ArrayLayouts.jl

A Julia package for describing array layouts and more general fast linear algebra

This package implements a trait-based framework for describing array layouts such as column major, row major, etc. that can be dispatched to appropriate BLAS or optimised Julia linear algebra routines. This supports a much wider class of matrix types than Julia's in-built StridedArray. Here is an example:

julia> using ArrayLayouts, LinearAlgebra

julia> A = randn(10_000,10_000); x = randn(10_000); y = similar(x);

julia> V = view(Symmetric(A),:,:)';

julia> @time mul!(y, A, x); # Julia does not recognize that V is symmetric
  0.040255 seconds (4 allocations: 160 bytes)

julia> @time muladd!(1.0, V, x, 0.0, y); # ArrayLayouts does and is 3x faster as it calls BLAS
  0.017677 seconds (4 allocations: 160 bytes)

The package is based on assigning a MemoryLayout to every array, which is used for dispatch. For example,

julia> MemoryLayout(A) # Each column of A is column major, and columns stored in order
DenseColumnMajor()

julia> MemoryLayout(view(A, 1:3,:))  # Each column of A is column major
ColumnMajor()

julia> MemoryLayout(V) # A symmetric version, whose storage is DenseColumnMajor
SymmetricLayout{DenseColumnMajor}()

This is then used by muladd!(α, A, B, β, C), ArrayLayouts.lmul!(A, B), and ArrayLayouts.rmul!(A, B) to lower to the correct BLAS calls via lazy objects MulAdd(α, A, B, β, C), Lmul(A, B), Rmul(A, B) which are materialized, in analogy to Base.Broadcasted.

Note there is also a higher level function mul(A, B) that materializes via Mul(A, B), which uses the layout of A and B to further reduce to either MulAdd, Lmul, and Rmul.