Added initial experimental support for explicit vectorization.

Currently only the following platform/operations are supported:
 - SSE2 compatible architecture
 - compiler compatible with intel's SSE2 intrinsics
 - float, double and int data types
 - fixed size matrices with a storage major dimension multiple of 4 (or 2 for double)
 - scalar-matrix product, component wise: +,-,*,min,max
 - matrix-matrix product only if the left matrix is vectorizable and column major
   or the right matrix is vectorizable and row major, e.g.:
   a.transpose() * b is not vectorized with the default column major storage.
To use it you must define EIGEN_VECTORIZE and EIGEN_INTEL_PLATFORM.
This commit is contained in:
Gael Guennebaud
2008-04-09 12:31:55 +00:00
parent 4920f2011e
commit 1985fb0551
25 changed files with 436 additions and 93 deletions

View File

@@ -65,7 +65,7 @@ template<typename Scalar> struct ei_scalar_cos_op;
template<typename Scalar> struct ei_scalar_sin_op;
template<typename Scalar> struct ei_scalar_pow_op;
template<typename Scalar, typename NewType> struct ei_scalar_cast_op;
template<typename Scalar> struct ei_scalar_multiple_op;
template<typename Scalar, bool IsVectorizable> struct ei_scalar_multiple_op;
template<typename Scalar> struct ei_scalar_quotient1_op;
template<typename Scalar> struct ei_scalar_min_op;
template<typename Scalar> struct ei_scalar_max_op;
@@ -116,5 +116,10 @@ template<typename T> struct ei_functor_traits
};
};
template<typename T> struct ei_packet_traits
{
typedef T type;
enum {size=1};
};
#endif // EIGEN_FORWARDDECLARATIONS_H