matrix-multiplication

Here Matrix multiplication using hdf5 I use hdf5 (pytables) for big matrix multiplication, but I was suprised because using hdf5 it works even faster then using plain numpy.dot and store matrices in RAM, what is the reason of this behavior? And maybe there is some faster function for matrix multiplication in python, because I still use numpy.dot for small block matrix multiplication. here is some code: Assume matrices can fit in RAM: test on matrix 10*1000 x 1000. Using default numpy(I think no BLAS lib). Plain numpy arrays are in RAM: time 9.48 If A,B in RAM, C on disk: time 1.48 If A,B,C on

I was trying to figure out the fastest way to do matrix multiplication and tried 3 different ways: Pure python implementation: no surprises here. Numpy implementation using numpy.dot(a, b) Interfacing with C using ctypes module in Python. This is the C code that is transformed into a shared library: #include <stdio.h> #include <stdlib.h> void matmult(float* a, float* b, float* c, int n) { int i = 0; int j = 0; int k = 0; /*float* c = malloc(nay * sizeof(float));*/ for (i = 0; i < n; i++) { for (j = 0; j < n; j++) { int sub = 0; for (k = 0; k < n; k++) { sub = sub + a[i * n + k] * b[k * n + j];

I'm looking for a faster and trickier way to multiply two 4x4 matrices in C. My current research is focused on x86-64 assembly with SIMD extensions. So far, I've created a function witch is about 6x faster than a naive C implementation, which has exceeded my expectations for the performance improvement. Unfortunately, this stays true only when no optimization flags are used for compilation (GCC 4.7). With -O2 , C becomes faster and my effort becomes meaningless. I know that modern compilers make use of complex optimization techniques to achieve an almost perfect code, usually faster than an

I'm new to the CUDA paradigm. My question is in determining the number of threads per block, and blocks per grid. Does a bit of art and trial play into this? What I've found is that many examples have seemingly arbitrary number chosen for these things. I'm considering a problem where I would be able to pass matrices - of any size - to a method for multiplication. So that, each element of C (as in C = A * B) would be calculated by a single thread. How would you determine the threads/block, blocks/grid in this case? In general you want to size your blocks/grid to match your data and

I want to calculate the row-wise dot product of two matrices of the same dimension as fast as possible. This is the way I am doing it: import numpy as np a = np.array([[1,2,3], [3,4,5]]) b = np.array([[1,2,3], [1,2,3]]) result = np.array([]) for row1, row2 in a, b: result = np.append(result, np.dot(row1, row2)) print result and of course the output is: [ 26. 14.] Check out numpy.einsum for another method: In [52]: a Out[52]: array([[1, 2, 3], [3, 4, 5]]) In [53]: b Out[53]: array([[1, 2, 3], [1, 2, 3]]) In [54]: einsum('ij,ij->i', a, b) Out[54]: array([14, 26]) Looks like einsum is a bit

I have two matrices a = np.matrix([[1,2], [3,4]]) b = np.matrix([[5,6], [7,8]]) and I want to get the element-wise product, [[1*5,2*6], [3*7,4*8]] , equaling [[5,12], [21,32]] I have tried print(np.dot(a,b)) and print(a*b) but both give the result [[19 22], [43 50]] which is the matrix product, not the element-wise product. How can I get the the element-wise product (aka Hadamard product) using built-in functions? Rahul K P For elementwise multiplication of matrix objects, you can use numpy.multiply : import numpy as np a = np.array([[1,2],[3,4]]) b = np.array([[5,6],[7,8]]) np.multiply(a,b)

I am working on parallel programming concepts and trying to optimize matrix multiplication example on single core. The fastest implementation I came up so far is the following: /* This routine performs a dgemm operation * C := C + A * B * where A, B, and C are lda-by-lda matrices stored in column-major format. * On exit, A and B maintain their input values. */ void square_dgemm (int n, double* A, double* B, double* C) { /* For each row i of A */ for (int i = 0; i < n; ++i) /* For each column j of B */ for (int j = 0; j < n; ++j) { /* Compute C(i,j) */ double cij = C[i+j*n]; for( int k = 0; k <