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Relevant bibliographies by topics / Compressed Row Storage

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Journal articles

Academic literature on the topic 'Compressed Row Storage'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Compressed Row Storage"

1

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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2

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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3

Yan, Kaizhuang, Yongxian Wang, and Wenbin Xiao. "A New Compression and Storage Method for High-Resolution SSP Data Based-on Dictionary Learning." Journal of Marine Science and Engineering 10, no. 8 (2022): 1095. http://dx.doi.org/10.3390/jmse10081095.

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The sound speed profile data of seawater provide an important basis for carrying out underwater acoustic modeling and analysis, sonar performance evaluation, and underwater acoustic assistant decision-making. The data volume of the high-resolution sound speed profile is vast, and the demand for data storage space is high, which severely limits the analysis and application of the high-resolution sound speed profile data in the field of marine acoustics. This paper uses the dictionary learning method to achieve sparse coding of the high-resolution sound speed profile and uses a compressed sparse

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4

Knopp, T., and A. Weber. "Local System Matrix Compression for Efficient Reconstruction in Magnetic Particle Imaging." Advances in Mathematical Physics 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/472818.

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Magnetic particle imaging (MPI) is a quantitative method for determining the spatial distribution of magnetic nanoparticles, which can be used as tracers for cardiovascular imaging. For reconstructing a spatial map of the particle distribution, the system matrix describing the magnetic particle imaging equation has to be known. Due to the complex dynamic behavior of the magnetic particles, the system matrix is commonly measured in a calibration procedure. In order to speed up the reconstruction process, recently, a matrix compression technique has been proposed that makes use of a basis transf

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5

Christnatalis, Christnatalis, Bachtiar Bachtiar, and Rony Rony. "Comparative Compression of Wavelet Haar Transformation with Discrete Wavelet Transform on Colored Image Compression." JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING 3, no. 2 (2020): 202–9. http://dx.doi.org/10.31289/jite.v3i2.3154.

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In this research, the algorithm used to compress images is using the haar wavelet transformation method and the discrete wavelet transform algorithm. The image compression based on Wavelet Wavelet transform uses a calculation system with decomposition with row direction and decomposition with column direction. While discrete wavelet transform-based image compression, the size of the compressed image produced will be more optimal because some information that is not so useful, not so felt, and not so seen by humans will be eliminated so that humans still assume that the data can still be used e

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6

Tanaka, Teruo, Ryo Otsuka, Akihiro Fujii, Takahiro Katagiri, and Toshiyuki Imamura. "Implementation of D-Spline-Based Incremental Performance Parameter Estimation Method with ppOpen-AT." Scientific Programming 22, no. 4 (2014): 299–307. http://dx.doi.org/10.1155/2014/310879.

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In automatic performance tuning (AT), a primary aim is to optimize performance parameters that are suitable for certain computational environments in ordinary mathematical libraries. For AT, an important issue is to reduce the estimation time required for optimizing performance parameters. To reduce the estimation time, we previously proposed the Incremental Performance Parameter Estimation method (IPPE method). This method estimates optimal performance parameters by inserting suitable sampling points that are based on computational results for a fitting function. As the fitting function, we i

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7

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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8

Zhang, Xi Xi, Yu Jing Jia, and Guang Zhen Cheng. "The Water Sump Cleaning Machine by Vacuum Suction." Applied Mechanics and Materials 201-202 (October 2012): 785–88. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.785.

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This article describes a vacuum water sump cleaning machine which is used to clean up coal mine water sump. Cleaning machine is composed of mechanical structure and electrical control devices. The parts of machine are made up of Walk the flatbed, storage mud tank, vacuum pumps, suction pipe, mud tubes, swing devices, control valves, suction pipe and pressure tracheal. When working, under the function of vacuum pumping, cleaning machine pulls out the vacuum from storage mud tank through the vacuum air feeder. As the vacuum level in the tank is increasing, under the function of atmospheric press

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9

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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10

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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