Data movement
In parallel computing, data movements are quite common and should be minimized due to the time and the network overhead as a result of the movements. In this recipe, we will see how that can be optimized to avoid latency as much as we can.
Getting ready
To get ready for this recipe, you need to have the Julia REPL started in multiprocessing mode. This is explained in the Getting ready section in the preceding recipe.
How to do it...
Firstly, we will see how to do a matrix computation using the
@spawnmacro, which helps in data movement. So, we construct a matrix of shape 200 x 200 and then try to square it using the@spawnmacro. This can be done as follows:mat = rand(200, 200) exec_mat = @spawn mat^2 fetch(exec_mat)
The preceding command gives the following output:
Now, we will look at an another way to achieve the same result. This time, we will use the
@spawnmacro directly instead of the initialization step. We will discuss the advantages and drawbacks of each method in the...