matrix-multiplication

I newbie to mpi programming. I was trying to write matrix multiplication. Went through the post MPI Matrix Multiplication with scatter gather about matrix multiplication using scatter and gather routine. I tried modifying the code available on above post as below... #define N 4 #include <stdio.h> #include <math.h> #include <sys/time.h> #include <stdlib.h> #include <stddef.h> #include "mpi.h" void print_results(char *prompt, int a[N][N]); int main(int argc, char *argv[]) { int i, j, k, rank, size, tag = 99, blksz, sum = 0; int a[N][N]={{1,2,3,4},{5,6,7,8},{9,1,2,3},{4,5,6,7,}}; int b[N][N]={{1

I have two separate dictionaries with keys and values that I would like to multiply together. The values should be multiplied just by the keys that they have. i.e. dict1 = {'a': 1, 'b': 2, 'c': 3} dict2 = {'a': 15, 'b': 10, 'd': 17} dict3 = dict.items() * dict.items() print dict3 #### #dict3 should equal {'a': 15, 'b': 20} If anyone could help, that would be great. Thanks! You can use a dict comprehension : >>> {k : v * dict2[k] for k, v in dict1.items() if k in dict2} {'a': 15, 'b': 20} Or, in pre-2.7 Python, the dict constructor in combination with a generator expression : >>> dict((k, v *

Does anyone know which algorithm MATLAB uses for matrix multiplication and what is its time complexity? For completeness -- as mentioned in this thread , Matlab uses the DGEMM (Double GEneral Matrix Multiplication) routine from BLAS (Basic Linear Algebra Subprograms). Note that there is not one single implementation of BLAS - it is tuned for particular processor architectures. Therefore you cannot be absolutely certain which algorithm is being used on your machine without finding out which version of BLAS is in use. The specification for BLAS specifies the inputs and output of each subroutine,

I have two matrix A and B , so what's the fastest way to just calculate diag(A%*%B) , i.e., the inner-product of the ith row of A and ith column of B , and the inner-product of other terms are not concerned . supplement: A and B have large row and column numbers respectively. This can be done without full matrix multiplication, using just multiplication of matrix elements. We need to multiply rows of A by the matching columns of B and sum the elements. Rows of A are columns of t(A) , which we multiply element-wise by B and sum the columns. In other words: colSums(t(A) * B) Testing the code we

I need to implement a matrix multiplication on GPU with CUDA for large matrices. Size of each matrix alone is bigger than the GPU memory. So I think I need an algorithm to do that efficiently. I went around the internet but couldn't find any. Can anyone give me the name or link of such algorithms. Thank you talonmies There isn't really a formal algorithm for this; in general, these sorts of linear algebra operations where the whole problem isn't stored in memory simultaneously are referred to as "out of core" operations. To solve it, you don't need a particularly elaborate algorithm, just the

I am using Google Analytics and processing the data with Bigquery, I need to do a matrix multiplication . What is the most feasible way of implementing matrix multiplication in Google Cloud? Can it be done directly in Bigquery? Assuming MatrixA is a table with below columns: i, k, value and MatrixB - has schema as k, j, value and also assuming that range of k-values is the same in both tables: This would mimic below matrices : Matrix A 2 -3 4 -1 0 2 Matrix B -1 2 3 0 1 7 1 1 -2 Below code for multiplication is for BigQuery Standard SQL #standardSQL WITH MatrixA AS ( SELECT 1 AS i, 1 AS k, 2 AS