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Academic literature on the topic 'Sparse Matrix Storage Formats'

Author: Grafiati

Published: 6 September 2023

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Journal articles on the topic "Sparse Matrix Storage Formats"

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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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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