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Статті в журналах з теми "Sawtooth Compressed Row Storage"
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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 (April 2012): 52–67. http://dx.doi.org/10.4018/ijirr.2012040105.
Повний текст джерелаАнотація:
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 storage structure and the efficiency of the query processing algorithm. The experimental results demonstrate that CSR is more efficient in terms of storage space requirements and query processing time than the other sparse matrix storage structures. The results also show that CSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other point entry storage structures (COO, CSC). The results demonstrate that BSR requires the least amount of disk space and performs the best in terms of query processing time compared with the other block entry storage structures (BCO, BSC).
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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 (January 2018): 54–70. http://dx.doi.org/10.4018/ijghpc.2018010104.
Повний текст джерелаАнотація:
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 model is proposed, which uses the Adaptive-Compressed Row Storage (A-CSR) format to implement the same. Using CSR in the convolution process achieves a 1.45x and a 1.159x increase in speed compared to the normal convolution of image smoothing and edge detection operations, respectively. An average speedup of 2.05x is achieved for image smoothing technique and 1.58x for edge detection technique in GPU platform usig adaptive CSR format.
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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.
Повний текст джерелаАнотація:
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 introduced d-Spline, which is highly adaptable and requires little estimation time. In this paper, we report the implementation of the IPPE method with ppOpen-AT, which is a scripting language (set of directives) with features that reduce the workload of the developers of mathematical libraries that have AT features. To confirm the effectiveness of the IPPE method for the runtime phase AT, we applied the method to sparse matrix–vector multiplication (SpMV), in which the block size of the sparse matrix structure blocked compressed row storage (BCRS) was used for the performance parameter. The results from the experiment show that the cost was negligibly small for AT using the IPPE method in the runtime phase. Moreover, using the obtained optimal value, the execution time for the mathematical library SpMV was reduced by 44% on comparing the compressed row storage and BCRS (block size 8).
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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 (August 10, 2022): 1095. http://dx.doi.org/10.3390/jmse10081095.
Повний текст джерелаАнотація:
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 row method to compress and store the sparse characteristics of the data matrix. The influence of related parameters on the compression rate and recovery data error is analyzed and discussed, as are different scenarios and the difference in compression processing methods. Through comparative experiments, the average error of the sound speed profile data compressed is less than 0.5 m/s, the maximum error is less than 3 m/s, and the data volume is about 10% to 15% of the original data volume. This method significantly reduces the storage capacity of high-resolution sound speed profile data and ensures the accuracy of the data, providing technical support for efficient and convenient access to high-resolution sound speed profiles.
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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.
Повний текст джерелаАнотація:
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 transformation in order to compress the MPI system matrix. By thresholding the resulting matrix and storing the remaining entries in compressed row storage format, only a fraction of the data has to be processed when reconstructing the particle distribution. In the present work, it is shown that the image quality of the algorithm can be considerably improved by using a local threshold for each matrix row instead of a global threshold for the entire system matrix.
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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 (October 13, 2020): 1675. http://dx.doi.org/10.3390/electronics9101675.
Повний текст джерелаАнотація:
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 sufficient detail. In this paper, we provide a detailed performance analysis of SpMV performance on GPUs using four notable sparse matrix storage schemes (compressed sparse row (CSR), ELLAPCK (ELL), hybrid ELL/COO (HYB), and compressed sparse row 5 (CSR5)), five performance metrics (execution time, giga floating point operations per second (GFLOPS), achieved occupancy, instructions per warp, and warp execution efficiency), five matrix sparsity features (nnz, anpr, nprvariance, maxnpr, and distavg), and 17 sparse matrices from 10 application domains (chemical simulations, computational fluid dynamics (CFD), electromagnetics, linear programming, economics, etc.). Subsequently, based on the deeper insights gained through the detailed performance analysis, we propose a technique called the heterogeneous CPU–GPU Hybrid (HCGHYB) scheme. It utilizes both the CPU and GPU in parallel and provides better performance over the HYB format by an average speedup of 1.7x. Heterogeneous computing is an important direction for SpMV and other application areas. Moreover, to the best of our knowledge, this is the first work where the SpMV performance on GPUs has been discussed in such depth. We believe that this work on SpMV performance analysis and the heterogeneous scheme will open up many new directions and improvements for the SpMV computing field in the future.
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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 (January 20, 2020): 202–9. http://dx.doi.org/10.31289/jite.v3i2.3154.
Повний текст джерелаАнотація:
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 even though it is compressed. The data used are data taken directly, so the test results are obtained that digital image compression based on Wavelet Wavelet Transformation gets a compression ratio of 41%, while the discrete wavelet transform reaches 29.5%. Based on research problems regarding the efficiency of storage media, it can be concluded that the right algorithm to choose is the Haar Wavelet transformation algorithm. To improve compression results it is recommended to use wavelet transforms other than haar, such as daubechies, symlets, and so on.
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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.
Повний текст джерелаАнотація:
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 pressure outside world, the mud flows into the reservoir along the suction tube. When storage mud tank is full, vacuum pump automatically shut down. Turning off the vacuum valve and opening the pressure valve, the slime in the tank under the function of compressed air comes into the mine car through the row mud tube. The layout of this cleaning machine is reasonable, what is more, it is flexible and convenient to operate, so that it reduces the labor intensity significantly and improves the work efficiency of the clearance.
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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.
Повний текст джерелаАнотація:
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 Jacobi and sparse appropriate inverse (SPAI) preconditioning, respectively. Some numerical experiments show that the both preconditioners can improve the convergence of the iterative method, and SPAI is more powerful than Jacobi in the sence of reducing the number of iterations and parallel efficiency. Both of the two solvers can be used to solve large sparse linear system.
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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 (August 24, 2018): 45. http://dx.doi.org/10.3390/computation6030045.
Повний текст джерелаАнотація:
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 newly-developed hybrid format for storing CI sparse matrices on the Graphics Processing Unit (GPU). In addition to the new developed format, the proposed model includes the SpMV kernel for multiplying the CI matrix (proposed format) by a vector using the C language and the Compute Unified Device Architecture (CUDA) platform. The proposed SpMV kernel is a vector kernel that uses the warp approach. We have gauged the newly developed model in terms of two primary factors, memory usage and performance. Our proposed kernel was compared to the cuSPARSE library and the CSR5 (Compressed Sparse Row 5) format and already outperformed both.
Дисертації з теми "Sawtooth Compressed Row Storage"
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Ramesh, Chinthala. "Hardware-Software Co-Design Accelerators for Sparse BLAS." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4276.
Повний текст джерелаАнотація:
Sparse Basic Linear Algebra Subroutines (Sparse BLAS) is an important library. Sparse BLAS includes three levels of subroutines. Level 1, Level2 and Level 3 Sparse BLAS routines. Level 1 Sparse BLAS routines do computations over sparse vector and spare/dense vector. Level 2 deals with sparse matrix and vector operations. Level 3 deals with sparse matrix and dense matrix operations. The computations of these Sparse BLAS routines on General Purpose Processors (GPPs) not only suffer from less utilization of hardware resources but also takes more compute time than the workload due to poor data locality of sparse vector/matrix storage formats.
In the literature, tremendous efforts have been put into software to improve these Sparse BLAS routines performance on GPPs. GPPs best suit for applications with high data locality, whereas Sparse BLAS routines operate on applications with less data locality hence, GPPs performance is poor. Various Custom Function Units (Hardware Accelerators) are proposed in the literature and are proved to be efficient than soft wares which tried to accelerate Sparse BLAS subroutines. Though existing hardware accelerators improved the Sparse BLAS performance compared to software Sparse BLAS routines, there is still lot of scope to improve these accelerators.
This thesis describes both the existing software and hardware software co-designs (HW/SW co-design) and identifies the limitations of these existing solutions. We propose a new sparse data representation called Sawtooth Compressed Row Storage (SCRS) and corresponding SpMV and SpMM algorithms. SCRS based SpMV and SpMM are performing better than existing software solutions. Even though SCRS based SpMV and SpMM algorithms perform better than existing solutions, they still could not reach theoretical peak performance.
The knowledge gained from the study of limitations of these existing solutions including the proposed SCRS based SpMV and SpMM is used to propose new HW/SW co-designs. Software accelerators are limited by the hardware properties of GPPs, and GPUs itself, hence, we propose HW/SW co-designs to accelerate few basic Sparse BLAS operations (SpVV and SpMV). Our proposed Parallel Sparse BLAS HW/SW co-design achieves near theoretical peak performance with reasonable hardware resources.
Частини книг з теми "Sawtooth Compressed Row Storage"
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D’Azevedo, Eduardo F., Mark R. Fahey, and Richard T. Mills. "Vectorized Sparse Matrix Multiply for Compressed Row Storage Format." In Lecture Notes in Computer Science, 99–106. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11428831_13.
Повний текст джерела