From Eigen
Revision as of 08:07, 11 November 2018 by Ggael (Talk | contribs)

Jump to: navigation, search

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() and reshaped(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" through Eigen::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.