. Matrix multiplication plays an important role in a number of applications. Two matrices can only be multiplied if the number of columns of the first matrix is equal to the number of rows in the second. Let's assume we have an mxn matrix A and we want to multiply it by an nxp matrix B. We can express their product as an mxp matrix denoted by C= AB where cij denotes the entry in C at position (i,j) for each element i and j with 1 s is m and 1 sj s p. Now we want to see if we can parallelize the computation of C. Assume that matrices are laid out in memory sequentially as follows: ai.i, azi, az.1, a41,.., etc. Assume that we are going to compute C on both a single core shared memory machine and a 4-core shared-memory machine. Discuss the speedup we would expect to obtain on the 4-core machine, ignoring any memory issues 5 points) . Matrix multiplication plays an important role in a number of applications. Two matrices can only be multiplied if the number of columns of the first matrix is equal to the number of rows in the second. Let's assume we have an mxn matrix A and we want to multiply it by an nxp matrix B. We can express their product as an mxp matrix denoted by C= AB where cij denotes the entry in C at position (i,j) for each element i and j with 1 s is m and 1 sj s p. Now we want to see if we can parallelize the computation of C. Assume that matrices are laid out in memory sequentially as follows: ai.i, azi, az.1, a41,.., etc. Assume that we are going to compute C on both a single core shared memory machine and a 4-core shared-memory machine. Discuss the speedup we would expect to obtain on the 4-core machine, ignoring any memory issues 5 points)