The following Intel MKL function domains are threaded with the OpenMP* technology:
Direct sparse solver.
For a list of threaded routines, see LAPACK Routines.
Level1 and Level2 BLAS.
For a list of threaded routines, see BLAS Level1 and Level2 Routines.
All Level 3 BLAS and all Sparse BLAS routines except Level 2 Sparse Triangular solvers.
All Vector Mathematics functions (except service functions).
For a list of FFT transforms that can be threaded, see Threaded FFT Problems.
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Notice revision #20110804
In this section, ? stands for a precision prefix of each flavor of the respective routine and may have the value of s, d, c, or z.
The following LAPACK routines are threaded with OpenMP*:
- Linear equations, computational routines:
- Factorization: ?getrf, ?getrfnpi, ?gbtrf, ?potrf, ?pptrf, ?sytrf, ?hetrf, ?sptrf, ?hptrf
- Solving: ?dttrsb, ?gbtrs, ?gttrs, ?pptrs, ?pbtrs, ?pttrs, ?sytrs, ?sptrs, ?hptrs, ?tptrs, ?tbtrs
- Orthogonal factorization, computational routines:
?geqrf, ?ormqr, ?unmqr, ?ormlq, ?unmlq, ?ormql, ?unmql, ?ormrq, ?unmrq
- Singular Value Decomposition, computational routines:
- Symmetric Eigenvalue Problems, computational routines:
?sytrd, ?hetrd, ?sptrd, ?hptrd, ?steqr, ?stedc.
- Generalized Nonsymmetric Eigenvalue Problems, computational routines:
A number of other LAPACK routines, which are based on threaded LAPACK or BLAS routines, make effective use of OpenMP* parallelism:
?gesv, ?posv, ?gels, ?gesvd, ?syev, ?heev, cgegs/zgegs, cgegv/zgegv, cgges/zgges, cggesx/zggesx, cggev/zggev, cggevx/zggevx, and so on.
Threaded BLAS Level1 and Level2 Routines
In the following list, ? stands for a precision prefix of each flavor of the respective routine and may have the value of s, d, c, or z.
The following routines are threaded with OpenMP* for Intel® Core™2 Duo and Intel® Core™ i7 processors:
- Level1 BLAS:
?axpy, ?copy, ?swap, ddot/sdot, cdotc, drot/srot
- Level2 BLAS:
?gemv, ?trmv, dsyr/ssyr, dsyr2/ssyr2, dsymv/ssymv
Threaded FFT Problems
The following characteristics of a specific problem determine whether your FFT computation may be threaded with OpenMP*:
- precision (single or double)
- placement (in-place or out-of-place)
- number of transforms
- layout (for example, interleaved or split layout of complex data)
Most FFT problems are threaded. In particular, computation of multiple transforms in one call (number of transforms > 1) is threaded. Details of which transforms are threaded follow.
One-dimensional (1D) transforms
1D transforms are threaded in many cases.
1D complex-to-complex (c2c) transforms of size N using interleaved complex data layout are threaded under the following conditions depending on the architecture:
N is a power of 2, log2(N) > 9, the transform is double-precision out-of-place, and input/output strides equal 1.
N is a power of 2, log2(N) > 13, and the transform is single-precision.
N is a power of 2, log2(N) > 14, and the transform is double-precision.
N is composite, log2(N) > 16, and input/output strides equal 1.
1D complex-to-complex transforms using split-complex layout are not threaded.
All multidimensional transforms on large-volume data are threaded.