diff options
author | Rasmus Munk Larsen <rmlarsen@google.com> | 2016-05-18 15:09:48 -0700 |
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committer | Rasmus Munk Larsen <rmlarsen@google.com> | 2016-05-18 15:09:48 -0700 |
commit | 7df811cfe5d0047658de1cb4522c9c00d211b059 (patch) | |
tree | 19241e8c671fd00717ad6b9f96172098b0d15d20 /unsupported/Eigen/CXX11/src | |
parent | 86ae94462e7a8a6ee87303fb558ac8c90349797d (diff) |
Minor cleanups: 1. Get rid of unused variables. 2. Get rid of last uses of EIGEN_USE_COST_MODEL.
Diffstat (limited to 'unsupported/Eigen/CXX11/src')
4 files changed, 6 insertions, 27 deletions
diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h index 88d485f38..98fe6f542 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorContractionThreadPool.h @@ -568,10 +568,6 @@ struct TensorEvaluator<const TensorContractionOp<Indices, LeftArgType, RightArgT (parallel_pack_ ? nm_ + nn_ : (shard_by_col_ ? nn_ : nm_)) + nm_ * nn_; if (k < nk_) { - // It is important to copy out nm_ and nn_, because once we kick off - // the last packing operation this and device_ can be destroyed. - Index nm = nm_; - Index nn = nn_; // Issue lhs/rhs packing. Their completion will in turn kick off // kernels. if (parallel_pack_) { diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h index cb6fb4626..a76c8ca35 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorCostModel.h @@ -10,9 +10,6 @@ #ifndef EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H #define EIGEN_CXX11_TENSOR_TENSOR_COST_MODEL_H -// Turn on the cost model by default -#define EIGEN_USE_COST_MODEL - namespace Eigen { /** \class TensorEvaluator diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h index 868398753..2f1acd321 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h @@ -150,9 +150,8 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> { 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()); -#if !defined(EIGEN_USE_SIMPLE_THREAD_POOL) && defined(EIGEN_USE_COST_MODEL) +#if !defined(EIGEN_USE_SIMPLE_THREAD_POOL) device.parallelFor(size, evaluator.costPerCoeff(Vectorizable), EvalRange<Evaluator, Index, Vectorizable>::alignBlockSize, [&evaluator](Index first, Index last) { @@ -160,15 +159,14 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> { }); #else size_t 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 { + const Index PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1; 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; @@ -185,7 +183,7 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> { } barrier.Wait(); } -#endif // defined(EIGEN_USE_NONBLOCKING_THREAD_POOL) && defined(EIGEN_USE_COST_MODEL) +#endif } evaluator.cleanup(); } diff --git a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h index 2a8047b7d..8b10c1120 100644 --- a/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h +++ b/unsupported/Eigen/CXX11/src/Tensor/TensorReduction.h @@ -248,16 +248,12 @@ struct FullReducer<Self, Op, ThreadPoolDevice, Vectorizable> { *output = reducer.finalize(reducer.initialize()); return; } -#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, Vectorizable>::reduce(self, 0, num_coeffs, reducer); @@ -472,22 +468,14 @@ struct TensorEvaluator<const TensorReductionOp<Op, Dims, ArgType>, Device> 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 && + if (RunningFullReduction && + internal::FullReducer<Self, Op, Device>::HasOptimizedImplementation && ((RunningOnGPU && (m_device.majorDeviceVersion() >= 3)) || - (!RunningOnGPU && size_large_enough(internal::array_prod(m_impl.dimensions()))))) { - + !RunningOnGPU)) { bool need_assign = false; if (!data) { m_result = static_cast<CoeffReturnType*>(m_device.allocate(sizeof(CoeffReturnType))); |