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diff --git a/bench/tensors/tensor_benchmarks.h b/bench/tensors/tensor_benchmarks.h
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+++ b/bench/tensors/tensor_benchmarks.h
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+#ifndef THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+#define THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_
+
+typedef int TensorIndex;
+#define EIGEN_DEFAULT_DENSE_INDEX_TYPE int
+
+#include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor"
+#include "testing/base/public/benchmark.h"
+
+using Eigen::Tensor;
+using Eigen::TensorMap;
+
+
+// TODO(bsteiner): also templatize on the input type since we have users
+// for int8 as well as floats.
+template <typename Device> class BenchmarkSuite {
+ public:
+  BenchmarkSuite(const Device& device, size_t m, size_t k, size_t n)
+      : m_(m), k_(k), n_(n), device_(device) {
+    initialize();
+  }
+
+  BenchmarkSuite(const Device& device, size_t m)
+      : m_(m), k_(m), n_(m), device_(device) {
+    initialize();
+  }
+
+  ~BenchmarkSuite() {
+    device_.deallocate(a_);
+    device_.deallocate(b_);
+    device_.deallocate(c_);
+  }
+
+  void memcpy(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      device_.memcpy(c_, a_, m_ * m_ * sizeof(float));
+    }
+    // Record the number of values copied per second
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  void random(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = C.random();
+    }
+    // Record the number of random numbers generated per second
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  void slicing(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+    const Eigen::DSizes<TensorIndex, 2> quarter_sizes(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+    const Eigen::DSizes<TensorIndex, 2> first_quadrant(Eigen::array<TensorIndex, 2>(0, 0));
+    const Eigen::DSizes<TensorIndex, 2> second_quadrant(Eigen::array<TensorIndex, 2>(0, m_/2));
+    const Eigen::DSizes<TensorIndex, 2> third_quadrant(Eigen::array<TensorIndex, 2>(m_/2, 0));
+    const Eigen::DSizes<TensorIndex, 2> fourth_quadrant(Eigen::array<TensorIndex, 2>(m_/2, m_/2));
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.slice(first_quadrant, quarter_sizes).device(device_) =
+          A.slice(first_quadrant, quarter_sizes);
+      C.slice(second_quadrant, quarter_sizes).device(device_) =
+          B.slice(second_quadrant, quarter_sizes);
+      C.slice(third_quadrant, quarter_sizes).device(device_) =
+          A.slice(third_quadrant, quarter_sizes);
+      C.slice(fourth_quadrant, quarter_sizes).device(device_) =
+          B.slice(fourth_quadrant, quarter_sizes);
+    }
+    // Record the number of values copied from the rhs slice to the lhs slice
+    // each second
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  void shuffling(int num_iters) {
+    eigen_assert(m_ == n_);
+    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+    const Eigen::array<int, 2> shuffle(1, 0);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      B.device(device_) = A.shuffle(shuffle);
+    }
+    // Record the number of values shuffled from A and copied to B each second
+    finalizeBenchmark(m_ * k_ * num_iters);
+  }
+
+ void padding(int num_iters) {
+    eigen_assert(m_ == k_);
+    const Eigen::array<TensorIndex, 2> size_a(m_, k_-3);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const Eigen::array<TensorIndex, 2> size_b(k_, m_);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+    Eigen::array<Eigen::IndexPair<TensorIndex>, 2> paddings;
+    paddings[0] = Eigen::IndexPair<TensorIndex>(0, 0);
+    paddings[1] = Eigen::IndexPair<TensorIndex>(2, 1);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      B.device(device_) = A.pad(paddings);
+    }
+    // Record the number of values copied from the padded tensor A each second
+    finalizeBenchmark(m_ * k_ * num_iters);
+  }
+
+ void striding(int num_iters) {
+    eigen_assert(m_ == k_);
+    const Eigen::array<TensorIndex, 2> size_a(m_, k_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const Eigen::array<TensorIndex, 2> size_b(m_, k_ / 2);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, size_b);
+
+    const Eigen::array<TensorIndex, 2> strides(1, 2);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      B.device(device_) = A.stride(strides);
+    }
+    // Record the number of values copied from the padded tensor A each second
+    finalizeBenchmark(m_ * k_ * num_iters);
+  }
+
+  void broadcasting(int num_iters) {
+    const Eigen::array<TensorIndex, 2> size_a(m_, 1);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, size_a);
+    const Eigen::array<TensorIndex, 2> size_c(m_, n_);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, size_c);
+
+#if defined(__CUDACC__)
+    // nvcc doesn't support cxx11
+    const Eigen::array<int, 2> broadcast(1, n_);
+#else
+    // Take advantage of cxx11 to give the compiler information it can use to
+    // optimize the code.
+    Eigen::IndexList<Eigen::type2index<1>, int> broadcast;
+    broadcast.set(1, n_);
+#endif
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A.broadcast(broadcast);
+    }
+    // Record the number of values broadcasted from A and copied to C each second
+    finalizeBenchmark(m_ * n_ * num_iters);
+  }
+
+  void coeffWiseOp(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A * A.constant(3.14) + B * B.constant(2.7);
+    }
+    // Record the number of FLOP executed per second (2 multiplications and
+    // 1 addition per value)
+    finalizeBenchmark(3 * m_ * m_ * num_iters);
+  }
+
+  void algebraicFunc(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A.rsqrt() + B.sqrt() * B.square();
+    }
+    // Record the number of FLOP executed per second (assuming one operation
+    // per value)
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  void transcendentalFunc(int num_iters) {
+    eigen_assert(m_ == k_ && k_ == n_);
+    const Eigen::array<TensorIndex, 2> sizes(m_, m_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizes);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizes);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizes);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A.exp() + B.log();
+    }
+    // Record the number of FLOP executed per second (assuming one operation
+    // per value)
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  // Simple reduction
+  void reduction(int num_iters) {
+    const Eigen::array<TensorIndex, 2> input_size(k_, n_);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, input_size);
+    const Eigen::array<TensorIndex, 1> output_size(n_);
+    TensorMap<Tensor<float, 1>, Eigen::Aligned> C(c_, output_size);
+
+    const Eigen::array<TensorIndex, 1> sum_along_dim(0);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = B.sum(sum_along_dim);
+    }
+    // Record the number of FLOP executed per second (assuming one operation
+    // per value)
+    finalizeBenchmark(m_ * m_ * num_iters);
+  }
+
+  // do a contraction which is equivalent to a matrix multiplication
+  void contraction(int num_iters) {
+    const Eigen::array<TensorIndex, 2> sizeA(m_, k_);
+    const Eigen::array<TensorIndex, 2> sizeB(k_, n_);
+    const Eigen::array<TensorIndex, 2> sizeC(m_, n_);
+
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, sizeA);
+    const TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, sizeB);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, sizeC);
+
+    typedef typename Tensor<float, 2>::DimensionPair DimPair;
+    const Eigen::array<DimPair, 1> dims(DimPair(1, 0));
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A.contract(B, dims);
+    }
+    // Record the number of FLOP executed per second (size_ multiplications and
+    // additions for each value in the resulting tensor)
+    finalizeBenchmark(static_cast<int64>(2) * m_ * n_ * k_ * num_iters);
+  }
+
+  void convolution(int num_iters, int kernel_x, int kernel_y) {
+    const Eigen::array<TensorIndex, 2> input_sizes(m_, n_);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> A(a_, input_sizes);
+    const Eigen::array<TensorIndex, 2> kernel_sizes(kernel_x, kernel_y);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> B(b_, kernel_sizes);
+    const Eigen::array<TensorIndex, 2> result_sizes(
+        m_ - kernel_x + 1, n_ - kernel_y + 1);
+    TensorMap<Tensor<float, 2>, Eigen::Aligned> C(c_, result_sizes);
+    Eigen::array<Tensor<float, 2>::Index, 2> dims(0, 1);
+
+    StartBenchmarkTiming();
+    for (int iter = 0; iter < num_iters; ++iter) {
+      C.device(device_) = A.convolve(B, dims);
+    }
+    // Record the number of FLOP executed per second (kernel_size
+    // multiplications and additions for each value in the resulting tensor)
+    finalizeBenchmark(
+        (m_ - kernel_x + 1) * (n_ - kernel_y + 1) * kernel_x * kernel_y * 2 * num_iters);
+  }
+
+ private:
+  void initialize() {
+    a_ = (float *) device_.allocate(m_ * k_ * sizeof(float));
+    b_ = (float *) device_.allocate(k_ * n_ * sizeof(float));
+    c_ = (float *) device_.allocate(m_ * n_ * sizeof(float));
+
+    // Initialize the content of the memory pools to prevent asan from
+    // complaining.
+    device_.memset(a_, 12, m_ * k_ * sizeof(float));
+    device_.memset(b_, 23, k_ * n_ * sizeof(float));
+    device_.memset(c_, 31, m_ * n_ * sizeof(float));
+
+    BenchmarkUseRealTime();
+  }
+
+  inline void finalizeBenchmark(int64 num_items) {
+#if defined(EIGEN_USE_GPU) && defined(__CUDACC__)
+    if (Eigen::internal::is_same<Device, Eigen::GpuDevice>::value) {
+      device_.synchronize();
+    }
+#endif
+    StopBenchmarkTiming();
+    SetBenchmarkItemsProcessed(num_items);
+  }
+
+
+  size_t m_;
+  size_t k_;
+  size_t n_;
+  float* a_;
+  float* b_;
+  float* c_;
+  Device device_;
+};
+#endif  // THIRD_PARTY_EIGEN3_TENSOR_BENCHMARKS_H_