Numerical ComputationParallel Computing

Parallel computing involves decomposing a computational task into subtasks which may be performed concurrently.

You can start a Julia session with worker processes via julia -p n and loading the distributed computing tools with using Distributed.

• pmap(f,A) applies the function f to each element of the collection A, taking advantage of the available worker processes. For example, to check the primality of the positive integers up to 100,000 in parallel:

  using Distributed, Primes
  pmap(isprime,2:100_000)

• If (op) is an operator, then @distributed (op) for ... end assigns a subrange of the given for loop to each worker. The values returned by the body of the loop are combined using the operator op. For example, to sum a million random Gaussians in parallel fashion:

  using Distributed
  @distributed (+) for i=1:1_000_000
    randn()
  end

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Congratulations! You have finished the Data Gymnasia numerical computation course.