3.4
From Eigen
Raw dump of the main novelties and improvements that will be part of the 3.4 release compared to the 3.3 branch:
New features
- New versatile API for sub-matrices, slices, and indexed views [doc]. It basically extends
A(.,.)
to let it accept anything that looks-like a sequence of indices with random access. To make it usable this new feature comes with new symbols:Eigen::all
,Eigen::last
, and functions generating arithmetic sequences:Eigen::seq(first,last[,incr])
,Eigen::seqN(first,size[,incr])
,Eigen::lastN(size[,incr])
. Here is an example picking even rows but the first and last ones, and a subset of indexed columns:
MatrixXd A = ...; std::vector<int> col_ind{7,3,4,3}; MatrixXd B = A(seq(2,last-2,fix<2>, col_ind);
- Reshaped views through the new members
reshaped()
andreshaped(rows,cols)
. This feature also comes with new symbols:Eigen::AutoOrder
,Eigen::AutoSize
. [doc]
- A new helper
Eigen::fix<N>
to pass compile-time integer values to Eigen's functions. It is currently supported to pass compile-time sizes to.block(...)
,.segment(...)
, and all variants as well as the first, size and increment parameters of the seq, seqN, and lastN functions. You can also pass "possibly compile-time values" throughEigen::fix<N>(n)
. [doc]
Performance optimizations
- Vectorization of partial-reductions along outer-dimension, e.g.: colmajor.rowwise().mean()
- Speed up evaluation of HouseholderSequence to a dense matrix, e.g.,
MatrixXd Q = A.qr().householderQ();
Hardware supports
- Generalization of the CUDA support to CUDA/HIP for AMD GPUs.
- Add explicit support for MSA vectorization engine (MIPS).
- AVX512 is enabled by default when enabled on compiler side.