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Vectorized the evaluation of tensor expression (using SSE, AVX, NEON, ...)

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Added the ability to parallelize the evaluation of a tensor expression over multiple cpu cores.
Added the ability to offload the evaluation of a tensor expression to a GPU.
This commit is contained in:
Benoit Steiner
2014-05-16 15:08:05 -07:00
parent 0320f7e3a7
commit 7402fea0a8
17 changed files with 719 additions and 65 deletions
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158
unsupported/Eigen/CXX11/src/Tensor/TensorMap.h
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@@ -22,16 +22,16 @@ template<int InnerStrideAtCompileTime, int OuterStrideAtCompileTime> class Strid
*
*/
template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap<PlainObjectType> >
template<typename PlainObjectType, int Options_> class TensorMap : public TensorBase<TensorMap<PlainObjectType, Options_> >
{
public:
typedef TensorMap<PlainObjectType> Self;
typedef TensorMap<PlainObjectType, Options_> Self;
typedef typename PlainObjectType::Base Base;
typedef typename Eigen::internal::nested<Self>::type Nested;
typedef typename internal::traits<PlainObjectType>::StorageKind StorageKind;
typedef typename internal::traits<PlainObjectType>::Index Index;
typedef typename internal::traits<PlainObjectType>::Scalar Scalar;
typedef typename internal::packet_traits<Scalar>::type PacketScalar;
typedef typename internal::packet_traits<Scalar>::type Packet;
typedef typename NumTraits<Scalar>::Real RealScalar;
typedef typename Base::CoeffReturnType CoeffReturnType;
@@ -43,13 +43,12 @@ template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap
typedef Scalar* PointerType;
typedef PointerType PointerArgType;
// Fixed size plain object type only
/* EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr) : m_data(dataPtr) {
// The number of dimensions used to construct a tensor must be equal to the rank of the tensor.
//EIGEN_STATIC_ASSERT(1 == PlainObjectType::NumIndices, YOU_MADE_A_PROGRAMMING_MISTAKE)
// todo: add assert to ensure we don't screw up here.
}*/
static const int Options = Options_;
enum {
IsAligned = bool(EIGEN_ALIGN) && ((int(Options_)&Aligned)==Aligned),
PacketAccess = true,
};
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE TensorMap(PointerArgType dataPtr, Index firstDimension) : m_data(dataPtr), m_dimensions(array<DenseIndex, PlainObjectType::NumIndices>({{firstDimension}})) {
@@ -65,7 +64,7 @@ template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap
}
#endif
inline TensorMap(PointerArgType dataPtr, const array<Index, PlainObjectType::NumIndices>& dimensions)
inline TensorMap(PointerArgType dataPtr, const array<Index, PlainObjectType::NumIndices>& dimensions)
: m_data(dataPtr), m_dimensions(dimensions)
{ }
@@ -80,12 +79,97 @@ template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar* data() const { return m_data; }
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(const array<Index, PlainObjectType::NumIndices>& indices) const
{
// eigen_assert(checkIndexRange(indices));
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(indices);
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(indices);
return m_data[index];
}
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index firstIndex, IndexTypes... otherIndices) const
{
static_assert(sizeof...(otherIndices) + 1 == PlainObjectType::NumIndices, "Number of indices used to access a tensor coefficient must be equal to the rank of the tensor.");
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(array<Index, PlainObjectType::NumIndices>{{firstIndex, otherIndices...}});
return m_data[index];
}
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index index) const
{
eigen_internal_assert(index >= 0 && index < size());
return m_data[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i1 + i0 * m_dimensions[0];
return m_data[index];
} else {
const Index index = i0 + i1 * m_dimensions[0];
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i2 + m_dimensions[1] * (i1 + m_dimensions[0] * i0);
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE const Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4) const
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
return m_data[index];
}
}
#endif
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(const array<Index, PlainObjectType::NumIndices>& indices)
{
// eigen_assert(checkIndexRange(indices));
if (PlainObjectType::Options&RowMajor) {
const Index index = m_dimensions.IndexOfRowMajor(indices);
return m_data[index];
} else {
const Index index = m_dimensions.IndexOfColMajor(indices);
return m_data[index];
}
}
#ifdef EIGEN_HAS_VARIADIC_TEMPLATES
template<typename... IndexTypes> EIGEN_DEVICE_FUNC
@@ -100,8 +184,60 @@ template<typename PlainObjectType> class TensorMap : public TensorBase<TensorMap
return m_data[index];
}
}
#else
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index index)
{
eigen_internal_assert(index >= 0 && index < size());
return m_data[index];
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i1 + i0 * m_dimensions[0];
return m_data[index];
} else {
const Index index = i0 + i1 * m_dimensions[0];
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i2 + m_dimensions[1] * (i1 + m_dimensions[0] * i0);
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * i2);
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * i3));
return m_data[index];
}
}
EIGEN_DEVICE_FUNC
EIGEN_STRONG_INLINE Scalar& operator()(Index i0, Index i1, Index i2, Index i3, Index i4)
{
if (PlainObjectType::Options&RowMajor) {
const Index index = i4 + m_dimensions[4] * (i3 + m_dimensions[3] * (i2 + m_dimensions[2] * (i1 + m_dimensions[1] * i0)));
return m_data[index];
} else {
const Index index = i0 + m_dimensions[0] * (i1 + m_dimensions[1] * (i2 + m_dimensions[2] * (i3 + m_dimensions[3] * i4)));
return m_data[index];
}
}
#endif
template<typename OtherDerived>
EIGEN_DEVICE_FUNC
Self& operator=(const OtherDerived& other)