Merged in rmlarsen/eigen (pull request PR-178) Eigen Tensor cost model part 2: Thread scheduling for standard evaluators and reductions.
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h index 32bc5d0..4e8f866 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h
@@ -10,9 +10,9 @@ #ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H #define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H -#if !defined(EIGEN_USE_GPU) -#define EIGEN_USE_COST_MODEL -#endif +//#if !defined(EIGEN_USE_GPU) +//#define EIGEN_USE_COST_MODEL +//#endif namespace Eigen {
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h index f1f9a90..2930126 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorEvaluator.h
@@ -189,6 +189,11 @@ return loadConstant(m_data+index); } + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorOpCost costPerCoeff(bool vectorized) const { + return TensorOpCost(sizeof(CoeffReturnType), 0, 0, vectorized, + internal::unpacket_traits<PacketReturnType>::size); + } + EIGEN_DEVICE_FUNC const Scalar* data() const { return m_data; } protected:
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h index eabfd91..7a54f7a 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h
@@ -59,9 +59,16 @@ { const Index size = array_prod(evaluator.dimensions()); const int PacketSize = unpacket_traits<typename TensorEvaluator<Expression, DefaultDevice>::PacketReturnType>::size; + // Manually unroll this loop since compilers don't do it. + const Index UnrolledSize = (size / (4 * PacketSize)) * 4 * PacketSize; + for (Index i = 0; i < UnrolledSize; i += 4*PacketSize) { + evaluator.evalPacket(i); + evaluator.evalPacket(i+PacketSize); + evaluator.evalPacket(i+2*PacketSize); + evaluator.evalPacket(i+3*PacketSize); + } const Index VectorizedSize = (size / PacketSize) * PacketSize; - - for (Index i = 0; i < VectorizedSize; i += PacketSize) { + for (Index i = UnrolledSize; i < VectorizedSize; i += PacketSize) { evaluator.evalPacket(i); } for (Index i = VectorizedSize; i < size; ++i) { @@ -78,8 +85,9 @@ #ifdef EIGEN_USE_THREADS template <typename Evaluator, typename Index, bool Vectorizable> struct EvalRange { - static void run(Evaluator evaluator, const Index first, const Index last) { - eigen_assert(last > first); + static void run(Evaluator* evaluator_in, const Index first, const Index last) { + Evaluator evaluator = *evaluator_in; + eigen_assert(last >= first); for (Index i = first; i < last; ++i) { evaluator.evalScalar(i); } @@ -88,28 +96,34 @@ template <typename Evaluator, typename Index> struct EvalRange<Evaluator, Index, true> { - static void run(Evaluator evaluator, const Index first, const Index last) { - eigen_assert(last > first); - + static void run(Evaluator* evaluator_in, const Index first, const Index last) { + Evaluator evaluator = *evaluator_in; + eigen_assert(last >= first); Index i = first; - static const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size; + const int PacketSize = unpacket_traits<typename Evaluator::PacketReturnType>::size; if (last - first >= PacketSize) { eigen_assert(first % PacketSize == 0); - Index lastPacket = last - (last % PacketSize); - for (; i < lastPacket; i += PacketSize) { + Index last_chunk_offset = last - 4 * PacketSize; + // Manually unroll this loop since compilers don't do it. + for (; i <= last_chunk_offset; i += 4*PacketSize) { + evaluator.evalPacket(i); + evaluator.evalPacket(i+PacketSize); + evaluator.evalPacket(i+2*PacketSize); + evaluator.evalPacket(i+3*PacketSize); + } + last_chunk_offset = last - PacketSize; + for (; i <= last_chunk_offset; i += PacketSize) { evaluator.evalPacket(i); } } - for (; i < last; ++i) { evaluator.evalScalar(i); } } }; -template<typename Expression, bool Vectorizable> -class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> -{ +template <typename Expression, bool Vectorizable> +class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> { public: typedef typename Expression::Index Index; static inline void run(const Expression& expr, const ThreadPoolDevice& device) @@ -119,24 +133,34 @@ const bool needs_assign = evaluator.evalSubExprsIfNeeded(NULL); if (needs_assign) { + const Index PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1; const Index size = array_prod(evaluator.dimensions()); - - static const int PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1; - - int blocksz = std::ceil<int>(static_cast<float>(size)/device.numThreads()) + PacketSize - 1; - const Index blocksize = numext::maxi<Index>(PacketSize, (blocksz - (blocksz % PacketSize))); - const unsigned int numblocks = static_cast<unsigned int>(size / blocksize); - - Barrier barrier(numblocks); - for (unsigned int i = 0; i < numblocks; ++i) { - device.enqueue_with_barrier(&barrier, &EvalRange<Evaluator, Index, Vectorizable>::run, evaluator, i*blocksize, (i+1)*blocksize); + int num_threads = device.numThreads(); +#ifdef EIGEN_USE_COST_MODEL + if (num_threads > 1) { + num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( + size, evaluator.costPerCoeff(Vectorizable), num_threads); } +#endif + if (num_threads == 1) { + EvalRange<Evaluator, Index, Vectorizable>::run(&evaluator, 0, size); + } else { + Index blocksz = std::ceil<Index>(static_cast<float>(size)/num_threads) + PacketSize - 1; + const Index blocksize = numext::maxi<Index>(PacketSize, (blocksz - (blocksz % PacketSize))); + const Index numblocks = size / blocksize; - if (static_cast<Index>(numblocks) * blocksize < size) { - EvalRange<Evaluator, Index, Vectorizable>::run(evaluator, numblocks * blocksize, size); + Barrier barrier(numblocks); + for (int i = 0; i < numblocks; ++i) { + device.enqueue_with_barrier( + &barrier, &EvalRange<Evaluator, Index, Vectorizable>::run, + &evaluator, i * blocksize, (i + 1) * blocksize); + } + if (numblocks * blocksize < size) { + EvalRange<Evaluator, Index, Vectorizable>::run( + &evaluator, numblocks * blocksize, size); + } + barrier.Wait(); } - - barrier.Wait(); } evaluator.cleanup(); } @@ -226,7 +250,6 @@ #endif // __CUDACC__ #endif // EIGEN_USE_GPU - } // end namespace internal } // end namespace Eigen
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h index 1c9e7ab..885295f 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h
@@ -214,7 +214,7 @@ static EIGEN_DEVICE_FUNC void run(const Self& self, Op& reducer, const Device&, typename Self::CoeffReturnType* output) { const typename Self::Index num_coeffs = array_prod(self.m_impl.dimensions()); - *output = InnerMostDimReducer<Self, Op>::reduce(self, 0, num_coeffs, reducer); + *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); } }; @@ -222,18 +222,19 @@ #ifdef EIGEN_USE_THREADS // Multithreaded full reducers template <typename Self, typename Op, - bool vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> + bool Vectorizable = (Self::InputPacketAccess & Op::PacketAccess)> struct FullReducerShard { static EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void run(const Self& self, typename Self::Index firstIndex, typename Self::Index numValuesToReduce, Op& reducer, typename Self::CoeffReturnType* output) { - *output = InnerMostDimReducer<Self, Op, vectorizable>::reduce( + *output = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( self, firstIndex, numValuesToReduce, reducer); } }; -template <typename Self, typename Op> -struct FullReducer<Self, Op, ThreadPoolDevice, false> { +// Multithreaded full reducer +template <typename Self, typename Op, bool Vectorizable> +struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> { static const bool HasOptimizedImplementation = !Op::IsStateful; static const int PacketSize = unpacket_traits<typename Self::PacketReturnType>::size; @@ -247,79 +248,44 @@ *output = reducer.finalize(reducer.initialize()); return; } - const std::size_t num_threads = device.numThreads(); +#ifdef EIGEN_USE_COST_MODEL + const TensorOpCost cost = + self.m_impl.costPerCoeff(Vectorizable) + + TensorOpCost(0, 0, internal::functor_traits<Op>::Cost, Vectorizable, + PacketSize); + const int num_threads = TensorCostModel<ThreadPoolDevice>::numThreads( + num_coeffs, cost, device.numThreads()); +#else + const int num_threads = device.numThreads(); +#endif if (num_threads == 1) { - *output = InnerMostDimReducer<Self, Op, false>::reduce(self, 0, num_coeffs, reducer); - return; - } else { - const Index blocksize = std::floor<Index>(static_cast<float>(num_coeffs) / num_threads); - const unsigned int numblocks = blocksize > 0 ? static_cast<unsigned int>(num_coeffs / blocksize) : 0; - eigen_assert(num_coeffs >= static_cast<Index>(numblocks) * blocksize); - - Barrier barrier(numblocks); - MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize()); - for (unsigned int i = 0; i < numblocks; ++i) { - device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, false>::run, self, - i * blocksize, blocksize, reducer, &shards[i]); - } - - typename Self::CoeffReturnType finalShard; - if (static_cast<Index>(numblocks) * blocksize < num_coeffs) { - finalShard = InnerMostDimReducer<Self, Op, false>::reduce( - self, numblocks * blocksize, num_coeffs - numblocks * blocksize, reducer); - } else { - finalShard = reducer.initialize(); - } - barrier.Wait(); - for (unsigned int i = 0; i < numblocks; ++i) { - reducer.reduce(shards[i], &finalShard); - } - *output = reducer.finalize(finalShard); - } - } -}; - -template <typename Self, typename Op> -struct FullReducer<Self, Op, ThreadPoolDevice, true> { - static const bool HasOptimizedImplementation = !Op::IsStateful; - static const int PacketSize = - unpacket_traits<typename Self::PacketReturnType>::size; - - // launch one reducer per thread and accumulate the result. - static void run(const Self& self, Op& reducer, const ThreadPoolDevice& device, - typename Self::CoeffReturnType* output) { - typedef typename Self::Index Index; - const Index num_coeffs = array_prod(self.m_impl.dimensions()); - if (num_coeffs == 0) { - *output = reducer.finalize(reducer.initialize()); + *output = + InnerMostDimReducer<Self, Op, Vectorizable>::reduce(self, 0, num_coeffs, reducer); return; } - const std::size_t num_threads = device.numThreads(); - if (num_threads == 1) { - *output = InnerMostDimReducer<Self, Op, true>::reduce(self, 0, num_coeffs, reducer); - return; - } - const Index blocksize = std::floor<Index>(static_cast<float>(num_coeffs) / num_threads); - const unsigned int numblocks = blocksize > 0 ? static_cast<unsigned int>(num_coeffs / blocksize) : 0; - eigen_assert(num_coeffs >= static_cast<Index>(numblocks) * blocksize); + const Index blocksize = + std::floor<Index>(static_cast<float>(num_coeffs) / num_threads); + const Index numblocks = blocksize > 0 ? num_coeffs / blocksize : 0; + eigen_assert(num_coeffs >= numblocks * blocksize); Barrier barrier(numblocks); MaxSizeVector<typename Self::CoeffReturnType> shards(numblocks, reducer.initialize()); - for (unsigned int i = 0; i < numblocks; ++i) { - device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, true>::run, + for (Index i = 0; i < numblocks; ++i) { + device.enqueue_with_barrier(&barrier, &FullReducerShard<Self, Op, Vectorizable>::run, self, i * blocksize, blocksize, reducer, &shards[i]); } typename Self::CoeffReturnType finalShard; - if (static_cast<Index>(numblocks) * blocksize < num_coeffs) { - finalShard = InnerMostDimReducer<Self, Op, true>::reduce( - self, numblocks * blocksize, num_coeffs - numblocks * blocksize, reducer); + if (numblocks * blocksize < num_coeffs) { + finalShard = InnerMostDimReducer<Self, Op, Vectorizable>::reduce( + self, numblocks * blocksize, num_coeffs - numblocks * blocksize, + reducer); } else { finalShard = reducer.initialize(); } - barrier.Wait(); - for (unsigned int i = 0; i < numblocks; ++i) { + + for (Index i = 0; i < numblocks; ++i) { reducer.reduce(shards[i], &finalShard); } *output = reducer.finalize(finalShard); @@ -498,13 +464,21 @@ EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } + static bool size_large_enough(Index total_size) { +#ifndef EIGEN_USE_COST_MODEL + return total_size > 1024 * 1024; +#else + return true || total_size; +#endif + } + EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool evalSubExprsIfNeeded(CoeffReturnType* data) { m_impl.evalSubExprsIfNeeded(NULL); // Use the FullReducer if possible. if (RunningFullReduction && internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation && ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || - (!RunningOnGPU && (internal::array_prod(m_impl.dimensions()) > 1024 * 1024)))) { + (!RunningOnGPU && size_large_enough(internal::array_prod(m_impl.dimensions()))))) { bool need_assign = false; if (!data) {