3.4
From Eigen
Eigen 3.4-beta1 has been released on December XX, 2018. It can be downloaded from the Download section on the Main Page. Since Eigen 3.3, the 3.4 development branch received more than 1300 commits [1] representing numerous major changes.
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 [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" throughEigen::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 }
- Add STL-compatible iterators for dense expressions [doc]. Some examples:
VectorXd v = ...; MatrixXd A = ...; // range for loop over all entries of v then A for(auto x : v) { cout << x << " "; } for(auto x : A.reshaped()) { cout << x << " "; } // sort v then each column of A std::sort(v.begin(), v.end()); for(auto c : A.colwise()) std::sort(c.begin(), c.end());
- Add c++11 initializer_list constructors to Matrix and Array [doc]:
MatrixXi a { // construct a 2x3 matrix {1,2,3}, // first row {4,5,6} // second row }; VectorXd v{{1, 2, 3, 4, 5}}; // construct a dynamic-size vector with 5 elements Array<int,1,5> a{1,2, 3, 4, 5}; // initialize a fixed-size 1D array of size 5.
- 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]
- Misc
- Add templated
subVector<Vertical/Horizonal>(Index)
aliases tocol/row(Index)
methods, andsubVectors<>()
aliases torows()/cols()
. - Add
innerVector()
andinnerVectors()
methods. - Add diagmat +/- diagmat operators (bug 520)
- Add specializations for
res ?= dense +/- sparse
andres ?= sparse +/- dense
. (see bug 632) - Add support for SuiteSparse's KLU sparse direct solver (LU-based solver tailored for problems coming from circuit simulation).
- Add templated
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 when using large SIMD registers (e.g., AVX and AVX512).
- Optimize evaluation of small products of the form
s*A*B
by rewriting them as:s*(A.lazyProduct(B))
to save a costly temporary. Measured speedup from 2x to 5x (see bug 1562). - Improve multi-threading heuristic for matrix products with a small number of columns.
- 20% speedup of matrix products on ARM64
- Speed-up reductions of sub-matrices.
- Optimize extraction of factor Q in SparseQR.
- SIMD implementations of math functions (exp,log,sin,cos) have been unified as a generic implementation compatible over all supported SIMD engines (SSE,AVX,AVX512,NEON,Altivec,VSX,MSA).
Hardware support
- AVX512 support is now complete (including complex scalars) and enabled by default when enabled on compiler side.
- Generalization of the CUDA support to CUDA/HIP for AMD GPUs.
- Add explicit SIMD support for MSA instruction set (MIPS).
Footnotes
[1] $ hg log -r "3.3.0:: and not merge() and not branch(3.2) and not branch(3.3)" | grep "changeset:" | wc -l