed from modifications of the repeated iterated tabu search algorithm to the quadratic assignment problem. The effectiveness of these portfolios was evaluated through experimental computations. Results. The portfolios, derived from modifications of the repeated iterated tabu search algorithm, were applied to the quadratic assignment problem. For the most demanding test instances, the proposed algorithms were evaluated on the SCIT-4 supercomputer, alongside previously published results from the authors, confirming their competitive performance. Additionally, we assessed the parallel efficiency of these portfolios in solving instances of the quadratic assignment problem. The results demonstrate their ability to accelerate the optimization process (with speedup dependent on problem size and utilized processors), enabling the solution of large-scale problems. Conclusions. The conducted studies demonstrate that employing algorithm portfolios significantly accelerates the solution process for the quadratic assignment problem. Analysis of the results reveals a near-linear speedup factor achieved by the portfolio. For the challenging test instance tai100a, a new best solution value of 21040996 was obtained using a portfolio of 16 algorithms. Keywords: quadratic assignment problem, algorithm portfolios, experimental research, algorithm portfolios efficiency.

Dominant point-based sequential and parallel algorithms for the multiple sequential substring constrained-LCS problem

The Longest Common Subsequence (LCS) problem is a well-known and studied problem in computer science and bioinformatics. It consists in finding the longest subsequence that is common to two or more given sequences. In this paper, we address the problem of finding all LCS for the Sequential Substring Constrained-LCS (SSCLCS) problem, called the Multiple SSCLCS problem. To solve this problem, we first propose a dominant point-based sequential algorithm, designed on a new Leveled Direct Acyclic Graph (DAG) that gives the correct evaluation order of subproblems to avoid redundancy due to overlap. Depending on whether the constraints may overlap or not, it requires O ( S | Σ | K + 1 + r + n | Σ |) and O ( S | Σ | K + 1 + n | Σ |) time with \(O(Max\_level+n|\Sigma |) \) space. S is the number of partial SSCLCS in a node, K is the number of DAG levels, n is the length of sequences, r is the total length of constraints, \(Max\_level \) is the number of nodes in the largest level of the DAG, and | Σ | is the length of the alphabet. Then, we derive a coarse-grained multicomputer parallel solution requiring \(O\left(\frac{S|\Sigma |^{K+1}+r+n|\Sigma |}{p}\right) \) and \(O\left(\frac{S|\Sigma |^{K+1}+n|\Sigma |}{p}\right) \) execution time, \(O(Max\_level+n|\Sigma |) \) memory space and O ( K ) communication rounds. p is the number of processors. Experimental results showed that the parallel algorithm is respectively 14.43 × and 19.19 × faster than the sequential algorithm on 32 and 64 processors.

Study on Large-Scale Urban Water Distribution Network Computation Method Based on a GPU Framework

Large-scale urban water distribution network simulation plays a critical role in the construction, monitoring, and maintenance of urban water distribution systems. However, during the simulation process, matrix inversion calculations generate a large amount of computational data and consume significant amounts of time, posing challenges for practical applications. To address this issue, this paper proposes a parallel gradient calculation algorithm based on GPU hardware and the CUDA Toolkit library and compares it with the EPANET model and a model based on CPU hardware and the Armadillo library. The results show that the GPU-based model not only achieves a precision level very close to the EPANET model, reaching 99% accuracy, but also significantly outperforms the CPU-based model. Furthermore, during the simulation, the GPU architecture is able to efficiently handle large-scale data and achieve faster convergence, significantly reducing the overall simulation time. Particularly in handling larger-scale water distribution networks, the GPU architecture can improve computational efficiency by up to 13 times. Further analysis reveals that different GPU models exhibit significant differences in computational efficiency, with memory capacity being a key factor affecting performance. GPU devices with larger memory capacity demonstrate higher computational efficiency when processing large-scale water distribution networks. This study demonstrates the advantages of GPU acceleration technology in the simulation of large-scale urban water distribution networks and provides important theoretical and technical support for practical applications in this field. By carefully selecting and configuring GPU devices, the computational efficiency of large-scale water distribution networks can be significantly improved, providing more efficient solutions for future urban water resource management and planning.

On the adaptive solution of phase‐field problems with A‐stable explicit last‐stage diagonally implicit Runge–Kutta (ELDIRK) methods

AbstractRunge–Kutta algorithms with adaptive step size control provide reliable tools for the solution of initial value problems with diagonally implicit Runge‐Kutta (DIRK) methods as the most common approach. In this contribution, the new low‐order explicit last‐stage diagonally implicit Runge–Kutta (ELDIRK) methods are investigated, combining implicit schemes with an additional explicit evaluation as an explicit last stage. This results in Butcher tableaus with two solutions of different convergence orders suitable for embedded methods, where the higher‐order solution is achieved by additional explicit evaluations. Thus, the iterative solution of non‐linear systems is omitted for the additional stage, presenting a major reduction in computational cost for the determination of a local error estimate. The key contribution is the application of the novel Butcher tableaux to phase‐field problems, solved with the finite‐element method, leading to substantial numerical investigations with an efficient approach for DIRK schemes. The most important aspects are the investigations of stability properties which lead to the novel class of A‐stable ELDIRK methods. In accordance, the study of the convergence orders is presented. A local error estimator is presented, such that adaptive step size control for the new low‐order embedded schemes based on an empirical approach for error estimation is achieved. A suitable parallel algorithm is presented with conclusive two‐dimensional phase‐field simulations based on a Kobayashi‐Warren‐Carter model. The higher‐order convergence suggested by the novel schemes is confirmed, and their effective results are demonstrated, resulting in a valuable semi‐explicit addition to the family of Runge–Kutta time integration schemes.

Ordered and disordered stealthy hyperuniform point patterns across spatial dimensions

In previous work [] it was shown that stealthy hyperuniform systems can be regarded as hard spheres in Fourier space in the sense that the structure factor is exactly zero in a spherical region around the origin in analogy with the pair-correlation function of real-space hard spheres. While this earlier work focused on spatial dimensions d=1–4, here we extend the analysis to higher dimensions in order to make connections to high-dimensional sphere packings and the mean-field theory of glasses. We exploit this correspondence to confirm that the densest Fourier-space hard-sphere system is that of a Bravais lattice in contrast to real-space hard spheres, whose densest configuration is conjectured to be disordered. In passing, we give a concise form for the position of the first Bragg peak. We also extend the virial series previously suggested for disordered stealthy hyperuniform systems to higher dimensions in order to predict spatial decorrelation as a function of dimension. This prediction is then borne out by numerical simulations of disordered stealthy hyperuniform ground states in dimensions d=2–8, which have only recently been made possible due to a highly parallelized algorithm. Published by the American Physical Society 2024

Elastic MSM: A Fast, Elastic and Modular Preprocessing Technique for Multi-Scalar Multiplication Algorithm on GPUs

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.

Parallel fractional dominance MOEAs for feature subset selection in big data

In this paper, we solve the feature subset selection (FSS) problem with three objective functions namely, cardinality, area under receiver operating characteristic curve (AUC) and Matthews correlation coefficient (MCC) using novel multi-objective evolutionary algorithms (MOEAs). MOEAs often encounter poor convergence due to the increase in non-dominated solutions and getting entrapped in the local optima. This situation worsens when dealing with large, voluminous big and high-dimensional datasets. To address these challenges, we propose parallel, fractional dominance-based MOEAs for FSS under Spark. Further, to improve the exploitation of MOEAs, we introduce a novel batch opposition-based learning (BOP) along with a cardinality constraint on the opposite solution. Accordingly, we propose two variants, namely, BOP1 and BOP2. In BOP1, a single neighbour is randomly chosen in the opposite solution space, whereas in BOP2, a group of randomly chosen neighbours in the opposite solution space. In either case, the opposite solutions are evaluated to improve the exploitation capability of the underlying MOEAs. We observe that in terms of mean optimal objective function values and across all datasets, the proposed BOP2 variant of parallel fractional dominance-based algorithms emerges as the top performer in obtaining efficient solutions. Further, we introduce a novel metric, namely the ratio of hypervolume (HV) and inverted generated distance (IGD), HV/IGD, that combines both diversity and convergence. With respect to the mean HV/IGD computed over 20 runs and Formula 1 racing, the BOP1 variants of fractional dominance-based MOEAs outperformed other algorithms.

Parallel Optimization of Message Passing Interface for 3D Sound Propagation Model BELLHOP3D

Abstract BELLHOP3D is a widely used three-dimensional computational model for acoustic wave propagation in the ocean based on the ray method. Although its computational efficiency is relatively high, there is still significant scope for improvement. This paper presents an optimized version of BELLHOP3D that utilizes the message passing interface (MPI) to parallelize the calculation of azimuth and pitch angles. The parallel algorithm developed in this study exhibits high efficiency and produces stable and reliable results. The current work introduces both the serial and parallel algorithms of BELLHOP3D, and subsequently validates the accuracy of the parallel program via numerical simulations and also evaluates the parallel computing performance. The computation results demonstrate that parallel calculation of both azimuth and pitch angles through the use of the message transfer interface considerably enhances the parallel efficiency of BELLHOP3D.

SPNet: A Serial and Parallel Convolutional Neural Network algorithm for the cross-language coreference resolution

Current models of coreference resolution always neglect the importance of hidden feature extraction, accurate scoring framework design, and the long-term influence of preceding potential antecedents on future decision-making. However, these aspects play vital roles in scoring the likelihood of coreference between an anaphora and its’ real antecedent. In this paper, we present a novel model named Serial and Parallel Convolutional Neural Network (SPNet). Based on the SPNet, two kinds of resolvers are proposed. Given the characteristics of reinforcement learning, we joint the reinforcement learning framework and the SPNet to solve the problem of Chinese zero pronoun resolution. What’s more, we make some fine-tuning on the SPNet and propose a new resolver combined with the end-to-end framework to solve the problem of coreference resolution. The experiments are conducted on the CoNLL-2012 dataset and the results show that our model is effective. Our model achieves excellent performance in the Chinese zero pronoun resolution task. On the other hand, compared with our baseline, our model also has an improvement of 0.3% in coreference resolution task.