Difference between revisions of "3.4"

From Eigen
Jump to: navigation, search
(Performance optimizations)
(Hardware supports)
Line 40: Line 40:
===  Hardware supports ===
===  Hardware support ===
* Generalization of the CUDA support to CUDA/HIP for AMD GPUs.
* Generalization of the CUDA support to CUDA/HIP for AMD GPUs.
* Add explicit support for MSA vectorization engine (MIPS).
* Add explicit support for MSA vectorization engine (MIPS).
* AVX512 is enabled by default when enabled on compiler side.
* AVX512 is enabled by default when enabled on compiler side.

Revision as of 11:37, 11 November 2018

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 [doc]. It can be used 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 introduced above. You can also pass "possibly compile-time values" through Eigen::fix<N>(n). Here is an example comparing the old and new way to call .block with fixed sizes:
template<typename MatrixType,int N>
void foo(const MatrixType &A, int i, int j, int n) {
    A.block(i,j,2,3);                         // runtime sizes
    // compile-time nb rows and columns:
    A.template block<2,3>(i,j);               // 3.3 way
    A.block(i,j,fix<2>,fix<3>);               // new 3.4 way
    // compile-time nb rows only:
    A.template block<2,Dynamic>(i,j,2,n);     // 3.3 way
    A.block(i,j,fix<2>,n);                    // new 3.4 way
    // possibly compile-time nb columns
    // (use n if N==Dynamic, otherwise we must have n==N):
    A.template block<2,N>(i,j,2,n);           // 3.3 way
    A.block(i,j,fix<2>,fix<N>(n));            // new 3.4 way
  • A new namespace indexing allowing to exclusively import the subset of functions and symbols that are typically used within A(.,.), that is: all,seq, seqN, lastN, last, lastp1. [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();
* Various optimizations of matrix products for small and medium sizes matrices when using large SIMD registers (e.g., AVX and AVX512).

Hardware support