Towards creating a general matrix vector multiplication function скачать в хорошем качестве

Towards creating a general matrix vector multiplication function

We analyze our four algorithms for our two different types of matrix-vector multiplication including matrix-column-vector multiplication (MCVM) and row-vector-matrix multiplication (RVMM). 00:00 -Introduction 00:11 -Recall the two versions of MCVM 00:58 -Intro to table for data flow of matrix-vector multiplication 01:21 -Algorithm 1.1: BLAS 2 column axpy algorithm for MCVM 01:31 -For all algorithms, we initialize and then define outer loop 02:37 -Algorithm 1.2: BLAS 1 column axpy algorithm for MCVM 03:12 -Analyze algorithm 1 data flow: The column axpy algorithm for MCVM 04:19 -Algorithm 2.1: BLAS 2 row dot algorithm for MCVM 05:04 -Algorithm 2.2: BLAS 1 row dot algorithm for MCVM 05:36 -Analyze data flow for algorithm 2: The row dot algorithm for MCVM 06:23 -Recall the two versions of RVMM 07:01 -Algorithm 3.1: BLAS 2 row axpy algorithm for RVMM 08:08 -Algorithm 3.2: BLAS 1 row axpy algorithm for RVMM 08:47 -Analyze data flow for algorithm 3: The row axpy algorithm for RVMM 09:36 -Make some guesses about Algorithm 4 10:18 -Algorithm 4.1: BLAS 2 column dot algorithm for RVMM 11:06 -Algorithm 4.2: BLAS 1 column dot algorithm for RVMM 12:09 -Analyze algorithm 4 data flow: The column dot algorithm for RVMM 12:15 -Why spend so much time analyzing these details? 12:34 -General model for Von Neumann architecture 12:48 -Basic model for how matrices are stored in memory 15:28 -MATLAB is not designed for code optimization at low level 16:00 -Challenge 1: Write specifications for a matrix-vector product function 16:28 -Challenge 2: Teach yourself about computer architecture 17:24 -Conclusion