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 Original Message (ID '122748') By yehuda: Parallelization is not always faster there are cost of communication to move results forward and backward to and from the slave kernels When the cost is high compared with the computation (as in you case) serial computation is preferable In addition, why are you using Do loops in the first place? if you use the Listable property of Plus you need just to write c=a+b; no wasting time on generating n x n matrix of random numbers just to occupy memory because you are using loops AbsoluteTiming[n = 5000; a = RandomReal[{}, {n, n}]; b = RandomReal[{}, {n, n}]; c = a + b;] This simple serial code is faster than any parallelization Just to demonstrate when parallelization is better take a recursive function (say computing elements of Fibonacci series). The definition uses two recursions I set both to 25 (relatively long) I have on my system 8 hyper threads (quad core) the serial implementation is now fib[i_Integer] := fib[i - 1] + fib[i - 2] fib[0] = fib[1] = 1; n = 8; a = b = c = ConstantArray[25, {n, n}]; AbsoluteTiming[ Do[c[[i, j]] = fib[a[[i, j]]] + fib[b[[i, j]]], {i, n}, {j, n}];] which takes almost 24 seconds now take the simplest parallelization INCLUDING the time to upload the kernels, and it takes about a third of the serial (all 8 hyper threads are working) fib[i_Integer] := fib[i - 1] + fib[i - 2] fib[0] = fib[1] = 1; n = 8; a = b = c = ConstantArray[25, {n, n}]; AbsoluteTiming[ ParallelDo[ c[[i, j]] = fib[a[[i, j]]] + fib[b[[i, j]]], {i, n}, {j, n}];] Quikt[] The longer the computational part, the better parallelization performance Of course of IO is involved, things are different HTH yehuda