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Artykuły w czasopismach na temat „Sparse Matrix Storage Formats”

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Data publikacji: 6 września 2023

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1

Langr, Daniel, and Pavel Tvrdik. "Evaluation Criteria for Sparse Matrix Storage Formats." IEEE Transactions on Parallel and Distributed Systems 27, no. 2 (2016): 428–40. http://dx.doi.org/10.1109/tpds.2015.2401575.

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MUKADDES, ABUL MUKID MOHAMMAD, MASAO OGINO, and RYUJI SHIOYA. "PERFORMANCE EVALUATION OF DOMAIN DECOMPOSITION METHOD WITH SPARSE MATRIX STORAGE SCHEMES IN MODERN SUPERCOMPUTER." International Journal of Computational Methods 11, supp01 (2014): 1344007. http://dx.doi.org/10.1142/s0219876213440076.

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The use of proper data structures with corresponding algorithms is critical to achieve good performance in scientific computing. The need of sparse matrix vector multiplication in each iteration of the iterative domain decomposition method has led to implementation of a variety of sparse matrix storage formats. Many storage formats have been presented to represent sparse matrix and integrated in the method. In this paper, the storage efficiency of those sparse matrix storage formats are evaluated and compared. The performance results of sparse matrix vector multiplication used in the domain de

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Chen, Shizhao, Jianbin Fang, Chuanfu Xu, and Zheng Wang. "Adaptive Hybrid Storage Format for Sparse Matrix–Vector Multiplication on Multi-Core SIMD CPUs." Applied Sciences 12, no. 19 (2022): 9812. http://dx.doi.org/10.3390/app12199812.

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Optimizing sparse matrix–vector multiplication (SpMV) is challenging due to the non-uniform distribution of the non-zero elements of the sparse matrix. The best-performing SpMV format changes depending on the input matrix and the underlying architecture, and there is no “one-size-fit-for-all” format. A hybrid scheme combining multiple SpMV storage formats allows one to choose an appropriate format to use for the target matrix and hardware. However, existing hybrid approaches are inadequate for utilizing the SIMD cores of modern multi-core CPUs with SIMDs, and it remains unclear how to best mix

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Sanderson, Conrad, and Ryan Curtin. "Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation." Mathematical and Computational Applications 24, no. 3 (2019): 70. http://dx.doi.org/10.3390/mca24030070.

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Despite the importance of sparse matrices in numerous fields of science, software implementations remain difficult to use for non-expert users, generally requiring the understanding of the underlying details of the chosen sparse matrix storage format. In addition, to achieve good performance, several formats may need to be used in one program, requiring explicit selection and conversion between the formats. This can be both tedious and error-prone, especially for non-expert users. Motivated by these issues, we present a user-friendly and open-source sparse matrix class for the C++ language, wi

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FRAGUELA, BASILIO B., RAMÓN DOALLO, and EMILIO L. ZAPATA. "MEMORY HIERARCHY PERFORMANCE PREDICTION FOR BLOCKED SPARSE ALGORITHMS." Parallel Processing Letters 09, no. 03 (1999): 347–60. http://dx.doi.org/10.1142/s0129626499000323.

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Nowadays the performance gap between processors and main memory makes an efficient usage of the memory hierarchy necessary for good program performance. Several techniques have been proposed for this purpose. Nevertheless most of them consider only regular access patterns, while many scientific and numerical applications give place to irregular patterns. A typical case is that of indirect accesses due to the use of compressed storage formats for sparse matrices. This paper describes an analytic approach to model both regular and irregular access patterns. The application modeled is an optimize

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Smith, Barry F., and William D. Gropp. "The Design of Data-Structure-Neutral Libraries for the Iterative Solution of Sparse Linear Systems." Scientific Programming 5, no. 4 (1996): 329–36. http://dx.doi.org/10.1155/1996/417629.

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Over the past few years several proposals have been made for the standardization of sparse matrix storage formats in order to allow for the development of portable matrix libraries for the iterative solution of linear systems. We believe that this is the wrong approach. Rather than define one standard (or a small number of standards) for matrix storage, the community should define an interface (i.e., the calling sequences) for the functions that act on the data. In addition, we cannot ignore the interface to the vector operations because, in many applications, vectors may not be stored as cons

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Guo, Dahai, and William Gropp. "Applications of the streamed storage format for sparse matrix operations." International Journal of High Performance Computing Applications 28, no. 1 (2013): 3–12. http://dx.doi.org/10.1177/1094342012470469.

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Akhunov, R. R., S. P. Kuksenko, V. K. Salov, and T. R. Gazizov. "Sparse matrix storage formats and acceleration of iterative solution of linear algebraic systems with dense matrices." Journal of Mathematical Sciences 191, no. 1 (2013): 10–18. http://dx.doi.org/10.1007/s10958-013-1296-7.

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Merrill, Duane, and Michael Garland. "Merge-based sparse matrix-vector multiplication (SpMV) using the CSR storage format." ACM SIGPLAN Notices 51, no. 8 (2016): 1–2. http://dx.doi.org/10.1145/3016078.2851190.

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Zhang, Jilin, Jian Wan, Fangfang Li, et al. "Efficient sparse matrix–vector multiplication using cache oblivious extension quadtree storage format." Future Generation Computer Systems 54 (January 2016): 490–500. http://dx.doi.org/10.1016/j.future.2015.03.005.

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Smith, Barry, and Hong Zhang. "Sparse triangular solves for ILU revisited: data layout crucial to better performance." International Journal of High Performance Computing Applications 25, no. 4 (2010): 386–91. http://dx.doi.org/10.1177/1094342010389857.

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A key to good processor utilization for sparse matrix computations is storing the data in the format that is most conducive to fast access by the memory system. In particular, for sparse matrix triangular solves the traditional compressed sparse matrix format is poor, and minor adjustments to the data structure can increase the processor utilization dramatically. Such adjustments involve storing the L and U factors separately and storing the U rows ‘backwards' so that they are accessed in a simple streaming fashion during the triangular solves. Changes to the PETSc libraries to use this modifi

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Ji, Guo Liang, Yang De Feng, Wen Kai Cui, and Liang Gang Lu. "Implementation Procedures of Parallel Preconditioning with Sparse Matrix Based on FEM." Applied Mechanics and Materials 166-169 (May 2012): 3166–73. http://dx.doi.org/10.4028/www.scientific.net/amm.166-169.3166.

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A technique to assemble global stiffness matrix stored in sparse storage format and two parallel solvers for sparse linear systems based on FEM are presented. The assembly method uses a data structure named associated node at intermediate stages to finally arrive at the Compressed Sparse Row (CSR) format. The associated nodes record the information about the connection of nodes in the mesh. The technique can reduce large memory because it only stores the nonzero elements of the global stiffness matrix. This method is simple and effective. The solvers are Restarted GMRES iterative solvers with

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13

Oganesyan, P. A., and O. O. Shtein. "Implementation of Basic Operations for Sparse Matrices when Solving a Generalized Eigenvalue Problem in the ACELAN-COMPOS Complex." Advanced Engineering Research 23, no. 2 (2023): 121–29. http://dx.doi.org/10.23947/2687-1653-2023-23-2-121-129.

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Introduction. The widespread use of piezoelectric materials in various industries stimulates the study of their physical characteristics and determines the urgency of such research. In this case, modal analysis makes it possible to determine the operating frequency and the coefficient of electromechanical coupling of piezoelectric elements of various devices. These indicators are of serious theoretical and applied interest. The study was aimed at the development of numerical methods for solving the problem of determining resonance frequencies in a system of elastic bodies. To achieve this goal

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Bramas, Bérenger, and Pavel Kus. "Computing the sparse matrix vector product using block-based kernels without zero padding on processors with AVX-512 instructions." PeerJ Computer Science 4 (April 30, 2018): e151. http://dx.doi.org/10.7717/peerj-cs.151.

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The sparse matrix-vector product (SpMV) is a fundamental operation in many scientific applications from various fields. The High Performance Computing (HPC) community has therefore continuously invested a lot of effort to provide an efficient SpMV kernel on modern CPU architectures. Although it has been shown that block-based kernels help to achieve high performance, they are difficult to use in practice because of the zero padding they require. In the current paper, we propose new kernels using the AVX-512 instruction set, which makes it possible to use a blocking scheme without any zero padd

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Mahmoud, Mohammed, Mark Hoffmann, and Hassan Reza. "Developing a New Storage Format and a Warp-Based SpMV Kernel for Configuration Interaction Sparse Matrices on the GPU." Computation 6, no. 3 (2018): 45. http://dx.doi.org/10.3390/computation6030045.

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Sparse matrix-vector multiplication (SpMV) can be used to solve diverse-scaled linear systems and eigenvalue problems that exist in numerous, and varying scientific applications. One of the scientific applications that SpMV is involved in is known as Configuration Interaction (CI). CI is a linear method for solving the nonrelativistic Schrödinger equation for quantum chemical multi-electron systems, and it can deal with the ground state as well as multiple excited states. In this paper, we have developed a hybrid approach in order to deal with CI sparse matrices. The proposed model includes a

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Mohammed, Saira Banu Jamal, M. Rajasekhara Babu, and Sumithra Sriram. "GPU Implementation of Image Convolution Using Sparse Model with Efficient Storage Format." International Journal of Grid and High Performance Computing 10, no. 1 (2018): 54–70. http://dx.doi.org/10.4018/ijghpc.2018010104.

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With the growth of data parallel computing, role of GPU computing in non-graphic applications such as image processing becomes a focus in research fields. Convolution is an integral operation in filtering, smoothing and edge detection. In this article, the process of convolution is realized as a sparse linear system and is solved using Sparse Matrix Vector Multiplication (SpMV). The Compressed Sparse Row (CSR) format of SPMV shows better CPU performance compared to normal convolution. To overcome the stalling of threads for short rows in the GPU implementation of CSR SpMV, a more efficient mod

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Zhang, Jianfei, and Lei Zhang. "Efficient CUDA Polynomial Preconditioned Conjugate Gradient Solver for Finite Element Computation of Elasticity Problems." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/398438.

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Graphics processing unit (GPU) has obtained great success in scientific computations for its tremendous computational horsepower and very high memory bandwidth. This paper discusses the efficient way to implement polynomial preconditioned conjugate gradient solver for the finite element computation of elasticity on NVIDIA GPUs using compute unified device architecture (CUDA). Sliced block ELLPACK (SBELL) format is introduced to store sparse matrix arising from finite element discretization of elasticity with fewer padding zeros than traditional ELLPACK-based formats. Polynomial preconditioning

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Li, Yishui, Peizhen Xie, Xinhai Chen, et al. "VBSF: a new storage format for SIMD sparse matrix–vector multiplication on modern processors." Journal of Supercomputing 76, no. 3 (2019): 2063–81. http://dx.doi.org/10.1007/s11227-019-02835-4.

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Boo, Hee-Hyung, and Sung-Ho Kim. "Two dimensional variable-length vector storage format for efficient storage of sparse matrix in the finite element method." Journal of the Korea Society of Computer and Information 17, no. 9 (2012): 9–16. http://dx.doi.org/10.9708/jksci/2012.17.9.009.

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AlAhmadi, Sarah, Thaha Mohammed, Aiiad Albeshri, Iyad Katib, and Rashid Mehmood. "Performance Analysis of Sparse Matrix-Vector Multiplication (SpMV) on Graphics Processing Units (GPUs)." Electronics 9, no. 10 (2020): 1675. http://dx.doi.org/10.3390/electronics9101675.

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Graphics processing units (GPUs) have delivered a remarkable performance for a variety of high performance computing (HPC) applications through massive parallelism. One such application is sparse matrix-vector (SpMV) computations, which is central to many scientific, engineering, and other applications including machine learning. No single SpMV storage or computation scheme provides consistent and sufficiently high performance for all matrices due to their varying sparsity patterns. An extensive literature review reveals that the performance of SpMV techniques on GPUs has not been studied in s

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Ahmed, Muhammad, Sardar Usman, Nehad Ali Shah, et al. "AAQAL: A Machine Learning-Based Tool for Performance Optimization of Parallel SPMV Computations Using Block CSR." Applied Sciences 12, no. 14 (2022): 7073. http://dx.doi.org/10.3390/app12147073.

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The sparse matrix–vector product (SpMV), considered one of the seven dwarfs (numerical methods of significance), is essential in high-performance real-world scientific and analytical applications requiring solution of large sparse linear equation systems, where SpMV is a key computing operation. As the sparsity patterns of sparse matrices are unknown before runtime, we used machine learning-based performance optimization of the SpMV kernel by exploiting the structure of the sparse matrices using the Block Compressed Sparse Row (BCSR) storage format. As the structure of sparse matrices varies a

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Moussaoui, Mohammed Lamine, Abderrahmane Kibboua, and Mohamed Chabaat. "Contribution to Bridge Damage Analysis." Applied Mechanics and Materials 704 (December 2014): 435–41. http://dx.doi.org/10.4028/www.scientific.net/amm.704.435.

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Structural damage detection has become an important research area since several works [2] were focused on the crack zones detection in order to foresee the appropriate solutions. The present research aims to carry out the reinforced concrete bridge damage detection with the finite element mathematical model updating method (MMUM). Unknown degrees of freedom dof are expanded from measured ones. The partitioned system of equations has provided a large sub-system of equations which can be solved efficiently by handling sparse matrix algorithms at each time step of the finite time centered space F

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Zeng, Guangsen, and Yi Zou. "Leveraging Memory Copy Overlap for Efficient Sparse Matrix-Vector Multiplication on GPUs." Electronics 12, no. 17 (2023): 3687. http://dx.doi.org/10.3390/electronics12173687.

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Sparse matrix-vector multiplication (SpMV) is central to many scientific, engineering, and other applications, including machine learning. Compressed Sparse Row (CSR) is a widely used sparse matrix storage format. SpMV using the CSR format on GPU computing platforms is widely studied, where the access behavior of GPU is often the performance bottleneck. The Ampere GPU architecture recently from NVIDIA provides a new asynchronous memory copy instruction, memcpy_async, for more efficient data movement in shared memory. Leveraging the capability of this new memcpy_async instruction, we first prop

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Gao, Jiaquan, Yuanshen Zhou, and Kesong Wu. "A Novel Multi-GPU Parallel Optimization Model for The Sparse Matrix-Vector Multiplication." Parallel Processing Letters 26, no. 04 (2016): 1640001. http://dx.doi.org/10.1142/s0129626416400016.

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Accelerating the sparse matrix-vector multiplication (SpMV) on the graphics processing units (GPUs) has attracted considerable attention recently. We observe that on a specific multiple-GPU platform, the SpMV performance can usually be greatly improved when a matrix is partitioned into several blocks according to a predetermined rule and each block is assigned to a GPU with an appropriate storage format. This motivates us to propose a novel multi-GPU parallel SpMV optimization model. Our model involves two stages. In the first stage, a simple rule is defined to divide any given matrix among mu

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Huang, Lan, Jia Zeng, Shiqi Sun, Wencong Wang, Yan Wang, and Kangping Wang. "Coarse-Grained Pruning of Neural Network Models Based on Blocky Sparse Structure." Entropy 23, no. 8 (2021): 1042. http://dx.doi.org/10.3390/e23081042.

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Deep neural networks may achieve excellent performance in many research fields. However, many deep neural network models are over-parameterized. The computation of weight matrices often consumes a lot of time, which requires plenty of computing resources. In order to solve these problems, a novel block-based division method and a special coarse-grained block pruning strategy are proposed in this paper to simplify and compress the fully connected structure, and the pruned weight matrices with a blocky structure are then stored in the format of Block Sparse Row (BSR) to accelerate the calculatio

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Liu, Guangwei, Zhen Hao, Zheng Niu, Ka Mu, Jixian Ma, and Wenzhe Zhang. "Lossless Compression and Optimization Method of Data Flow Efficiency of Infrared Image Depth Learning Model of Substation Equipment." Journal of Physics: Conference Series 2320, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2320/1/012026.

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Abstract Infrared detection of substation equipment has become one of the important means for live detection of power grid equipment. The insulation fault and equipment defect of electrical equipment in operation can be found by using infrared thermal imaging technology. Infrared image deep learning model usually needs a very large computational cost and storage space, which greatly limits the application of deep learning model in embedded terminal. In order to apply the deep learning model well in embedded devices with limited resources, this paper proposes a lossless compression method of in

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Zhang, Yi, Zebin Wu, Jin Sun, et al. "A Distributed Parallel Algorithm Based on Low-Rank and Sparse Representation for Anomaly Detection in Hyperspectral Images." Sensors 18, no. 11 (2018): 3627. http://dx.doi.org/10.3390/s18113627.

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Anomaly detection aims to separate anomalous pixels from the background, and has become an important application of remotely sensed hyperspectral image processing. Anomaly detection methods based on low-rank and sparse representation (LRASR) can accurately detect anomalous pixels. However, with the significant volume increase of hyperspectral image repositories, such techniques consume a significant amount of time (mainly due to the massive amount of matrix computations involved). In this paper, we propose a novel distributed parallel algorithm (DPA) by redesigning key operators of LRASR in te

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Benatia, Akrem, Weixing Ji, Yizhuo Wang, and Feng Shi. "Sparse matrix partitioning for optimizing SpMV on CPU-GPU heterogeneous platforms." International Journal of High Performance Computing Applications 34, no. 1 (2019): 66–80. http://dx.doi.org/10.1177/1094342019886628.

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Sparse matrix–vector multiplication (SpMV) kernel dominates the computing cost in numerous applications. Most of the existing studies dedicated to improving this kernel have been targeting just one type of processing units, mainly multicore CPUs or graphics processing units (GPUs), and have not explored the potential of the recent, rapidly emerging, CPU-GPU heterogeneous platforms. To take full advantage of these heterogeneous systems, the input sparse matrix has to be partitioned on different available processing units. The partitioning problem is more challenging with the existence of many s

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Langr, Daniel, and Ivan Šimeček. "Analysis of Memory Footprints of Sparse Matrices Partitioned Into Uniformly-Sized Blocks." Scalable Computing: Practice and Experience 19, no. 3 (2018): 275–92. http://dx.doi.org/10.12694/scpe.v19i3.1358.

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The presented study analyses memory footprints of 563 representative benchmark sparse matrices with respect to their partitioning into uniformly-sized blocks. Different block sizes and different ways of storing blocks in memory are considered and statistically evaluated. Memory footprints of partitioned matrices are then compared with their lower bounds and CSR, index-compressed CSR, and EBF storage formats. The results show that blocking-based storage formats may significantly reduce memory footprints of sparse matrices arising from a wide range of application domains. Additionally, measured

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., V. Kabeer. "SPARSE MATRIX STORAGE USING DECIMAL CODING." International Journal of Research in Engineering and Technology 05, no. 34 (2016): 12–15. http://dx.doi.org/10.15623/ijret.2016.0534003.

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Bani-Ismail, Basel, and Ghassan Kanaan. "Comparing Different Sparse Matrix Storage Structures as Index Structure for Arabic Text Collection." International Journal of Information Retrieval Research 2, no. 2 (2012): 52–67. http://dx.doi.org/10.4018/ijirr.2012040105.

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In the authors’ study they evaluate and compare the storage efficiency of different sparse matrix storage structures as index structure for Arabic text collection and their corresponding sparse matrix-vector multiplication algorithms to perform query processing in any Information Retrieval (IR) system. The study covers six sparse matrix storage structures including the Coordinate Storage (COO), Compressed Sparse Row (CSR), Compressed Sparse Column (CSC), Block Coordinate (BCO), Block Sparse Row (BSR), and Block Sparse Column (BSC). Evaluation depends on the storage space requirements for each

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Wang, Ying, and Korhan Cengiz. "Implementation of the Spark technique in a matrix distributed computing algorithm." Journal of Intelligent Systems 31, no. 1 (2022): 660–71. http://dx.doi.org/10.1515/jisys-2022-0051.

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Abstract Two analyzes of Spark engine performance strategies to implement the Spark technique in a matrix distributed computational algorithm, the multiplication of a sparse multiplication operational test model. The dimensions of the two input sparse matrices have been fixed to 30,000 × 30,000, and the density of the input matrix have been changed. The experimental results show that when the density reaches about 0.3, the original dense matrix multiplication performance can outperform the sparse-sparse matrix multiplication, which is basically consistent with the relationship between the spar

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Et.al, Sudha Hanumanthu. "Universal Measurement Matrix Design for Sparse and Co-Sparse Signal Recovery." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 6 (2021): 404–11. http://dx.doi.org/10.17762/turcomat.v12i6.1407.

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Compressed Sensing (CS) avails mutual coherence metric to choose the measurement matrix that is incoherent with dictionary matrix. Random measurement matrices are incoherent with any dictionary, but their highly uncertain elements necessitate large storage and make hardware realization difficult. In this paper deterministic matrices are employed which greatly reduce memory space and computational complexity. To avoid the randomness completely, deterministic sub-sampling is done by choosing rows deterministically rather than randomly, so that matrix can be regenerated during reconstruction with

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Grasedyck, Lars, and Wolfgang Hackbusch. "An Introduction to Hierarchical (H-) Rank and TT-Rank of Tensors with Examples." Computational Methods in Applied Mathematics 11, no. 3 (2011): 291–304. http://dx.doi.org/10.2478/cmam-2011-0016.

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Abstract We review two similar concepts of hierarchical rank of tensors (which extend the matrix rank to higher order tensors): the TT-rank and the H-rank (hierarchical or H-Tucker rank). Based on this notion of rank, one can define a data-sparse representation of tensors involving O(dnk + dk^3) data for order d tensors with mode sizes n and rank k. Simple examples underline the differences and similarities between the different formats and ranks. Finally, we derive rank bounds for tensors in one of the formats based on the ranks in the other format.

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Song, Qi, Pu Chen, and Shuli Sun. "Partial Refactorization in Sparse Matrix Solution: A New Possibility for Faster Nonlinear Finite Element Analysis." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/403912.

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This paper proposes a partial refactorization for faster nonlinear analysis based on sparse matrix solution, which is nowadays the default solution choice in finite element analysis and can solve finite element models up to millions degrees of freedom. Among various fill-in’s reducing strategies for sparse matrix solution, the graph partition is in general the best in terms of resultant fill-ins and floating-point operations and furthermore produces a particular graph of sparse matrix that prevents local change of entries from wide spreading in factorization. Based on this feature, an explicit

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Liu, Sheng, Yasong Cao, and Shuwei Sun. "Mapping and Optimization Method of SpMV on Multi-DSP Accelerator." Electronics 11, no. 22 (2022): 3699. http://dx.doi.org/10.3390/electronics11223699.

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Sparse matrix-vector multiplication (SpMV) solves the product of a sparse matrix and dense vector, and the sparseness of a sparse matrix is often more than 90%. Usually, the sparse matrix is compressed to save storage resources, but this causes irregular access to dense vectors in the algorithm, which takes a lot of time and degrades the SpMV performance of the system. In this study, we design a dedicated channel in the DMA to implement an indirect memory access process to speed up the SpMV operation. On this basis, we propose six SpMV algorithm schemes and map them to optimize the performance

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Mukaddes, Abul Mukid Mohammad, Masao Ogino, Ryuji Shioya, and Hiroshi Kanayama. "Treatment of Block-Based Sparse Matrices in Domain Decomposition Method." International Journal of System Modeling and Simulation 2, no. 1 (2017): 1. http://dx.doi.org/10.24178/ijsms.2017.2.1.01.

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Abstract— The domain decomposition method involves the finite element solution of problems in the parallel computer. The finite element discretization leads to the solution of large systems of linear equation whose matrix is naturally sparse. The use of proper storing techniques for sparse matrix is fundamental especially when dealing with large scale problems typical of industrial applications. The aim of this research is to review the sparsity pattern of the matrices originating from the discretization of the elasto-plastic and thermal-convection problems. Some practical strategies dealing w

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Zhou, Huilin, Youwen Liu, Yuhao Wang, Liangbing Chen, and Rongxing Duan. "Nonlinear Electromagnetic Inverse Scattering Imaging Based on IN-LSQR." International Journal of Antennas and Propagation 2018 (August 2, 2018): 1–9. http://dx.doi.org/10.1155/2018/2794646.

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A nonlinear inversion scheme is proposed for electromagnetic inverse scattering imaging. It exploits inexact Newton (IN) and least square QR factorization (LSQR) methods to tackle the nonlinearity and ill-posedness of the electromagnetic inverse scattering problem. A nonlinear model of the inverse scattering in functional form is developed. At every IN iteration, the sparse storage method is adopted to solve the storage and computational bottleneck of Fréchet derivative matrix, a large-scale sparse Jacobian matrix. Moreover, to address the slow convergence problem encountered in the inexact Ne

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Zhang, Xu Dong, Jian Ye Yuan, Jing Ping Zhang, and Jian Ying Feng. "Two-Port Characteristic Analysis for Transformers with the Large Scale Windings Based on Sparse Matrix." Advanced Materials Research 986-987 (July 2014): 2035–38. http://dx.doi.org/10.4028/www.scientific.net/amr.986-987.2035.

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In order to calculate the two-port wide frequency parameters of a large transformer quickly and accurately, parameter calculation of multi-conductor transmission lines model is proposed based on the sparse matrix operation. It solved the problem caused by the large matrix. Firstly, multi-conductor transmission lines model of the transformer windings is established. Secondly, matrix characteristics ofYandZis analyzed, based on which the block storage and computation method is applied. Then the frequency-domain model is solved based on sparse matrix operation. At last, taking a SS11-20000/110 tr

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CUI, Hang, Shoichi HIRASAWA, Hiroaki KOBAYASHI, and Hiroyuki TAKIZAWA. "A Machine Learning-Based Approach for Selecting SpMV Kernels and Matrix Storage Formats." IEICE Transactions on Information and Systems E101.D, no. 9 (2018): 2307–14. http://dx.doi.org/10.1587/transinf.2017edp7176.

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Tang, Wai Teng, Wen Jun Tan, Rick Siow Mong Goh, Stephen John Turner, and Weng-Fai Wong. "A Family of Bit-Representation-Optimized Formats for Fast Sparse Matrix-Vector Multiplication on the GPU." IEEE Transactions on Parallel and Distributed Systems 26, no. 9 (2015): 2373–85. http://dx.doi.org/10.1109/tpds.2014.2357437.

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Wang, Pei, Xu Sheng Yang, Zhuo Yuan Wang, Lin Gong Li, Ji Chang He, and Qing Jie Wang. "Solving Large-Scale Asymmetric Sparse Linear Equations Based on SuperLU Algorithm." Advanced Materials Research 230-232 (May 2011): 1355–61. http://dx.doi.org/10.4028/www.scientific.net/amr.230-232.1355.

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This article introduces the recent research of SuperLU algorithms and the optimal storage method for [1] the sparse linear equations of coefficient matrix. How to solve large-scale non-symmetric sparse linear equations by SuperLU algorithm is the key part of this article. The advantage of SuperLU algorithm compared to other algorithms is summarized at last. SuperLU algorithm not only saves memory space, but also reduces the computation time. Because of less storage needed by this algorithm, it could solve equation with larger scale, which is much more useful.

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Lu, Xinmiao, Cunfang Yang, Qiong Wu, et al. "Improved Reconstruction Algorithm of Wireless Sensor Network Based on BFGS Quasi-Newton Method." Electronics 12, no. 6 (2023): 1267. http://dx.doi.org/10.3390/electronics12061267.

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Aiming at the problems of low reconstruction rate and poor reconstruction precision when reconstructing sparse signals in wireless sensor networks, a sparse signal reconstruction algorithm based on the Limit-Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) quasi-Newton method is proposed. The L-BFGS quasi-Newton method uses a two-loop recursion algorithm to find the descent direction dk directly by calculating the step difference between m adjacent iteration points, and a matrix Hk approximating the inverse of the Hessian matrix is constructed. It solves the disadvantages of BFGS requiring the calcul

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Stevanović, Dragoljub, Marko Topalović, and Miroslav Živković. "IMPROVEMENT OF THE SPARSE MATRICES STORAGE ROUTINES FOR LARGE FEM CALCULATIONS." Journal of the Serbian Society for Computational Mechanics 15, no. 1 (2021): 81–97. http://dx.doi.org/10.24874/jsscm.2021.15.01.06.

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Efficient memory handling is one of the key issues that engineers and programmers face in developing software for numerical analysis such as the Finite Element Method. This method operates on huge matrices that have a large number of zero coefficients which waste memory, so it is necessary to save it and to work only with non-zero coefficients using so called "SPARSE" matrices. Analysis of two methods used for the improvement of "SPARSE" matrix creation is presented in this paper and their pseudo code is given. Comparison is made on a wide range of problem sizes. Results show that "indexing" m

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Yuhendri, Muldi, Ahyanuardi Ahyanuardi, and Aswardi Aswardi. "Direct Torque Control Strategy of PMSM Employing Ultra Sparse Matrix Converter." International Journal of Power Electronics and Drive Systems (IJPEDS) 9, no. 1 (2018): 64. http://dx.doi.org/10.11591/ijpeds.v9.i1.pp64-72.

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Matrix converter is a good choice for Permanent Magnet Synchronous Motor (PMSM) drives, because it has high power density and does not require dc-link energy storage. the disadvantages of conventional matrix converter is using 18 active switches, so it becomes expensive and the modulation method becomes more complicated than back to back converter. To minimize this problem, this paper proposes variable speed drive of PMSM using Ultra Sparse Matrix Converter (USMC) based on Direct Torque Control (DTC) methods. This converter uses only 9 active switches, making it cheaper than conventional matri

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Mukaddes, A. M. M., Masao Ogino, and Ryuji Shioya. "403 A Computational Study of Sparse Matrix Storage Schemes in the Domain Decomposition Method." Proceedings of The Computational Mechanics Conference 2012.25 (2012): 95–96. http://dx.doi.org/10.1299/jsmecmd.2012.25.95.

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Fernandes, P., and P. Girdinio. "A new storage scheme for an efficient implementation of the sparse matrix-vector product." Parallel Computing 12, no. 3 (1989): 327–33. http://dx.doi.org/10.1016/0167-8191(89)90090-2.

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Koslowski, T., and W. Von Niessen. "Linear combination of Lanczos vectors: A storage-efficient algorithm for sparse matrix eigenvector computations." Journal of Computational Chemistry 14, no. 7 (1993): 769–74. http://dx.doi.org/10.1002/jcc.540140703.

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Vasco, Don W., John E. Peterson, and Ernest L. Majer. "Resolving seismic anisotropy: Sparse matrix methods for geophysical inverse problems." GEOPHYSICS 63, no. 3 (1998): 970–83. http://dx.doi.org/10.1190/1.1444408.

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Two techniques for the singular value decomposition (SVD) of large sparse matrices are directly applicable to geophysical inverse problems: subspace iteration and the Lanczos method. Both methods require very little in‐core storage and efficiently compute a set of singular values and singular vectors. A comparison of the singular value and vector estimates of these iterative approaches with the results of a conventional in‐core SVD algorithm demonstrates their accuracy. Hence, it is possible to conduct an assessment of inversion results for larger sparse inverse problems such as those arising

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Kai, Hong. "Application of Internet of Things Audio Technology Based on Parallel Storage System in Music Classroom." Advances in Multimedia 2022 (September 14, 2022): 1–12. http://dx.doi.org/10.1155/2022/5883238.

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In order to improve the effect of music classroom teaching, this paper combines the parallel storage system and the Internet of Things audio technology to construct a music education system to improve the effect of music education in colleges and universities. Moreover, this paper expounds a grid-free method for sparsity and parameter estimation from the model and mathematical principles and proposes a grid-free DOA method based on fast reconstruction of the T-matrix. This method is guaranteed to produce sparse parameter estimates. At the same time, the numerical simulation shows that the meth

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