Improvements to parallelFor.

Move some scalar functors from TensorFunctors. to Eigen core.

Eigen/src/Core/functors/BinaryFunctors.h

@@ -591,6 +591,47 @@ template<typename Scalar>
 struct functor_traits<scalar_inverse_mult_op<Scalar> >
 { enum { PacketAccess = packet_traits<Scalar>::HasDiv, Cost = NumTraits<Scalar>::template Div<PacketAccess>::Cost }; };
 
+/** \internal
+ * \brief Template functor to compute the modulo between an array and a fixed scalar.
+ */
+template <typename Scalar>
+struct scalar_mod_op {
+  EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {}
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a % m_divisor; }
+  const Scalar m_divisor;
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::template Div<false>::Cost, PacketAccess = false }; };
+
+/** \internal
+ * \brief Template functor to compute the modulo between two arrays.
+ */
+template <typename Scalar>
+struct scalar_mod2_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_mod2_op);
+  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a, const Scalar& b) const { return a % b; }
+};
+template <typename Scalar>
+struct functor_traits<scalar_mod2_op<Scalar> >
+{ enum { Cost = NumTraits<Scalar>::template Div<false>::Cost, PacketAccess = false }; };
+
+/** \internal
+ * \brief Template functor to compute the float modulo between two arrays.
+ */
+template <typename Scalar>
+struct scalar_fmod_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op);
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar
+  operator()(const Scalar& a, const Scalar& b) const {
+    return numext::fmod(a, b);
+  }
+};
+template <typename Scalar>
+struct functor_traits<scalar_fmod_op<Scalar> > {
+  enum { Cost = 13,  // Reciprocal throughput of FPREM on Haswell.
+         PacketAccess = false };
+};
 
 } // end namespace internal
 

Eigen/src/Core/functors/UnaryFunctors.h

@@ -587,6 +587,33 @@ struct functor_traits<scalar_erfc_op<Scalar> >
   };
 };
 
+/** \internal
+  * \brief Template functor to compute the sigmoid of a scalar
+  * \sa class CwiseUnaryOp, ArrayBase::sigmoid()
+  */
+template <typename T>
+struct scalar_sigmoid_op {
+  EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_op)
+  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
+    const T one = T(1);
+    return one / (one + numext::exp(-x));
+  }
+
+  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
+  Packet packetOp(const Packet& x) const {
+    const Packet one = pset1<Packet>(T(1));
+    return pdiv(one, padd(one, pexp(pnegate(x))));
+  }
+};
+
+template <typename T>
+struct functor_traits<scalar_sigmoid_op<T> > {
+  enum {
+    Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6,
+    PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
+                   packet_traits<T>::HasNegate && packet_traits<T>::HasExp
+  };
+};
 
 /** \internal
   * \brief Template functor to compute the atan of a scalar

unsupported/Eigen/CXX11/Tensor

@@ -69,6 +69,7 @@ typedef unsigned __int64 uint64_t;
 #include "src/Tensor/TensorMacros.h"
 #include "src/Tensor/TensorForwardDeclarations.h"
 #include "src/Tensor/TensorMeta.h"
+#include "src/Tensor/TensorCostModel.h"
 #include "src/Tensor/TensorDeviceDefault.h"
 #include "src/Tensor/TensorDeviceThreadPool.h"
 #include "src/Tensor/TensorDeviceCuda.h"
@@ -83,7 +84,6 @@ typedef unsigned __int64 uint64_t;
 
 #include "src/Tensor/TensorBase.h"
 
-#include "src/Tensor/TensorCostModel.h"
 #include "src/Tensor/TensorEvaluator.h"
 #include "src/Tensor/TensorExpr.h"
 #include "src/Tensor/TensorReduction.h"

unsupported/Eigen/CXX11/src/Tensor/TensorDeviceThreadPool.h

@@ -172,37 +172,37 @@ struct ThreadPoolDevice {
     pool_->Schedule(func);
   }
 
-  // parallelFor executes f with [0, size) arguments in parallel and waits for
+  // parallelFor executes f with [0, n) arguments in parallel and waits for
-  // completion. Block size is choosen between min_block_size and
+  // completion. F accepts a half-open interval [first, last).
-  // 2 * min_block_size to achieve the best parallel efficiency.
+  // Block size is choosen based on the iteration cost and resulting parallel
-  // If min_block_size == -1, parallelFor uses block size of 1.
+  // efficiency. If block_align is not nullptr, it is called to round up the
-  // If hard_align > 0, block size is aligned to hard_align.
+  // block size.
-  // If soft_align > hard_align, block size is aligned to soft_align provided
+  void parallelFor(Index n, const TensorOpCost& cost,
-  // that it does not increase block size too much.
+                   std::function<Index(Index)> block_align,
-  void parallelFor(Index size, Index min_block_size, Index hard_align,
-                   Index soft_align,
                    std::function<void(Index, Index)> f) const {
-    if (size <= 1 || (min_block_size != -1 && size < min_block_size) ||
+    typedef TensorCostModel<ThreadPoolDevice> CostModel;
-        numThreads() == 1) {
+    if (n <= 1 || numThreads() == 1 ||
-      f(0, size);
+        CostModel::numThreads(n, cost, numThreads()) == 1) {
+      f(0, n);
       return;
     }
 
-    Index block_size = 1;
+    // Calculate block size based on (1) the iteration cost and (2) parallel
-    Index block_count = size;
-    if (min_block_size != -1) {
-      // Calculate block size based on (1) estimated cost and (2) parallel
     // efficiency. We want blocks to be not too small to mitigate
-      // parallelization overheads; not too large to mitigate tail effect and
+    // parallelization overheads; not too large to mitigate tail
-      // potential load imbalance and we also want number of blocks to be evenly
+    // effect and potential load imbalance and we also want number
-      // dividable across threads.
+    // of blocks to be evenly dividable across threads.
-      min_block_size = numext::maxi<Index>(min_block_size, 1);
+
-      block_size = numext::mini(min_block_size, size);
+    double block_size_f = 1.0 / CostModel::taskSize(1, cost);
-      // Upper bound on block size:
+    Index block_size = numext::mini(n, numext::maxi<Index>(1, block_size_f));
-      const Index max_block_size = numext::mini(min_block_size * 2, size);
+    const Index max_block_size =
-      block_size = numext::mini(
+        numext::mini(n, numext::maxi<Index>(1, 2 * block_size_f));
-          alignBlockSize(block_size, hard_align, soft_align), size);
+    if (block_align) {
-      block_count = divup(size, block_size);
+      Index new_block_size = block_align(block_size);
+      eigen_assert(new_block_size >= block_size);
+      block_size = numext::mini(n, new_block_size);
+    }
+    Index block_count = divup(n, block_size);
     // Calculate parallel efficiency as fraction of total CPU time used for
     // computations:
     double max_efficiency =
@@ -213,14 +213,17 @@ struct ThreadPoolDevice {
     for (Index prev_block_count = block_count; prev_block_count > 1;) {
       // This is the next block size that divides size into a smaller number
       // of blocks than the current block_size.
-        Index coarser_block_size = divup(size, prev_block_count - 1);
+      Index coarser_block_size = divup(n, prev_block_count - 1);
-        coarser_block_size =
+      if (block_align) {
-            alignBlockSize(coarser_block_size, hard_align, soft_align);
+        Index new_block_size = block_align(coarser_block_size);
+        eigen_assert(new_block_size >= coarser_block_size);
+        coarser_block_size = numext::mini(n, new_block_size);
+      }
       if (coarser_block_size > max_block_size) {
         break;  // Reached max block size. Stop.
       }
       // Recalculate parallel efficiency.
-        const Index coarser_block_count = divup(size, coarser_block_size);
+      const Index coarser_block_count = divup(n, coarser_block_size);
       eigen_assert(coarser_block_count < prev_block_count);
       prev_block_count = coarser_block_count;
       const double coarser_efficiency =
@@ -235,7 +238,6 @@ struct ThreadPoolDevice {
         }
       }
     }
-    }
 
     // Recursively divide size into halves until we reach block_size.
     // Division code rounds mid to block_size, so we are guaranteed to get
@@ -251,26 +253,20 @@ struct ThreadPoolDevice {
       }
       // Split into halves and submit to the pool.
       Index mid = first + divup((last - first) / 2, block_size) * block_size;
-      pool_->Schedule([=, &handleRange]() { handleRange(mid, last); });
+      enqueue_func([=, &handleRange]() { handleRange(mid, last); });
-      pool_->Schedule([=, &handleRange]() { handleRange(first, mid); });
+      enqueue_func([=, &handleRange]() { handleRange(first, mid); });
     };
-    handleRange(0, size);
+    handleRange(0, n);
     barrier.Wait();
   }
 
+  // Convinience wrapper for parallelFor that does not align blocks.
+  void parallelFor(Index n, const TensorOpCost& cost,
+                   std::function<void(Index, Index)> f) const {
+    parallelFor(n, cost, nullptr, std::move(f));
+  }
+
  private:
-  static Index alignBlockSize(Index size, Index hard_align, Index soft_align) {
-    if (soft_align > hard_align && size >= 4 * soft_align) {
-      // Align to soft_align, if it won't increase size by more than 25%.
-      return (size + soft_align - 1) & ~(soft_align - 1);
-    }
-    if (hard_align > 0) {
-      return (size + hard_align - 1) & ~(hard_align - 1);
-    }
-    return size;
-  }
-
-
   ThreadPoolInterface* pool_;
   size_t num_threads_;
 };

unsupported/Eigen/CXX11/src/Tensor/TensorExecutor.h

@@ -137,6 +137,13 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> {
     {
       const Index PacketSize = Vectorizable ? unpacket_traits<typename Evaluator::PacketReturnType>::size : 1;
       const Index size = array_prod(evaluator.dimensions());
+#if defined(EIGEN_USE_NONBLOCKING_THREAD_POOL) && defined(EIGEN_USE_COST_MODEL)
+      device.parallelFor(size, evaluator.costPerCoeff(Vectorizable),
+                         EvalRange::alignBlockSize,
+                         [&evaluator](Index first, Index last) {
+                           EvalRange::run(&evaluator, first, last);
+                         });
+#else
       size_t num_threads = device.numThreads();
 #ifdef EIGEN_USE_COST_MODEL
       if (num_threads > 1) {
@@ -163,11 +170,12 @@ class TensorExecutor<Expression, ThreadPoolDevice, Vectorizable> {
         }
         barrier.Wait();
       }
+#endif  // EIGEN_USE_NONBLOCKING_THREAD_POOL
     }
     evaluator.cleanup();
   }
 };
-#endif
+#endif  // EIGEN_USE_THREADS
 
 
 // GPU: the evaluation of the expression is offloaded to a GPU.

unsupported/Eigen/CXX11/src/Tensor/TensorFunctors.h

@@ -13,77 +13,6 @@
 namespace Eigen {
 namespace internal {
 
-
-/** \internal
- * \brief Template functor to compute the modulo between an array and a scalar.
- */
-template <typename Scalar>
-struct scalar_mod_op {
-  EIGEN_DEVICE_FUNC scalar_mod_op(const Scalar& divisor) : m_divisor(divisor) {}
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a) const { return a % m_divisor; }
-  const Scalar m_divisor;
-};
-template <typename Scalar>
-struct functor_traits<scalar_mod_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::template Div<false>::Cost, PacketAccess = false }; };
-
-
-/** \internal
- * \brief Template functor to compute the modulo between 2 arrays.
- */
-template <typename Scalar>
-struct scalar_mod2_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_mod2_op);
-  EIGEN_DEVICE_FUNC inline Scalar operator() (const Scalar& a, const Scalar& b) const { return a % b; }
-};
-template <typename Scalar>
-struct functor_traits<scalar_mod2_op<Scalar> >
-{ enum { Cost = NumTraits<Scalar>::template Div<false>::Cost, PacketAccess = false }; };
-
-template <typename Scalar>
-struct scalar_fmod_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_fmod_op);
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Scalar
-  operator()(const Scalar& a, const Scalar& b) const {
-    return numext::fmod(a, b);
-  }
-};
-template <typename Scalar>
-struct functor_traits<scalar_fmod_op<Scalar> > {
-  enum { Cost = 13,  // Reciprocal throughput of FPREM on Haswell.
-         PacketAccess = false };
-};
-
-
-/** \internal
-  * \brief Template functor to compute the sigmoid of a scalar
-  * \sa class CwiseUnaryOp, ArrayBase::sigmoid()
-  */
-template <typename T>
-struct scalar_sigmoid_op {
-  EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_op)
-  EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& x) const {
-    const T one = T(1);
-    return one / (one + numext::exp(-x));
-  }
-
-  template <typename Packet> EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE
-  Packet packetOp(const Packet& x) const {
-    const Packet one = pset1<Packet>(T(1));
-    return pdiv(one, padd(one, pexp(pnegate(x))));
-  }
-};
-
-template <typename T>
-struct functor_traits<scalar_sigmoid_op<T> > {
-  enum {
-    Cost = NumTraits<T>::AddCost * 2 + NumTraits<T>::MulCost * 6,
-    PacketAccess = packet_traits<T>::HasAdd && packet_traits<T>::HasDiv &&
-                   packet_traits<T>::HasNegate && packet_traits<T>::HasExp
-  };
-};
-
-
 // Standard reduction functors
 template <typename T> struct SumReducer
 {