1623 add training #3145
1623 add training #3145
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… for transformation uses of PSyclone.
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I think I have addressed all issue. Additionally, I fixed a missing I'll trigger the IT tomorrow. |
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The documentation failure is caused by the link The IT failed in the lfric intregration test. It looks like an nvidia compiler crash: (line 40091) in the lfric gungho with openmp offload section. Again, I can't see how this PR should cause this? And I can't figure out which file might be causing this? |
Don't worry. We see this sometimes. Re-running the job almost always fixes it! |
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Great stuff Joerg. Just a bit more tidying to do and then we're good.
| def trans(psyir: FileContainer) -> None: | ||
| ''' | ||
| Take the supplied psy object, and apply 'omp parallel do' to all loops. | ||
| Take the supplied PSyOR object, and apply 'omp parallel do' to all loops. |
| :type psy: :py:class:`psyclone.psyGen.PSy` | ||
| Take the supplied psyir object, apply module inlining and add generic | ||
| OpenMP parallelisation to the code. Also check if loop fusion can | ||
| be applied. Also check if loop fusion can |
| The task here is to create an alternative version of a loop. When | ||
| optimising code, it can be useful to have two different versions of | ||
| the same loop depending on loop length. For example, if the loop | ||
| length it long enough, vectorisation might give a speed benefit, |
| length it long enough, vectorisation might give a speed benefit, | ||
| while if the loop is too short, vectorisation will have too much | ||
| start-up overhead, causing a slow-down. Or only if the loop length | ||
| is large enough it is worth to parallelise a loop. |
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"...enough is it worth parallelising."
| start-up overhead, causing a slow-down. Or only if the loop length | ||
| is large enough it is worth to parallelise a loop. | ||
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| Of course, two versions of the could be implemented in the original source |
Adds the PSyclone training material (based on #3131)