![]() The matrix analysis functions det, rcond, hess, and expm also show significant increase in speed on large double-precision arrays. The matrix multiply (X*Y) and matrix power (X^p) operators show significant increase in speed on large double-precision arrays (on order of 10,000 elements). As a general rule, complicated functions speed up more than simple functions. The operation is not memory-bound processing time is not dominated by memory access time. For example, most functions speed up only when the array contains several thousand elements or more. The data size is large enough so that any advantages of concurrent execution outweigh the time required to partition the data and manage separate execution threads. They should require few sequential operations. sz must contain at least 2 elements, and prod (sz) must be the same as numel (A). ![]() Inputs A and B must either be the same size or have sizes that are compatible (for example, A is an M -by- N matrix and B is a scalar or 1 -by- N row vector). We can easily find the transpose of the matrix A. Creating a matrix is as easy as making a vector, using semicolons ( ) to separate the rows of a matrix. For example, reshape (A, 2,3) reshapes A into a 2-by-3 matrix. Operands, specified as scalars, vectors, matrices, multidimensional arrays, tables, or timetables. plot (b, '' ) axis ( 0 10 0 10) One area in which MATLAB excels is matrix computation. These sections must be able to execute with little communication between processes. B reshape (A,sz) reshapes A using the size vector, sz, to define size (B). ![]() ![]() The function performs operations that easily partition into sections that execute concurrently. For example (n3): A 1,2,3 4,5,6 1,9,9 I want save this matrix to vector (or array) B, but rows should be first. ![]()
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