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Add paralell_for and use it where appropriate.
When we iterate over numbers as opposed to iterable elements, parallel_for fits better than parallel_for_each. llvm-svn: 288002
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@ -334,16 +334,14 @@ void BuildIdSection<ELFT>::computeHash(
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std::vector<ArrayRef<uint8_t>> Chunks = split(Data, 1024 * 1024);
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std::vector<uint8_t> Hashes(Chunks.size() * HashSize);
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auto Fn = [&](ArrayRef<uint8_t> &Chunk) {
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size_t Idx = &Chunk - Chunks.data();
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HashFn(Hashes.data() + Idx * HashSize, Chunk);
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};
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auto Fn = [&](size_t I) { HashFn(Hashes.data() + I * HashSize, Chunks[I]); };
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// Compute hash values.
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if (Config->Threads)
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parallel_for_each(Chunks.begin(), Chunks.end(), Fn);
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parallel_for(size_t(0), Chunks.size(), Fn);
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else
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std::for_each(Chunks.begin(), Chunks.end(), Fn);
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for (size_t I = 0, E = Chunks.size(); I != E; ++I)
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Fn(I);
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// Write to the final output buffer.
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HashFn(HashBuf, Hashes);
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@ -270,33 +270,63 @@ template <class T> void parallel_sort(T *start, T *end) {
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}
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#if !defined(LLVM_ENABLE_THREADS) || LLVM_ENABLE_THREADS == 0
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template <class Iterator, class Func>
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void parallel_for_each(Iterator begin, Iterator end, Func func) {
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std::for_each(begin, end, func);
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template <class IterTy, class FuncTy>
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void parallel_for_each(IterTy Begin, IterTy End, FuncTy Fn) {
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std::for_each(Begin, End, Fn);
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}
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template <class IndexTy, class FuncTy>
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void parallel_for(IndexTy Begin, IndexTy End, FuncTy Fn) {
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for (IndexTy I = Begin; I != End; ++I)
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Fn(I);
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}
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#elif defined(_MSC_VER)
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// Use ppl parallel_for_each on Windows.
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template <class Iterator, class Func>
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void parallel_for_each(Iterator begin, Iterator end, Func func) {
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concurrency::parallel_for_each(begin, end, func);
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template <class IterTy, class FuncTy>
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void parallel_for_each(IterTy Begin, IterTy End, FuncTy Fn) {
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concurrency::parallel_for_each(Begin, End, Fn);
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}
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template <class IndexTy, class FuncTy>
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void parallel_for(IndexTy Begin, IndexTy End, FuncTy Fn) {
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concurrency::parallel_for(Begin, End, Fn);
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}
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#else
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template <class Iterator, class Func>
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void parallel_for_each(Iterator begin, Iterator end, Func func) {
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template <class IterTy, class FuncTy>
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void parallel_for_each(IterTy Begin, IterTy End, FuncTy Fn) {
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// TaskGroup has a relatively high overhead, so we want to reduce
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// the number of spawn() calls. We'll create up to 1024 tasks here.
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// (Note that 1024 is an arbitrary number. This code probably needs
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// improving to take the number of available cores into account.)
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ptrdiff_t taskSize = std::distance(begin, end) / 1024;
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if (taskSize == 0)
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taskSize = 1;
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ptrdiff_t TaskSize = std::distance(Begin, End) / 1024;
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if (TaskSize == 0)
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TaskSize = 1;
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TaskGroup tg;
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while (taskSize <= std::distance(begin, end)) {
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tg.spawn([=, &func] { std::for_each(begin, begin + taskSize, func); });
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begin += taskSize;
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TaskGroup Tg;
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while (TaskSize <= std::distance(Begin, End)) {
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Tg.spawn([=, &Fn] { std::for_each(Begin, Begin + TaskSize, Fn); });
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Begin += TaskSize;
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}
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std::for_each(begin, end, func);
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std::for_each(Begin, End, Fn);
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}
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template <class IndexTy, class FuncTy>
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void parallel_for(IndexTy Begin, IndexTy End, FuncTy Fn) {
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ptrdiff_t TaskSize = (End - Begin) / 1024;
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if (TaskSize == 0)
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TaskSize = 1;
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TaskGroup Tg;
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IndexTy I = Begin;
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for (; I < End; I += TaskSize) {
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Tg.spawn([=, &Fn] {
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for (IndexTy J = I, E = I + TaskSize; J != E; ++J)
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Fn(J);
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});
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Begin += TaskSize;
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}
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for (; I < End; ++I)
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Fn(I);
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}
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#endif
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} // end namespace lld
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