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. It doesn't matter what rows of B that are used to get the matrix dimensions to match A. If A has more rows than B then NewB will just repeat B's rows until it matches the row size of A. The operation is not memory-bound processing time is not dominated by memory access time. I just need the number of rows to match both in A and B. 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. These sections must be able to execute with little communication between processes. The function performs operations that easily partition into sections that execute concurrently.
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