[libcxx-dev] OpenMP parallel reduce bugs
Christopher Nelson via libcxx-dev
libcxx-dev at lists.llvm.org
Sat Oct 31 06:35:58 PDT 2020
I was able to puzzle out the patch, and it seems to compile now. I'll
rewrite the reducer and see how it goes. Thanks!
On Fri, Oct 30, 2020 at 4:29 PM Christopher Nelson <nadiasvertex at gmail.com>
wrote:
> Hi Mikhail,
>
> I tried to apply the patch today, but it doesn't apply cleanly. May I ask
> what version of the repo I should use? After I apply it (skipping a hunk
> that doesn't apply) I get some compile errors:
>
>
> /home/christopher/projects/llvm-project/pstl/include/pstl/internal/algorithm_impl.h:1780:13:
> error: unknown type name '_ReduceRes'
> _ReduceRes __table[] = {__broken, __broken, __broken,
> __broken, __broken, __all_true,
> ^
> /home/christopher/projects/llvm-project/pstl/include/pstl/internal/algorithm_impl.h:1783:13:
> error: use of '_ReduceType' with tag type that does not match previous
> declaration
> struct _ReduceType
> ^
> /home/christopher/projects/llvm-project/pstl/include/pstl/internal/algorithm_impl.h:1770:18:
> note: previous use is here
> enum _ReduceType
> ^
> /home/christopher/projects/llvm-project/pstl/include/pstl/internal/algorithm_impl.h:1802:54:
> error: use of undeclared identifier '__val1'; did you mean '__value'?
> return _ReduceType{__broken, __val1.__pos};
> ^~~~~~
> __value
> /home/christopher/projects/llvm-project/pstl/include/pstl/internal/algorithm_impl.h:1799:73:
> note: '__value' declared here
> _ReduceType
> __value) -> _ReduceType {
>
> On Wed, Oct 21, 2020 at 9:36 AM Dvorskiy, Mikhail <
> mikhail.dvorskiy at intel.com> wrote:
>
>> Hi Christopher,
>>
>>
>>
>> Please find the attachment - a patch which fixes the mentioned problem
>> with is partitioned algo. I’ve not yet promoted it into LLVM repo, but you
>> can check it now with OpenMP backend.
>>
>>
>>
>> Best regards,
>>
>> Mikhail Dvorskiy
>>
>>
>>
>> *From:* Christopher Nelson <nadiasvertex at gmail.com>
>> *Sent:* Monday, October 5, 2020 3:01 PM
>> *To:* Dvorskiy, Mikhail <mikhail.dvorskiy at intel.com>
>> *Cc:* Kukanov, Alexey <Alexey.Kukanov at intel.com>; Pavlov, Evgeniy <
>> evgeniy.pavlov at intel.com>; Louis Dionne <ldionne at apple.com>; Thomas
>> Rodgers <trodgers at redhat.com>; Libc++ Dev <libcxx-dev at lists.llvm.org>
>> *Subject:* Re: [libcxx-dev] OpenMP parallel reduce bugs
>>
>>
>>
>> Great, thanks!
>>
>>
>>
>> I appreciate it!
>>
>>
>>
>> On Mon, Oct 5, 2020, 04:22 Dvorskiy, Mikhail <mikhail.dvorskiy at intel.com>
>> wrote:
>>
>> Hi Christopher,
>>
>> I’ve double check the code of __pattern_is_partitioned (which is based on
>> the reduction parallel pattern). Yes, a binary operation is not
>> commutative. So, my hypo was right.
>>
>> Generally speaking the writing “manually” reduction pattern w/o OpenMP s
>> reducer is not good approach due to it may be not effective. Indeed, if we
>> consider your example – the second loop (for) combines the results in
>> serial mode, and std::vector brings additional overheads…
>>
>>
>>
>> Once more, OpenMP reduction requires commutative binary operation and it
>> is right. With PSTL design perspective an algorithm pattern should not rely
>> on a fact that a parallel reduction pattern (which is provided by a
>> parallel backend) support a non-commutative binary operation. So, it is an
>> issue of __pattern_is_partitioned and we will fix it. So while I would
>> suggest don’t worry about that.
>>
>>
>>
>> Best regards,
>>
>> Mikhail Dvorskiy
>>
>>
>>
>> *From:* Christopher Nelson <nadiasvertex at gmail.com>
>> *Sent:* Saturday, October 3, 2020 6:19 PM
>> *To:* Dvorskiy, Mikhail <mikhail.dvorskiy at intel.com>
>> *Cc:* Kukanov, Alexey <Alexey.Kukanov at intel.com>; Pavlov, Evgeniy <
>> evgeniy.pavlov at intel.com>; Louis Dionne <ldionne at apple.com>; Thomas
>> Rodgers <trodgers at redhat.com>; Libc++ Dev <libcxx-dev at lists.llvm.org>
>> *Subject:* Re: [libcxx-dev] OpenMP parallel reduce bugs
>>
>>
>>
>> Hello again,
>>
>>
>>
>> I was able to rewrite the parallel_reduce function in a way that works
>> without using OpenMP's reducer. I have a couple of questions:
>>
>>
>>
>> 1. I use a vector to gather the intervening results for later reduction.
>> Is there any problem depending on vector here?
>>
>> 2. I can see that it might make sense to build a taskloop for the actual
>> reduction if the number of chunks is quite large. Is that something that I
>> should look into more?
>>
>>
>>
>> The code is below. Please let me know if you have any questions or
>> concerns.
>>
>>
>>
>>
>>
>>
>>
>> *//------------------------------------------------------------------------// parallel_reduce//------------------------------------------------------------------------*template <class _RandomAccessIterator, class _Value, typename _RealBody, typename _Reduction>
>> _Value
>> __parallel_reduce_body(_RandomAccessIterator __first, _RandomAccessIterator __last, _Value __identity,
>> _RealBody __real_body, _Reduction __reduction)
>> {
>> std::size_t __n_chunks{0}, __chunk_size{0}, __first_chunk_size{0};
>> __chunk_partitioner(__first, __last, __n_chunks, __chunk_size, __first_chunk_size);
>>
>> std::vector<_Value> __values(__n_chunks);
>>
>>
>> *// To avoid over-subscription we use taskloop for the nested parallelism *_PSTL_PRAGMA(omp taskloop shared(__values))
>> for (std::size_t __chunk = 0; __chunk < __n_chunks; ++__chunk)
>> {
>> auto __this_chunk_size = __chunk == 0 ? __first_chunk_size : __chunk_size;
>> auto __index = __chunk == 0 ? 0 : (__chunk * __chunk_size) + (__first_chunk_size - __chunk_size);
>> auto __begin = __first + __index;
>> auto __end = __begin + __this_chunk_size;
>> __values[__chunk] = __real_body(__begin, __end, __identity);
>> }
>>
>> auto __result = __values.front();
>> for (auto p = __values.begin() + 1; p != __values.end(); ++p)
>> {
>> __result = __reduction(__result, *p);
>> }
>>
>> return __result;
>> }
>>
>>
>>
>> On Fri, Oct 2, 2020 at 1:33 PM Christopher Nelson <nadiasvertex at gmail.com>
>> wrote:
>>
>> Thank you. I wondered if you had an update on this. I've done some
>> further looking, and I think that is correct. I've tried to find example
>> implementations of performing reductions with openmp that don't require a
>> commutative operator. It seems like rewriting the is_partioned algorithm to
>> provide a commutative operator might be a larger / undesirable change.
>>
>>
>>
>> Do you have any guidance on manually writing a task loop in openmp that
>> performs the reduction without requiring commutativity?
>>
>>
>>
>> Thanks!
>>
>>
>>
>> -={C}=-
>>
>>
>>
>> On Thu, Oct 1, 2020 at 9:11 AM Dvorskiy, Mikhail <
>> mikhail.dvorskiy at intel.com> wrote:
>>
>> Hi Christopher,
>>
>>
>>
>> Yes, “is_partitioned” algo implementation is based on a reduction
>> parallel pattern.
>>
>> And it looks that a binary operation (combiner) is not commutative.
>>
>>
>>
>> In general, “reduction” algorithm requires a commutative binary
>> operation. And OpenMP reduction requires that.
>>
>> For TBB backend it works because TBB parallel reduction algorithm doesn’t
>> require a commutative binary operation.
>>
>>
>>
>> We (me or Evgeniy) will check that hypo and inform you.
>>
>>
>>
>> Best regards,
>>
>> Mikhail Dvorskiy
>>
>>
>>
>> *From:* Christopher Nelson <nadiasvertex at gmail.com>
>> *Sent:* Thursday, October 1, 2020 2:46 AM
>> *To:* Kukanov, Alexey <Alexey.Kukanov at intel.com>
>> *Cc:* Dvorskiy, Mikhail <mikhail.dvorskiy at intel.com>; Pavlov, Evgeniy <
>> evgeniy.pavlov at intel.com>; Louis Dionne <ldionne at apple.com>; Thomas
>> Rodgers <trodgers at redhat.com>; Libc++ Dev <libcxx-dev at lists.llvm.org>
>> *Subject:* [libcxx-dev] OpenMP parallel reduce bugs
>>
>>
>>
>> Hello friends,
>>
>>
>>
>> I have been working on the OpenMP backend for the parallel STL, and most
>> of the tests are passing. However, among the failures is the
>> "is_partitioned" test. I have rewritten the __parallel_reduce backend
>> function to be simpler to understand in an attempt to understand what is
>> failing (code is below.)
>>
>>
>>
>> I also rewrote it as a serial function that splits the iteration range in
>> two and then calls __reduction() on each half of the range being passed in.
>> The result I get from the serial execution as compared to the result I get
>> from the parallel execution is different.
>>
>>
>>
>> I have verified that the parallel execution tasks are run, and that their
>> results match what each serial execution would be if I ran them that way.
>>
>>
>>
>> I am wondering if there is something wrong with the way OpenMP is running
>> the reducer here? Perhaps it is injecting a value into the computation that
>> is unexpected for this algorithm? Does anything jump out at anyone as being
>> suspicious?
>>
>>
>>
>> Thank you again for your time and assistance!
>>
>> template <class _RandomAccessIterator, class _Value, typename _RealBody, typename _Reduction>
>> _Value
>> __parallel_reduce_body(_RandomAccessIterator __first, _RandomAccessIterator __last, _Value __identity,
>> _RealBody __real_body, _Reduction __reduction)
>> {
>> std::size_t __item_count = __last - __first;
>> std::size_t __head_items = (__item_count / __default_chunk_size) * __default_chunk_size;
>>
>>
>>
>> *// We should encapsulate a result value and a reduction operator since we // cannot use a lambda in OpenMP UDR. *using _CombinerType = __pstl::__internal::_Combiner<_Value, _Reduction>;
>> _CombinerType __result{__identity, &__reduction};
>> _PSTL_PRAGMA_DECLARE_REDUCTION(__combiner, _CombinerType)
>>
>>
>>
>> *// To avoid over-subscription we use taskloop for the nested parallelism //_PSTL_PRAGMA(omp taskloop reduction(__combiner : __result)) *for (std::size_t __i = 0; __i < __item_count; __i += __default_chunk_size)
>> {
>> auto __begin = __first + __i;
>> auto __end = __i < __head_items ? __begin + __default_chunk_size : __last;
>> __result.__value = __real_body(__begin, __end, __identity);
>> }
>>
>> return __result.__value;
>> }
>>
>>
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