LeetCode 311. Sparse Matrix Multiplication 需要利用 sparse 的特性来做这道题。扫描A的每一个元素 A[i][j],考虑A[i][j]会被B中哪些元素乘到。 对于A的第i行 A[i][*],会乘以B的每一列 B[*][k] ∀k,得到 res[i][k]。因此,最后 res[i][k] += A[i][j] * B[j][k]。 这样的好处...
To address these challenges on a GPU, we evaluate three possible variations of matrix multiplication (Row-Column, Column-Row, Row-Row) and perform suitable optimizations targeted at sparse matrices. Our experiments indicate that the Row-Row formulation, which mostly outperforms the other formulations...
int[][] c12 = MatrixSum(SparseMatrixMultiplicationHelper(A,B,a11,b12),SparseMatrixMultiplicationHelper(A,B,a12,b22)); int[][] c21 = MatrixSum(SparseMatrixMultiplicationHelper(A,B,a21,b11),SparseMatrixMultiplicationHelper(A,B,a22,b21)); int[][] c22 = MatrixSum(SparseMatrixMultiplicationHelper...
HashMap, time O(m * max(n, l)), space O(n * l) 因为给定的matrix是稀疏矩阵,所以我们要做一些对于0的预处理。 由于C[i][k] = A[i][x] * B[x][k], 0 <= x <= n 我们可以用一个HashMap,将B中每行不为0的元素保存下来。 然后遍历A,将每个不为0的元素累加到C中去。 classSolution...
这里的优化在代码上不是很明显,但实际上是对矩阵乘法非常熟悉才能用得这么溜。这个帖子说得非常清楚为什么可以提高performance from 600ms to 60ms. 关键就是C[i][j]没有被一次就算出来,而是通过多次累计计算。并且因为可以先check A[i][k]是不是为零而省略 B[0].length那么多步。
LeetCode "Sparse Matrix Multiplication",Takingadvantageof'sparse'classSolution{public:vector>multiply(vector>&A,vector>&B){vector>ret;intha=A.si...
Sparse matrix multiplication package (SMMP). Advances in Compu- tational Mathematics 1, 1 (1993), 127-137.BANK, R. AND DOUGLAS, C. 1993. Sparse matrix multiplication package (SMMP). Adv. in Comput. Math. 1, 1, 127-137.Bank, R. and Douglas, C. 1993. Sparse matrix multiplication ...
311. Sparse Matrix Multiplication,classSolution{publicint[][]multiply(int[][]A,int[][]B){intm=A.length;intn=A[0].length;intnB=B[0].length;int[][]C=newint[m][nB];for(...
We also obtain improved algorithms for the multiplication of more than two sparse matrices. As the known fast rectangular matrix multiplication algorithms are far from being practical, our result, at least for now, is only of theoretical value....
Sparse matrix-matrix multiplication (SpGEMM) is a key kernel in many applications in High Performance Computing such as algebraic multigrid solvers and graph analytics. Optimizing SpGEMM on modern processors is challenging due to random data accesses, poor data locality and load imbalance during computat...