| Commit message (Collapse) | Author | Age |
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in benchmark. Still not as fasts as explicit eval(), strangely.
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extended cache optimal product to work in any row/column
major situations, and a few bugfixes (forgot to add the
Cholesky header, vectorization of CwiseBinary)
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Added a test for Triangular.
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m.upper() = a+b;
only updates the upper triangular part of m.
Note that:
m = (a+b).upper();
updates all coefficients of m (but half of the additions
will be skiped)
Updated back/forward substitution to better use Eigen's capability.
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- vector to vector assign
- PartialRedux
- Vectorization criteria of Product
- returned type of normalized
- SSE integer mul
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- support dynamic sizes
- support arbitrary matrix size when the matrix can be seen as a 1D array
(except for fixed size matrices where the size in Bytes must be a factor of 16,
this is to allow compact storage of a vector of matrices)
Note that the explict vectorization is still experimental and far to be completely tested.
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fully optimized.
* Even if LargeBit is set, only parallelize for large enough objects
(controlled by EIGEN_PARALLELIZATION_TRESHOLD).
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using a macro and _Pragma.
- use OpenMP also in cacheOptimalProduct and in the
vectorized paths as well
- kill the vector assignment unroller. implement in
operator= the logic for assigning a row-vector in
a col-vector.
- CMakeLists support for building tests/examples
with -fopenmp and/or -msse2
- updates in bench/, especially replace identity()
by ones() which prevents underflows from perturbing
bench results.
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* rename OperatorEquals -> Assign
* move Util.h and FwDecl.h to a util/ subdir
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