diff --git a/Eigen/src/Core/NumTraits.h b/Eigen/src/Core/NumTraits.h
index 09a70b713..af49abeb8 100644
--- a/Eigen/src/Core/NumTraits.h
+++ b/Eigen/src/Core/NumTraits.h
@@ -166,7 +166,16 @@ template<> struct NumTraits<double> : GenericNumTraits<double>
 template<> struct NumTraits<long double>
   : GenericNumTraits<long double>
 {
-  static inline long double dummy_precision() { return 1e-15l; }
+  static inline long double dummy_precision() { return static_cast<long double>(1e-15l); }
+
+#if defined(EIGEN_ARCH_PPC) && (__LDBL_MANT_DIG__ == 106)
+  // PowerPC double double causes issues with some values
+  static inline long double epsilon()
+  {
+    // 2^(-(__LDBL_MANT_DIG__)+1)
+    return static_cast<long double>(2.4651903288156618919116517665087e-32l);
+  }
+#endif
 };
 
 template<typename _Real> struct NumTraits<std::complex<_Real> >
diff --git a/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h b/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
index a3273da4e..dc8e6322f 100644
--- a/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
+++ b/unsupported/Eigen/src/MatrixFunctions/MatrixPower.h
@@ -282,7 +282,7 @@ inline int MatrixPowerAtomic<MatrixType>::getPadeDegree(long double normIminusT)
 #endif
   int degree = 3;
   for (; degree <= maxPadeDegree; ++degree)
-    if (normIminusT <= maxNormForPade[degree - 3])
+    if (normIminusT <= static_cast<long double>(maxNormForPade[degree - 3]))
       break;
   return degree;
 }
diff --git a/unsupported/test/cxx11_tensor_reduction.cpp b/unsupported/test/cxx11_tensor_reduction.cpp
index 1490ec3da..4030e31cf 100644
--- a/unsupported/test/cxx11_tensor_reduction.cpp
+++ b/unsupported/test/cxx11_tensor_reduction.cpp
@@ -484,7 +484,39 @@ static void test_reduce_middle_dims() {
   }
 }
 
+<<<<<<< HEAD
 void test_cxx11_tensor_reduction() {
+=======
+template <typename ScalarType, int num_elements, int max_mean>
+void test_sum_accuracy() {
+  Tensor<double, 1> double_tensor(num_elements);
+  Tensor<ScalarType, 1> tensor(num_elements);
+  for (double prescribed_mean = 0; prescribed_mean <= max_mean; prescribed_mean = numext::maxi(1.0, prescribed_mean*3.99)) {
+    // FIXME: NormalRandomGenerator doesn't work in bfloat and half.
+    double_tensor.setRandom<Eigen::internal::NormalRandomGenerator<double>>();
+    double_tensor += double_tensor.constant(prescribed_mean);
+    tensor = double_tensor.cast<ScalarType>();
+
+    Tensor<ScalarType, 0> sum;
+    sum = tensor.sum();
+
+    // Compute the reference value in double precsion.
+    double expected_sum = 0.0;
+    double abs_sum = 0.0;
+    for (int i = 0; i < num_elements; ++i) {
+      expected_sum += static_cast<double>(tensor(i));
+      abs_sum += static_cast<double>(numext::abs(tensor(i)));
+    }
+    // Test against probabilistic forward error bound. In reality, the error is much smaller
+    // when we use tree summation.
+    double err = Eigen::numext::abs(static_cast<double>(sum()) - expected_sum);
+    double tol = numext::sqrt(static_cast<double>(num_elements)) * NumTraits<ScalarType>::epsilon() * static_cast<ScalarType>(abs_sum);
+    VERIFY_LE(err, tol);
+  }
+}
+
+EIGEN_DECLARE_TEST(cxx11_tensor_reduction) {
+>>>>>>> 54459214a (Fix epsilon and dummy_precision values in long double for double doubles.  Prevented some algorithms from converging on PPC.)
   CALL_SUBTEST(test_trivial_reductions<ColMajor>());
   CALL_SUBTEST(test_trivial_reductions<RowMajor>());
   CALL_SUBTEST(test_simple_reductions<ColMajor>());