TileSpMVis an open source code that uses a tiled structure to optimize sparse matrix-vector multiplication (SpMV) on GPUs. Paper information Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu and Guangming Tan, "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on ...
摘要: We describe a simple random-sampling based procedure for producing sparse matrix approximations. Our procedure and analysis are extremely simple: the analysis uses nothing more than the Chernoff-HoeffDOI: 10.1007/11830924_26 被引量: 184 ...
A new algorithm for reducing the bandwidth and profile of a sparse matrix is described. Extensive testing on finite element matrices indicates that the algorithm typically produces bandwidth and profile which are comparable to those of the commonly-used reverse Cuthill-McKee algorithm, yet requires sig...
An iterative method is given for solving Ax ~ffi b and minU Ax- b 112, where the matrix A is large and sparse. The method is based on the bidiagonalization procedure of Golub and Kahan. It is analytically equivalent to the standard method of conjugate gradients, but possesses more favorable...
An “industrial strength” algorithm for solving sparse symmetric generalized eigenproblems is described. The algorithm has its foundations in known techni... RG Grimes,JG Lewis,HD Simon - 《Siam Journal on Matrix Analysis & Applications》 被引量: 608发表: 1994年 The Lanczos algorithm with partial...
3、训练集和测试集的输入支持dense matrix 和 sparse matrix,其中sparse matrix采用CSR表示法; 4、对于不平衡数据的处理一般来说从三个方面入手: 1)、对正例和负例赋予不同的C值,例如正例远少于负例,则正例的C值取得较大,这种方法的缺点是可能会偏离原始数据的概率分布; ...
When the matrix is sparse this method works fine because sparse matrices take less time to compute. It is not practically possible as it is computation and theoretical approach only. It takes more space for storing sub matrices. There is less chance of accuracy. ...
turning portions of the sparse matrix into dense blocks and invoking high-performance BLAS/lapack libraries. It is designed with optimization libraries for Levenberg-Marquardt in mind, and aims at reducing part of the complexity offering the best tool for the job. Compared to the library currently...
In their full generality, Good's methods are applicable to certain problems in which one must multiply an N-vector by an N X N matrix which can be factored into m sparse matrices, where m is proportional to log N. This results inma procedure requiring a number of operations proportional ...
Robust Adaptive Beamforming Algorithm for Sparse Array Based on Covariance Matrix Reconstruction Technology When the array structure of the sparse arrays (SA) cannot be determined, the existing beamforming algorithms designed according to specific formations such as coprime arrays (CA), nested arrays (...