This package is a draft implementation of SIMD-based basic linear algebra routines for matrices with element types from DoubleFloats.jl.

The package name is perhaps a bit misleading: only a modest fraction of the BLAS forest is implemented, the interface is (mostly) Julian rather then BLAS-like, and some extra related methods are included to make important parts of LinearAlgebra and its supplements work efficiently.

The API is intended to be seamlessly compatible with the LinearAlgebra standard library. That is, using DoubleBLAS will extend various methods from LinearAlgebra so that frequent operations such as matrix multiplication, LU and Cholesky factorization, and inv() will employ more efficient methods than the generic ones.

The arithmetic used in this package is the straightforward double-double variety which does not respect all IEEE-754 rules. (Kahan calls it "fast but grubby", but concedes that it may be useful for linear algebra, so here we are.) Underflow is largely similar to IEEE-754, but overflow and treatment of NaN and infinities are non-conforming (and complicated). Users are advised to make sure that vectors and matrices are scaled to avoid overflow. (This is good advice in general for linear algebra, but especially important here.)

Multi-threading (MT) is enabled for some sufficiently large problems. We use Base.Threads (q.v. in the Julia manual), limited by the JULIA_NUM_THREADS environment variable. On many systems there is significant overhead for MT, so heuristic thresholds for switching from simple versions to MT are provided. These may be adjusted with the set_mt_threshold(n::Real, problem::Symbol) function. Someday tuned values for a given system might be set during package installation or initialization, but currently they are notional or based on very limited testing.

DoubleFloats are especially useful for mixed-precision iterative refinement. This can be used to improve solutions of moderately poorly-conditioned problems. I didn't find an implementation in other well-known packages, so I provide refinedldiv here:

julia> n=4096; A=rand(Double64,n,n); x=rand(Double64,n);
julia> b=A*x;
julia> F=lu(Float64.(A));
julia> bf = Float64.(b);
julia> xf = F \ bf;
julia> norm(xf-x)/norm(x)
6.745241585354585e-11

julia> xx,cvg = refinedldiv(A,F,b);
julia> norm(xx-x)/norm(x)
2.1020613807856875066955678325842648e-27

This takes a couple of seconds vs. more than a minute for a full Double64 factorization (with several threads).

Most of the arithmetic was copied from DoubleFloats.jl and AccurateArithmetic.jl. Linear algebra routines were adapted from the Julia standard library.