Hey Zahra
For a matrix of size 15k x 10 x 1k, using parallel computing with "parfor" can potentially speed up the iteration process by utilizing multiple CPU cores to perform the computation in parallel. However, the actual speedup depends on the specific operations being performed within the loop, the available resources, and the memory usage.
In general, if the operations within the loop involve heavy computations, such as matrix multiplications, then using parallel computing can provide significant speedup. On the other hand, if the operations are simple, such as accessing individual elements of the matrix, then the overhead of parallelization may outweigh the benefits. You can read more about this here: https://www.mathworks.com/help/parallel-computing/decide-when-to-use-parfor.html . To further optimize the iteration process, you can consider vectorizing the loop or using built-in functions that operate on the entire matrix at once. Vectorization and built-in functions are generally faster than using loops, especially for large matrices.If you need to perform a more complex operation that cannot be easily vectorized, you can try to optimize the loop using techniques such as preallocating arrays, minimizing memory usage, and avoiding unnecessary operations.
Hope this information helps.
