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    <p>Answer a bit depends on nature of your hardware and your application/workload. Do you use multi-node cluster (most typical) or big shared memory machine? Assuming you are cluster user, you will have to use MPI or Fortran coarray for (more likely) distributed memory cross-node parallelism AND SOMETHING fon inter-node shared memory parallelism (SMP). </p> <p>Shared memory parallelism can give you speed-up proportional to number of cores on a node(up to 32x with Xeons) or even more with coprocessors. Distributed memory parallelism can give you speedup proportional to number of nodes. Both types (or actually all 3 types) of parallelism have to be used these days to get reasonable performance. You may think of it like a hierarchy: 1.MPI or coarray on the top, 2.something for shared memory threading in the middle and 3. vectorization in the innermost level.</p> <p>Well, from your question, it sounds like you are talking mostly about SMP multicore threading parallelism level. This is where -parallel Auto-Parallelization behaves. Dont expect big magic from auto-par. If you want to get better scalable parallelism, you have to try fortran OpenMP or MPI-for-shared memory. I would recommend OpenMP in most cases; its often easier to program and more performance. But. its up to you and you really should think bigger- about all 3 levels of parallelism. If you plan to address all 3 levels, then probably optimal combination (since you are a happy intel fortran user) is 1. MPI for 1st level+ 2. OpenMP for SMP level + 3. AutoVectorization guided by OpenMP 4.0 pragma simd on 3rd level. Im not an expert in coarray, but it might be good alternative to 1.MPI.</p> <p>My answer does make less sence if you dont deal with classic cluster hardware. </p>
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    1. POFortran code using auto parallelization vs MPI
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    1. COThank You, it makes more sense for me to use OPENMP because it seems much easier to implement.
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    2. COOk, glad to hear that, its my point of view too, since its less effort and normally more performance on multi-core. Its still not a free lunch(even -parallel is not a completely free lunch), because you have to understand some minimal background, but its still smaller programmer effort than MPI for SMP. OpenMP in intel fortran will also open the door to you for efficient guided autovectorization, which is a huge extra bonus in modern supercomputers.
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