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This is useful in Krita where I call lu().solve() with a Map object
as result. As solve() resizes the result, this was needed.
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and AsRequested for the PacketAccess parameter of MapBase. Updated respective
documentation.
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- added a MapBase base xpr on top of which Map and the specialization
of Block are implemented
- MapBase forces both aligned loads (and aligned stores, see below) in expressions
such as "x.block(...) += other_expr"
* Significant vectorization improvement:
- added a AlignedBit flag meaning the first coeff/packet is aligned,
this allows to not generate extra code to deal with the first unaligned part
- removed all unaligned stores when no unrolling
- removed unaligned loads in Sum when the input as the DirectAccessBit flag
* Some code simplification in CacheFriendly product
* Some minor documentation improvements
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* faster matrix-matrix and matrix-vector products (especially for not aligned cases)
* faster tridiagonalization (make it using our matrix-vector impl.)
Others:
* fix Flags of Map
* split the test_product to two smaller ones
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make use of static asserts in Map
fix 2 warnings in CacheFriendlyProduct: unused var 'Vectorized'
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* Improve the efficiency of matrix*vector in unaligned cases
* Trivial fixes in the destructors of MatrixStorage
* Removed the matrixNorm in test/product.cpp (twice faster and
that assumed the matrix product was ok while checking that !!)
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* rework PacketMath and DummyPacketMath, make these actual template
specializations instead of just overriding by non-template inline
functions
* introduce ei_ploadt and ei_pstoret, make use of them in Map and Matrix
* remove Matrix::map() methods, use Map constructors instead.
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to "public:method()" i.e. reimplementing the generic method()
from MatrixBase.
improves compilation speed by 7%, reduces almost by half the call depth
of trivial functions, making gcc errors and application backtraces
nicer...
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* introduce packet(int), make use of it in linear vectorized paths
--> completely fixes the slowdown noticed in benchVecAdd.
* generalize coeff(int) to linear-access xprs
* clarify the access flag bits
* rework api dox in Coeffs.h and util/Constants.h
* improve certain expressions's flags, allowing more vectorization
* fix bug in Block: start(int) and end(int) returned dyn*dyn size
* fix bug in Block: just because the Eval type has packet access
doesn't imply the block xpr should have it too.
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flags. This ensures that unless explicitly messed up otherwise,
a Matrix type is equal to its own Eval type. This seriously reduces
the number of types instantiated. Measured +13% compile speed, -7%
binary size.
* Improve doc of Matrix template parameters.
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Rename DefaultLostFlagMask --> HerediraryBits
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-finline-limit=1000 to gcc to get good performance. By the way some cleanup.
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(does not support complex and does not re-use the QR decomposition)
* Rewrite the cache friendly product to have only one instance per scalar type !
This significantly speeds up compilation time and reduces executable size.
The current drawback is that some trivial expressions might be
evaluated like conjugate or negate.
* Renamed "cache optimal" to "cache friendly"
* Added the ability to directly access matrix data of some expressions via:
- the stride()/_stride() methods
- DirectAccessBit flag (replace ReferencableBit)
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(needed by the new product implementation)
* Make the packet* members template to support aligned and unaligned
access. This makes Block vectorizable. Combined with ReferencableBit,
we should be able to determine at runtime (in some specific cases) if
an aligned vectorization is possible or not.
* Improved the new product implementation to robustly handle all cases,
it now passes all the tests.
* Renamed the packet version ei_predux to ei_preduxp to avoid name collision.
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part of a matrix. Triangular also provide an optimised method for forward
and backward substitution. Further optimizations regarding assignments and
products might come later.
Updated determinant() to take into account triangular matrices.
Started the QR module with a QR decompostion algorithm.
Help needed to build a QR algorithm (eigen solver) based on it.
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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.
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when to evaluate arguments and when to meta-unroll.
-use it in Product to determine when to eval args. not yet used
to determine when to unroll. for now, not used anywhere else but
that'll follow.
-fix badness of my last commit
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-- currently 3 flags: RowMajor, Lazy and Large
-- only RowMajor actually used for now
* many minor improvements
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to disable eigen's asserts without disabling one's own program's
asserts. Notice that Eigen code should now use ei_assert()
instead of assert().
* Remove findBiggestCoeff() as it's now almost redundant.
* Improve echelon.cpp: inner for loop replaced by xprs.
* remove useless "(*this)." here and there. I think they were
first introduced by automatic search&replace.
* fix compilation in Visitor.h (issue triggered by echelon.cpp)
* improve comment on swap().
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internal classes: AaBb -> ei_aa_bb
IntAtRunTimeIfDynamic -> ei_int_if_dynamic
unify UNROLLING_LIMIT (there was no reason to have operator= use
a higher limit)
etc...
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Finally the importing macro is named EIGEN_BASIC_PUBLIC_INTERFACE
because it does not only import the ei_traits, it also makes the base class
a friend, etc.
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template parameter "Scalar" is removed. This is achieved by introducting a
template <typename Derived> struct Scalar to achieve a forward-declaration of
the Scalar typedefs.
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previous ugly hack :)
* Renamed the scalar functors with the "Scalar" prefix (instead of "Cwise")
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Rework the matrix storage to ensure optimal sizeof in all cases, while
keeping the decoupling of matrix sizes versus storage sizes.
Also fixing (recently introduced) bugs caused by unwanted
reallocations of the buffers.
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- finally get the Eval stuff right. get back to having Eval as
a subclass of Matrix with limited functionality, and then,
add a typedef MatrixType to get the actual matrix type.
- add swap(), findBiggestCoeff()
- bugfix by Ramon in Transpose
- new demo: doc/echelon.cpp
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dimension. The advantage is that evaluating a dynamic-sized block in a fixed-size
matrix no longer causes a dynamic memory allocation. Other new thing:
IntAtRunTimeIfDynamic allows storing an integer at zero cost if it is known at
compile time.
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-use row-major traversal when the number of columns is fixed
and the number of rows is dynamic
-other minor changes
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- complete documentation
- add TODO
- update copyright years
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- the static constants are private again in the Derived classes
- more documentation and code snippets
- new isDiagonal() method
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column-major order, even if storage is row-major. Benchmark showed that adapting
the traversal order to the storage order brought no benefit.
Also do some cleanup after Gael's big patch.
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CCMAIL:eigen@lists.tuxfamily.org
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Honor this preference in operator=.
- add several methods to the API
- rework API for diagonal matrices
- add benchmarking code
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satisfactory
default copy constructor; remove useless static_cast's; some misc cleanup.
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Benoit Jacob
Gael Guennebaud