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Because of the open-collaborative of online encyclopedias, a large number of knowledge triples are improperly classified in online encyclopedia systems, and it is necessary to denoise and refine the open-domain encyclopedia Knowledge Bases (KBs) to improve the quality and precision. However, the lack and inaccuracy of triple semantic features lead to a poor refinement effect. In addition, considering large-scale encyclopedia KBs, the processing of massive knowledge will lead to too much computing time and poor scalability of the algorithm. To solve the problems of knowledge denoising in the Chinese encyclopedia system, first, based on data field theory, this paper proposes a new Cartesian product mapping-based method (TripleES) to calculate the semantic similarity of entity triples, based on which a method for quantifying the quality of entry tags is proposed. Second, to further improve the denoising effect on KBs, this paper proposes a new method (TriplePV) to compute the potential value of triple based on multi-feature fusion strategy to calculate the semantic distance between the "out-of-vocabulary" entry tags and embeds it into the potential function. Third, to ensure our algorithms have good scalability, the proposed denoising algorithms are implemented and optimized in parallel based on the Spark cluster-computing framework. Specifically, Spark-based TripleES (ES_Spark) and Spark-based TriplePV (PV_Spark) algorithms are proposed to calculate the semantic similarity and potential value of triples respectively. Finally, a comprehensive comparative analysis is performed on the denoising effect and time efficiency with the state-of-the-art distributed Chinese encyclopedia knowledge denoising algorithm. The experimental results on real-world datasets show that the parallel denoising algorithm proposed in this paper can improve the efficiency of knowledge denoising and the accuracy of KBs, which outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The sparse triangular solver (SpTRSV) is mostly used for scientific and engineering applications. The structured-grid triangular solver of regular dependencies (STRSV) is a special kind of SpTRSV. Some general SpTRSVs that disregards the regularity of the matrix are unsuitable for solving this problem. This paper proposes an efficient parallel algorithm for STRSV on the SW26010 (a kind of China independently designed many-core processors), namely swStructTRSV. The algorithm makes full use of the fine-grained and low latency communication characteristics of the SW26010 to reduce the waiting time for synchronization, maximizes the regularity of access to improve memory access bandwidth, and achieves overlap between memory access and computation simultaneously. Moreover, the idea of the algorithm can be extended to incomplete LU factorization (ILU factorization) because of consistent dependencies. The experimental results on a core group(8 * 8 network composed of 64 cores) of SW26010 show that swStructTRSV can achieve an average speedup of over 30 in the sequential version. swStructTRSV on SW26010 achieves solving speedups of 2.2 and 6.3 over the fast STRSV (fSpTRSV) previously implemented on SW26010 and MKL on Intel Xeon Gold 6132, respectively. swStructTRSV significantly outperforms cuSparse on NVIDIA TITAN RTX in terms of overall execution time. [ABSTRACT FROM AUTHOR]

The results of a parallel implementation of a randomized vector algorithm for solving systems of linear equations are presented in the paper. The solution is represented in the form of a Neumann series. The stochastic method computes this series by sampling only random columns, avoiding multiplication of matrix by matrix and matrix by vector. We consider the case when the matrix is too large to fit in random-access memory (RAM). We use two approaches to solve this problem. In the first approach, the matrix is divided into parts that are distributed among MPI processes and stored in the available RAM of the cluster nodes. In the second approach, the entire matrix is stored on each node's hard drive, loaded into RAM, and processed in parts. Independent Monte Carlo experiments for random column indices are distributed among MPI processes or OpenMP threads for both approaches to matrix storage. The efficiency of parallel implementations is analyzed. Results are given for a system governed by dense matrices of size 10 4 and 10 5 . [ABSTRACT FROM AUTHOR]

Fault diagnosis of interconnection networks is vital to ensuring the reliability and maintenance of multiprocessor systems. Based on graph theory, diagnostic algorithms for solving the problem of fault diagnosis in interconnection networks have been widely studied. As the number of processors in multiprocessor systems has increased in recent years, fault diagnosis algorithms based on graph theory cannot meet the current diagnosis requirements of some interconnection networks, such as t-diagnosable systems. In this paper, we use the evolution diagnosis approach to study fault diagnosis in t-diagnosable systems under the PMC model. First, for a t-diagnosable system G with a syndrome σ , we use a simple centralized algorithm to generate its simplified diagnosis graph G f , which contains all the faulty nodes in G. Based on the graph, we prove that the faulty node set in G is the minimum cover set of G f . Next, we prove that the problem of computing the minimum cover set for G f is equivalent to the problem of computing the optimal solution of a zero-one integer program. Using this result, we propose a novel genetic algorithm to solve the problem of the zero-one integer program. The simulation results show that the diagnostic accuracy of our proposed algorithm is equal to or greater than 96% and that the proposed algorithm outperforms its competitors in terms of diagnostic accuracy, number of iterations and running time. [ABSTRACT FROM AUTHOR]

In this paper, a novel parallel algorithm is proposed to solve the problems of heavy computation and long simulation time in the field of compressible flows. In this algorithm, a third-order upwind scheme and a fourth-order central difference scheme are employed, with a third-order Runge-Kutta method for time stepping. Considering the powerful floating-point computing ability of the Graphics Processing Unit (GPU), this paper establishes the algorithm on the basis of GPU. Moreover, the direct numerical simulation method is adopted in this algorithm to improve the solution accuracy of the simulation results. To further enhance the efficiency of the algorithm, several optimization strategies are explored in the design of the algorithm as well. Both accuracy and feasibility of the algorithm are verified by a classical two-dimensional example. Compared with solving this example on the Central Processing Unit platform, the experimental results demonstrate that the maximum speedup ratio achieved by our approach is 18.03 times. [ABSTRACT FROM AUTHOR]

The Jacobi iterative algorithm has the characteristic of low computational load, and multiple components of the solution can be solved independently. This paper applies these characteristics to the ternary optical computer, which can be used for parallel optimization because it has a large number of data bits and reconfigurable processor bits. Therefore, a new parallel design scheme is constructed to solve the problem of slow efficiency in solving large linear equations. And the elaborate experiment is used to verify. The experimental method is to simulate the calculation on the ternary optical computer experimental platform. Then, the resource consumption is numerically calculated and summarized to measure the feasibility of the parallel design. Eventually, the results show that the parallel design has obvious advantages in computing speed. The Jacobi iterative algorithm is optimized in parallel on ternary optical processor for the first time. There are two parallel highlights of the scheme. First, the n components are calculated in full parallel. Second, the modified signed-digit (MSD) multiplier based on the minimum module and one-step MSD adder are used to calculate each component to eliminate the impact of large amount of data on calculation time. The research provides a new method for fast solution of large linear equations. [ABSTRACT FROM AUTHOR]

The partitioning-based k-means clustering is one of the most important clustering algorithms. However, in big data environment, it faces the problems of random selection of initial cluster centers randomly, expensive communication overhead among MapReduce nodes and data skewing in data partitions, and others. To solve these problems, this paper proposes a parallel clustering algorithm based on grid density and local sensitive hash function (MR-PGDLSH) which takes into account the advantages of MapReduce and LSH (locality sensitive hash function). In the MR-PGDLSH, firstly the GDS (grid density strategy) is designed to obtain the relatively reasonable initial cluster centers. Then, a DP-LSH (data partition based on locality sensitive hash function) is proposed to divide the data set into multiple segments. The relevant data objects are mapped to the same sub-data set. The similarity function is designed to generate clusters, thereby reducing frequent communication overhead between nodes. Next, the AGS (adaptive grouping strategy) is applied to distribute the amount of data on each node evenly, which solves the problem of data skew on the node. Finally, the MR-PGDLSH is applied to mine the cluster centers in parallel, which obtains the final clustering results. Both theoretical analysis and experimental results have shown that the MR-PGDLSH is superior to the existing clustering algorithms. [ABSTRACT FROM AUTHOR]

The present study proposes a novel method based on evolutionary and fuzzy approaches for unifying two-level perfect nested loops. In this method, the Shuffled Frog Leaping Algorithm (SFLA) is used for achieving optimal answers, and simultaneously, three critical factors are applied as an input in determining basic dependence vectors. The use of fuzzy logic versus fixed coefficients for these three factors has led to the creation of optimal results with high variability and has solved the problem regarding the existence of the main vectors. In addition, the present algorithm has been proposed for many input data so that it can be used in parallel compilers automatically and with low complexity. After implementing and evaluating the proposed method, it was found that, compared to other existing methods, the results achieved were very close to optimal, in the least time, and with the lowest Dependence Cone Size (DCS) and highest number of input vectors. [ABSTRACT FROM AUTHOR]

In this paper, we present Para Miner which is a generic and parallel algorithm for closed pattern mining. Para Miner is built on the principles of pattern enumeration in strongly accessible set systems. Its efficiency is due to a novel dataset reduction technique (that we call EL-reduction), combined with novel technique for performing dataset reduction in a parallel execution on a multi-core architecture. We illustrate Para Miner's genericity by using this algorithm to solve three different pattern mining problems: the frequent itemset mining problem, the mining frequent connected relational graphs problem and the mining gradual itemsets problem. In this paper, we prove the soundness and the completeness of Para Miner. Furthermore, our experiments show that despite being a generic algorithm, Para Miner can compete with specialized state of the art algorithms designed for the pattern mining problems mentioned above. Besides, for the particular problem of gradual itemset mining, Para Miner outperforms the state of the art algorithm by two orders of magnitude. [ABSTRACT FROM AUTHOR]

We suggest in this paper a parallel implementation of cellular automation and global random walk algorithms for solving drift–diffusion–recombination problems which in contrast to the classical random walk on spheres (RWS) methods calculate the solution in any desired family of m prescribed points. The method uses only N trajectories in contrast to mN trajectories in the conventional RWS algorithm. The idea is based on the adjoint symmetry property of the Green function and a double randomization approach. The synchronous multi-particle cellular automaton model of drift–diffusion–recombination is based on known cellular automata. The global RWS and cellular automaton models are tested against the exact solutions of the equation. The accuracy and computer time of both algorithms is analyzed. Parallel codes for the Monte Carlo and cellular automaton algorithms are implemented. The domain decomposition method is employed for the cellular automaton parallel implementation. The global random walk algorithm is parallelized by the Monte Carlo trajectories distribution among the cluster cores. The efficiency of the parallel codes is studied. [ABSTRACT FROM AUTHOR]

The longest path problem is the problem of finding a simple path with the maximum number of vertices in a given graph, and so far it has been solved polynomially only for a few classes of graphs. This problem generalizes the well-known Hamiltonian path problem, hence it is NP-hard in general graphs. In this paper, first we give a sequential linear-time algorithm for the longest path problem in meshes. Then based on this algorithm, we present a constant-time parallel algorithm for the problem, which can be run on every parallel machine. [ABSTRACT FROM AUTHOR]

Metaheuristics have been successfully applied to solve different types of numerical and combinatorial optimization problems. However, they often lose their effectiveness and advantages when applied to large and complex problems. Moreover, the contributions of metaheuristics that deal with high dimensional problems are still very limited compared with low and middle dimensional problems. In this paper, Tabu Search algorithm based on variable partitioning is proposed for solving high dimensional problems. Specifically, multi-level neighborhood structures are constructed by partitioning the variables into small groups. Some of these groups are selected and the neighborhood of their variables are explored. The computational results shown later indicate that exploring the neighborhood of all variables at the same time, even for structured neighborhood, can badly effect the progress of the search. However, exploring the neighborhood gradually through smaller number of variables can give better results. The variable partitioning mechanism used in the proposed method can allow the search process to explore the region around the current iterate solution more precisely. Actually, this partitioning mechanism works as dimensional reduction mechanism. For high dimensional problems, extensive computational studies are carried out to evaluate the performance of newly proposed algorithm on large number of benchmark functions. The results show that the proposed method is promising and produces high quality solutions within low computational costs. [ABSTRACT FROM AUTHOR]

Multi-field coupled three-dimensional phase field model is a research hotspot for simulating the growth mechanism of solidification microstructure. Single-CPU has become the main bottleneck in the development of phase field model due to its computing power and storage limitations. Based on this, the PF-LBM model of three-dimensional dendritic growth of supercooled pure substance solution under flow is established in this study. We take the succinonitrile (SCN) as the research object and use OpenCL heterogeneous parallel technology to realize the parallel solution. Firstly, the performance of the phase field model solution process is analyzed to find out the time-consuming modules; then, a parallel solution is proposed, and the preliminary parallelization of the phase field model is realized; finally, the problems existing in the parallel algorithm are optimized. The experimental results show that compared with the serial algorithm on the CPU platform under the same conditions, the speedup ratio reaches the highest when the computing scale is 128*128*128, which is 62.10. And the average speedup is 57.46. The scale after parallel is also expanded from 189*189*189 to 357*357*357, which effectively solves the problems existing in single-CPU simulation. [ABSTRACT FROM AUTHOR]