Dec 7, 2015 · The best matrix multiplication algorithm is the one that someone with detailed architectural knowledge has already hand-tuned for your target platform. There are lots of good libraries that supply tuned matrix-multiply implementations.

Jan 22, 2017 · Solvay Strassen algorithm achieves a complexity of O(n 2.807) by reducing the number of multiplications required for each 2x2 sub-matrix from 8 to 7. The fastest known matrix multiplication algorithm is Coppersmith-Winograd algorithm with a complexity of O(n 2.3737). Unless the matrix is huge, these algorithms do not result in a vast difference ...

Jul 23, 2013 · Matrix multiplicaiton is so common that developers will optimize it by hand. In particular this is done in GotoBLAS. Heterogeneous(GPU) Computing. Matrix Multiply is very FLOP/compute intensive, making it an ideal candidate to be run on GPUs. cuBLAS and MAGMA are good candidates for this. In short, dense linear algebra is a well studied topic.

Aug 20, 2009 · Advanced matrix algorithms such as Strassen, implementations dont use them as they dont help in practice; Most implementations break each operation into small-dimension matrix or vector operations in the more or less obvious way. For example a large 1000x1000 matrix multiplication may broken into a sequence of 50x50 matrix multiplications.

I'm performing matrix multiplication with this simple algorithm. To be more flexible I used objects for the matricies which contain dynamicly created arrays. Comparing this solution to my first one with static arrays it is 4 times slower.

Apr 2, 2014 · Strassen's algorithm for matrix multiplication just gives a marginal improvement over the conventional O(N^3) algorithm. It has higher constant factors and is much harder to implement. Given these shortcomings, is strassens algorithm actually useful and is it implemented in any library for matrix multiplication?

Nov 6, 2015 · I'm familiar with a number of matrix setups, but I would like to know which method will run the fastest. I've done extensive research, but turned up very little results. Here is a list of the matrix multiplication algorithms I am familiar with: Iterative algorithm ; Divide and Conquer algorithm; Sub Cubic algorithms; Shared Memory Parallelism

Sep 16, 2015 · The problem comes when I looked up Wikipedia page of Matrix multiplication algorithm. It says: This algorithm has a critical path length of Θ((log n)^2) steps, meaning it takes that much time on an ideal machine with an infinite number of processors; therefore, it has a maximum possible speedup of Θ(n3/((log n)^2)) on any real computer.

May 31, 2012 · I had to add a random element into the matrix each time during the -O3 flag or the compiler completely optimized away the simple multiplication, taking a time of zero within clock precision. Since the laderman algorithm was a pain to check/double check I'll post the complete code below for posterity. Specs: Ubuntu 12.04, Dell Prevision T1600, gcc.

May 4, 2012 · The optimization, by the way, goes beyond compiler optimizations. Above, Philip mentioned Coppersmith–Winograd. If I remember correctly, this is the algorithm which is used for most cases of matrix multiplication in ATLAS (though a commenter notes it could be Strassen's algorithm). In other words, your matmult algorithm is the trivial ...