|-O3 -march=native -ffast-math -funroll-loops -fopenmp |85.2 ms |91.2 ms |86. An exception is when you take the dot product of a complex vector with itself. The iterative solvers require to determine the product Ax where x is the test solution. I use iterative solvers because the size of A is say 40000x40000. In general, the dot product of two complex vectors is also complex. I have to solve in MATLAB a linear system of equations AxB where A is symmetric and its elements depend on the difference of the indices: Aijf (i-j). This definition says that C (i,j) is the inner product of the i th row of A with the j th column of B. If A is an m-by-p and B is a p-by-n matrix, then C is an m-by-n matrix defined by C ( i, j) k 1 p A ( i, k) B ( k, j). That is Cx where xvec (X) Yet I found the last term (XB) is very difficult to vectorize, it would be very sparsy. Description example C AB is the matrix product of A and B. |-O3 -march=native |362 ms |363 ms |361 ms | The result is a complex scalar since A and B are complex. Vectorizing matrix multiplication in matlab Ask Question Asked 10 years, 9 months ago Modified 10 years, 9 months ago Viewed 1k times 1 I would like to transform the matrix product AX-XB into vector form. Within the brackets, use a semicolon to denote the end of a row. To define a matrix manually, use square brackets to denote the beginning and end of the array. The result is a 4-by-4 matrix, also called the outer product of the vectors. Alternatively, you can calculate the dot product A B with the syntax dot (A,B). | Options | C (loop) | Fortran (intrinsic) | Fortran (loop)| A column vector is an m-by-1 matrix, a row vector is a 1-by-n matrix, and a scalar is a 1-by-1 matrix. The result is a 1-by-1 scalar, also called the dot product or inner product of the vectors A and B. I’m curious if anybody has thoughts on the analysis I did - are there other options I should try, other circumstances in which the matmuls are occurring, something I overlooked? Thanks! _summary.md Here I compared the effect of different compiler optimizations in both Fortran and C for a program that multiplies a matrix with a vector. I compared with both Fortran and C, and got essentially the same top speed but Fortran’s matmul intrinsic was much faster with no optimization turned on (and interestingly gets slowed way down by -O3). Hi, I’m looking at the impact of different compiler options on the speed of vector matrix multiplication.
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