Optimization techniques for the sparse matrix vector multiplication are adopted in programming.
编程中采用了稀疏矩阵向量相乘的优化技术。
The load-balanced multiplication of a large sparse matrix with vector on workstation cluster is considered.
考虑在工作站机群上实现大型稀疏矩阵和向量乘的负载平衡。
Then, large sequence and sequential patterns are all out pass the vector of matrix multiplication operator corresponding to the elements and simple addition operations have been.
接下来的大序列、序列模式等都是通过矩阵的列向量对应元素的相乘运算和简单的加法运算而得到。
First summarizes the differences on principle between two kinds of parallel algorithm of matrix-vector multiplication, namely, divided by row and divided by column.
文中首先总结按行划分和按列划分的并行矩阵向量乘法在原理上的异同。
It reduces greatly the computational complexity of matrix-vector multiplication in conjugate gradient iteration improves the efficiency of MLFMA while the reasonable accuracy is maintained.
该方法在保证合理计算精度的同时大大降低了迭代过程中矩阵矢量相乘的计算复杂度，提高了多层快速多极子方法计算效率。
The algorithm calculated the wave front slope by a reusing core module manner and complemented the wave front reconstruction with the decomposition of matrix-vector multiplication.
该算法基于重复利用核心模块的方式完成波前斜率计算，利用矩阵与向量相乘的可分解性完成波前复原计算。
The algorithm calculated the wave front slope by a reusing core module manner and complemented the wave front reconstruction with the decomposition of matrix-vector multiplication.
该算法基于重复利用核心模块的方式完成波前斜率计算，利用矩阵与向量相乘的可分解性完成波前复原计算。
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