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 Eigen  3.3.9
Using Intel® MKL from Eigen

Since Eigen version 3.1 and later, users can benefit from built-in Intel® Math Kernel Library (MKL) optimizations with an installed copy of Intel MKL 10.3 (or later).

Intel MKL provides highly optimized multi-threaded mathematical routines for x86-compatible architectures. Intel MKL is available on Linux, Mac and Windows for both Intel64 and IA32 architectures.

Note
Intel® MKL is a proprietary software and it is the responsibility of users to buy or register for community (free) Intel MKL licenses for their products. Moreover, the license of the user product has to allow linking to proprietary software that excludes any unmodified versions of the GPL.

Using Intel MKL through Eigen is easy:

1. define the EIGEN_USE_MKL_ALL macro before including any Eigen's header
3. on a 64bits system, you must use the LP64 interface (not the ILP64 one)

When doing so, a number of Eigen's algorithms are silently substituted with calls to Intel MKL routines. These substitutions apply only for Dynamic or large enough objects with one of the following four standard scalar types: float, double, complex<float>, and complex<double>. Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms.

In addition you can choose which parts will be substituted by defining one or multiple of the following macros:

 EIGEN_USE_BLAS Enables the use of external BLAS level 2 and 3 routines EIGEN_USE_LAPACKE Enables the use of external Lapack routines via the Lapacke C interface to Lapack EIGEN_USE_LAPACKE_STRICT Same as EIGEN_USE_LAPACKE but algorithm of lower robustness are disabled. This currently concerns only JacobiSVD which otherwise would be replaced by gesvd that is less robust than Jacobi rotations. EIGEN_USE_MKL_VML Enables the use of Intel VML (vector operations) EIGEN_USE_MKL_ALL Defines EIGEN_USE_BLAS, EIGEN_USE_LAPACKE, and EIGEN_USE_MKL_VML

The options can be combined with MKL_DIRECT_CALL to enable MKL direct call feature. This may help to increase performance of some MKL BLAS (?GEMM, ?GEMV, ?TRSM, ?AXPY and ?DOT) and LAPACK (LU, Cholesky and QR) routines for very small matrices. To make it work properly, the macro EIGEN_USE_MKL must also be defined in the case none of the other EIGEN_USE_MKL_* macros has been defined.

Note that the BLAS and LAPACKE backends can be enabled for any F77 compatible BLAS and LAPACK libraries. See this page for the details.

Finally, the PARDISO sparse solver shipped with Intel MKL can be used through the PardisoLU, PardisoLLT and PardisoLDLT classes of the PardisoSupport module.

The following table summarizes the list of functions covered by EIGEN_USE_MKL_VML:

Code exampleMKL routines
v2=v1.array().sin();
v2=v1.array().asin();
v2=v1.array().cos();
v2=v1.array().acos();
v2=v1.array().tan();
v2=v1.array().exp();
v2=v1.array().log();
v2=v1.array().sqrt();
v2=v1.array().square();
v2=v1.array().pow(1.5);
v?Sin
v?Asin
v?Cos
v?Acos
v?Tan
v?Exp
v?Ln
v?Sqrt
v?Sqr
v?Powx

In the examples, v1 and v2 are dense vectors.