[cfe-dev] Comparison of 2 schemes to implement OpenMP 5.0 declare mapper codegen

Lingda Li via cfe-dev cfe-dev at lists.llvm.org
Sat Jun 29 06:12:53 PDT 2019


Hi Alexey, Hal, and James,

Please see my response inline below:

On Sat, Jun 29, 2019 at 8:30 AM Alexey Bataev <a.bataev at hotmail.com> wrote:

> Hi Hal,
>
> Best regards,
> Alexey Bataev
>
> 28 июня 2019 г., в 23:46, Finkel, Hal J. via cfe-dev <
> cfe-dev at lists.llvm.org> написал(а):
>
> Hi, Alexey, Lingda,
>
> I haven't been following this closely, so a few questions/comments:
>
>  1. Recursive mappers are not supported in OpenMP 5, but do we expect that
> to change in the future?
>
> Good question. Do not know, actually, but I think both of those schemes
> can be adapted to support recursive mappers.
>

I agree. It will be trivial to support recursive mappers within the
framework of these schemes if needed in the future. In case of recursive
mappers, mapper functions won't be able to fully inlined in scheme 1, so
compiler optimization may be limited.

>  2. Our experience so far suggests that the most important optimization in
> this space is to limit the number of distinct host-to-device transfers (or
> data copies) on systems where data needs to be copied. In these schemes,
> where does that coalescing occur?
>
> In both schemes we transfer the data only ones, after we gather all the
> required data mapping info and after that we transfer it to the device at
> once. The only difference in these schemes is the number of runtime
> functions calls required to fill this mapping data.
>

Both schemes can do such coalescing in the runtime after all mapping
information is collected. Scheme 1 can also do such coalescing in the
compiler optimization of mapper function, it will be hard to do so though.


>
>  3. So long as the mappers aren't recursive, I agree with Alexey that the
> total number of to-be-mapped components should be efficient to calculate.
> The counting function should simplify to a trivial expression in nearly all
> cases. The only case where it might not is where the type contains an array
> section with dynamic bounds, and the element type also has a mapper with an
> array section with dynamic bounds. In this case (similar to the unsupported
> recursive cases, which as an aside, we should probably support it as an
> extension) we could need to walk the data structure twice to precalculate
> the number of total components to map. However, this case is certainly
> detectable by static analysis of the declared mappers, and so I think that
> we can get the best of both worlds: we could use Alexey's proposed scheme
> except in cases where we truly need to walk the data-structure twice, in
> which case we could use Lingda's combined walk/push_back scheme. Is there
> any reason why that wouldn't work?
>
> I think it is better to use only one scheme. I rather doubt that we can
> implement some kind of analysis in the frontend. Later, when the real
> codegen is moved to the backend, we can try to implement 2 schemes. But not
> today. We need to choose one and I just want to hear all pros and cons for
> both (actually, there are 3 schemes already) schemes to choose the most
> flexible, reliable and fast one.
>

The benefit of scheme 2 is to have memory preallocated instead of using
push_back().
Hal, do you think the performance overhead of push_back() is larger than
the overhead of precalculating total size, and why?

Thanks,
Lingda Li

> Thanks again,
>
> Hal
> On 6/28/19 9:00 AM, Alexey Bataev wrote:
>
> Hi Lingda, thanks for your comments.
> We can allocate the buffer either by allocating it on the stack or calling
> OpenMP allocate function.
> With this solution, we allocate memory only once (no need to resize buffer
> after push_backs) and we do not need to call the runtime function to put
> map data to the buffer, compiler generated code can do it.
> But anyway, I agree, it would be good to hear some other opinions.
> --------------
> Best regards,
> Alexey Bataev
>
>
> ...
>
> --
> Hal Finkel
> Lead, Compiler Technology and Programming Languages
> Leadership Computing Facility
> Argonne National Laboratory
>
>
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