intermediate changes

ref:cde9a383711a11544ce7e107a78147fb96cc4029

1 files changed, 318 insertions, 0 deletions

diff --git a/contrib/libs/llvm12/include/llvm/Support/Parallel.h b/contrib/libs/llvm12/include/llvm/Support/Parallel.h
new file mode 100644
index 00000000000..ac053bb4cce
--- /dev/null
+++ b/contrib/libs/llvm12/include/llvm/Support/Parallel.h
@@ -0,0 +1,318 @@
+#pragma once
+
+#ifdef __GNUC__
+#pragma GCC diagnostic push
+#pragma GCC diagnostic ignored "-Wunused-parameter"
+#endif
+
+//===- llvm/Support/Parallel.h - Parallel algorithms ----------------------===//
+//
+// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
+// See https://llvm.org/LICENSE.txt for license information.
+// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+//
+//===----------------------------------------------------------------------===//
+
+#ifndef LLVM_SUPPORT_PARALLEL_H
+#define LLVM_SUPPORT_PARALLEL_H
+
+#include "llvm/ADT/STLExtras.h"
+#include "llvm/Config/llvm-config.h"
+#include "llvm/Support/Error.h"
+#include "llvm/Support/MathExtras.h"
+#include "llvm/Support/Threading.h"
+
+#include <algorithm>
+#include <condition_variable>
+#include <functional>
+#include <mutex>
+
+namespace llvm {
+
+namespace parallel {
+
+// Strategy for the default executor used by the parallel routines provided by
+// this file. It defaults to using all hardware threads and should be
+// initialized before the first use of parallel routines.
+extern ThreadPoolStrategy strategy;
+
+namespace detail {
+
+#if LLVM_ENABLE_THREADS
+
+class Latch {
+  uint32_t Count;
+  mutable std::mutex Mutex;
+  mutable std::condition_variable Cond;
+
+public:
+  explicit Latch(uint32_t Count = 0) : Count(Count) {}
+  ~Latch() { sync(); }
+
+  void inc() {
+    std::lock_guard<std::mutex> lock(Mutex);
+    ++Count;
+  }
+
+  void dec() {
+    std::lock_guard<std::mutex> lock(Mutex);
+    if (--Count == 0)
+      Cond.notify_all();
+  }
+
+  void sync() const {
+    std::unique_lock<std::mutex> lock(Mutex);
+    Cond.wait(lock, [&] { return Count == 0; });
+  }
+};
+
+class TaskGroup {
+  Latch L;
+  bool Parallel;
+
+public:
+  TaskGroup();
+  ~TaskGroup();
+
+  void spawn(std::function<void()> f);
+
+  void sync() const { L.sync(); }
+};
+
+const ptrdiff_t MinParallelSize = 1024;
+
+/// Inclusive median.
+template <class RandomAccessIterator, class Comparator>
+RandomAccessIterator medianOf3(RandomAccessIterator Start,
+                               RandomAccessIterator End,
+                               const Comparator &Comp) {
+  RandomAccessIterator Mid = Start + (std::distance(Start, End) / 2);
+  return Comp(*Start, *(End - 1))
+             ? (Comp(*Mid, *(End - 1)) ? (Comp(*Start, *Mid) ? Mid : Start)
+                                       : End - 1)
+             : (Comp(*Mid, *Start) ? (Comp(*(End - 1), *Mid) ? Mid : End - 1)
+                                   : Start);
+}
+
+template <class RandomAccessIterator, class Comparator>
+void parallel_quick_sort(RandomAccessIterator Start, RandomAccessIterator End,
+                         const Comparator &Comp, TaskGroup &TG, size_t Depth) {
+  // Do a sequential sort for small inputs.
+  if (std::distance(Start, End) < detail::MinParallelSize || Depth == 0) {
+    llvm::sort(Start, End, Comp);
+    return;
+  }
+
+  // Partition.
+  auto Pivot = medianOf3(Start, End, Comp);
+  // Move Pivot to End.
+  std::swap(*(End - 1), *Pivot);
+  Pivot = std::partition(Start, End - 1, [&Comp, End](decltype(*Start) V) {
+    return Comp(V, *(End - 1));
+  });
+  // Move Pivot to middle of partition.
+  std::swap(*Pivot, *(End - 1));
+
+  // Recurse.
+  TG.spawn([=, &Comp, &TG] {
+    parallel_quick_sort(Start, Pivot, Comp, TG, Depth - 1);
+  });
+  parallel_quick_sort(Pivot + 1, End, Comp, TG, Depth - 1);
+}
+
+template <class RandomAccessIterator, class Comparator>
+void parallel_sort(RandomAccessIterator Start, RandomAccessIterator End,
+                   const Comparator &Comp) {
+  TaskGroup TG;
+  parallel_quick_sort(Start, End, Comp, TG,
+                      llvm::Log2_64(std::distance(Start, End)) + 1);
+}
+
+// TaskGroup has a relatively high overhead, so we want to reduce
+// the number of spawn() calls. We'll create up to 1024 tasks here.
+// (Note that 1024 is an arbitrary number. This code probably needs
+// improving to take the number of available cores into account.)
+enum { MaxTasksPerGroup = 1024 };
+
+template <class IterTy, class FuncTy>
+void parallel_for_each(IterTy Begin, IterTy End, FuncTy Fn) {
+  // Limit the number of tasks to MaxTasksPerGroup to limit job scheduling
+  // overhead on large inputs.
+  ptrdiff_t TaskSize = std::distance(Begin, End) / MaxTasksPerGroup;
+  if (TaskSize == 0)
+    TaskSize = 1;
+
+  TaskGroup TG;
+  while (TaskSize < std::distance(Begin, End)) {
+    TG.spawn([=, &Fn] { std::for_each(Begin, Begin + TaskSize, Fn); });
+    Begin += TaskSize;
+  }
+  std::for_each(Begin, End, Fn);
+}
+
+template <class IndexTy, class FuncTy>
+void parallel_for_each_n(IndexTy Begin, IndexTy End, FuncTy Fn) {
+  // Limit the number of tasks to MaxTasksPerGroup to limit job scheduling
+  // overhead on large inputs.
+  ptrdiff_t TaskSize = (End - Begin) / MaxTasksPerGroup;
+  if (TaskSize == 0)
+    TaskSize = 1;
+
+  TaskGroup TG;
+  IndexTy I = Begin;
+  for (; I + TaskSize < End; I += TaskSize) {
+    TG.spawn([=, &Fn] {
+      for (IndexTy J = I, E = I + TaskSize; J != E; ++J)
+        Fn(J);
+    });
+  }
+  for (IndexTy J = I; J < End; ++J)
+    Fn(J);
+}
+
+template <class IterTy, class ResultTy, class ReduceFuncTy,
+          class TransformFuncTy>
+ResultTy parallel_transform_reduce(IterTy Begin, IterTy End, ResultTy Init,
+                                   ReduceFuncTy Reduce,
+                                   TransformFuncTy Transform) {
+  // Limit the number of tasks to MaxTasksPerGroup to limit job scheduling
+  // overhead on large inputs.
+  size_t NumInputs = std::distance(Begin, End);
+  if (NumInputs == 0)
+    return std::move(Init);
+  size_t NumTasks = std::min(static_cast<size_t>(MaxTasksPerGroup), NumInputs);
+  std::vector<ResultTy> Results(NumTasks, Init);
+  {
+    // Each task processes either TaskSize or TaskSize+1 inputs. Any inputs
+    // remaining after dividing them equally amongst tasks are distributed as
+    // one extra input over the first tasks.
+    TaskGroup TG;
+    size_t TaskSize = NumInputs / NumTasks;
+    size_t RemainingInputs = NumInputs % NumTasks;
+    IterTy TBegin = Begin;
+    for (size_t TaskId = 0; TaskId < NumTasks; ++TaskId) {
+      IterTy TEnd = TBegin + TaskSize + (TaskId < RemainingInputs ? 1 : 0);
+      TG.spawn([=, &Transform, &Reduce, &Results] {
+        // Reduce the result of transformation eagerly within each task.
+        ResultTy R = Init;
+        for (IterTy It = TBegin; It != TEnd; ++It)
+          R = Reduce(R, Transform(*It));
+        Results[TaskId] = R;
+      });
+      TBegin = TEnd;
+    }
+    assert(TBegin == End);
+  }
+
+  // Do a final reduction. There are at most 1024 tasks, so this only adds
+  // constant single-threaded overhead for large inputs. Hopefully most
+  // reductions are cheaper than the transformation.
+  ResultTy FinalResult = std::move(Results.front());
+  for (ResultTy &PartialResult :
+       makeMutableArrayRef(Results.data() + 1, Results.size() - 1))
+    FinalResult = Reduce(FinalResult, std::move(PartialResult));
+  return std::move(FinalResult);
+}
+
+#endif
+
+} // namespace detail
+} // namespace parallel
+
+template <class RandomAccessIterator,
+          class Comparator = std::less<
+              typename std::iterator_traits<RandomAccessIterator>::value_type>>
+void parallelSort(RandomAccessIterator Start, RandomAccessIterator End,
+                  const Comparator &Comp = Comparator()) {
+#if LLVM_ENABLE_THREADS
+  if (parallel::strategy.ThreadsRequested != 1) {
+    parallel::detail::parallel_sort(Start, End, Comp);
+    return;
+  }
+#endif
+  llvm::sort(Start, End, Comp);
+}
+
+template <class IterTy, class FuncTy>
+void parallelForEach(IterTy Begin, IterTy End, FuncTy Fn) {
+#if LLVM_ENABLE_THREADS
+  if (parallel::strategy.ThreadsRequested != 1) {
+    parallel::detail::parallel_for_each(Begin, End, Fn);
+    return;
+  }
+#endif
+  std::for_each(Begin, End, Fn);
+}
+
+template <class FuncTy>
+void parallelForEachN(size_t Begin, size_t End, FuncTy Fn) {
+#if LLVM_ENABLE_THREADS
+  if (parallel::strategy.ThreadsRequested != 1) {
+    parallel::detail::parallel_for_each_n(Begin, End, Fn);
+    return;
+  }
+#endif
+  for (size_t I = Begin; I != End; ++I)
+    Fn(I);
+}
+
+template <class IterTy, class ResultTy, class ReduceFuncTy,
+          class TransformFuncTy>
+ResultTy parallelTransformReduce(IterTy Begin, IterTy End, ResultTy Init,
+                                 ReduceFuncTy Reduce,
+                                 TransformFuncTy Transform) {
+#if LLVM_ENABLE_THREADS
+  if (parallel::strategy.ThreadsRequested != 1) {
+    return parallel::detail::parallel_transform_reduce(Begin, End, Init, Reduce,
+                                                       Transform);
+  }
+#endif
+  for (IterTy I = Begin; I != End; ++I)
+    Init = Reduce(std::move(Init), Transform(*I));
+  return std::move(Init);
+}
+
+// Range wrappers.
+template <class RangeTy,
+          class Comparator = std::less<decltype(*std::begin(RangeTy()))>>
+void parallelSort(RangeTy &&R, const Comparator &Comp = Comparator()) {
+  parallelSort(std::begin(R), std::end(R), Comp);
+}
+
+template <class RangeTy, class FuncTy>
+void parallelForEach(RangeTy &&R, FuncTy Fn) {
+  parallelForEach(std::begin(R), std::end(R), Fn);
+}
+
+template <class RangeTy, class ResultTy, class ReduceFuncTy,
+          class TransformFuncTy>
+ResultTy parallelTransformReduce(RangeTy &&R, ResultTy Init,
+                                 ReduceFuncTy Reduce,
+                                 TransformFuncTy Transform) {
+  return parallelTransformReduce(std::begin(R), std::end(R), Init, Reduce,
+                                 Transform);
+}
+
+// Parallel for-each, but with error handling.
+template <class RangeTy, class FuncTy>
+Error parallelForEachError(RangeTy &&R, FuncTy Fn) {
+  // The transform_reduce algorithm requires that the initial value be copyable.
+  // Error objects are uncopyable. We only need to copy initial success values,
+  // so work around this mismatch via the C API. The C API represents success
+  // values with a null pointer. The joinErrors discards null values and joins
+  // multiple errors into an ErrorList.
+  return unwrap(parallelTransformReduce(
+      std::begin(R), std::end(R), wrap(Error::success()),
+      [](LLVMErrorRef Lhs, LLVMErrorRef Rhs) {
+        return wrap(joinErrors(unwrap(Lhs), unwrap(Rhs)));
+      },
+      [&Fn](auto &&V) { return wrap(Fn(V)); }));
+}
+
+} // namespace llvm
+
+#endif // LLVM_SUPPORT_PARALLEL_H
+
+#ifdef __GNUC__
+#pragma GCC diagnostic pop
+#endif