Elemental.jl

Julia interface to the Elemental linear algebra library.
Popularity
35 Stars
Updated Last
6 Months Ago
Started In
March 2015

Elemental.jl

A package for dense and sparse distributed linear algebra and optimization. The underlying functionality is provided by the C++ library Elemental written originally by Jack Poulson and now maintained by LLNL.

Installation

The package is installed with Pkg.add("Elemental"). For Julia versions 1.3 and later, Elemental uses the binaries provided by BinaryBuilder, which are linked against the MPI (mpich) provided through BinaryBuilder.

Examples

Each of these examples should be run in a separate Julia session.

SVD example

This example runs on a single processor, and initializes MPI under the hood. However, explicit use of MPI.jl is not required in this case, compared to the other examples below.

julia> using LinearAlgebra, Elemental

julia> A = Elemental.Matrix(Float64)
0x0 Elemental.Matrix{Float64}

julia> Elemental.gaussian!(A, 100, 80);

julia> U, s, V = svd(A);

julia> convert(Matrix{Float64}, s)[1:10]
10-element Array{Float64,1}:
 19.8989
 18.2702
 17.3665
 17.0475
 16.4513
 16.3197
 16.0989
 15.8353
 15.5947
 15.5079

SVD example using MPI to parallelize on 4 processors

In this example, @mpi_do has to be used to send the parallel instructions to all processors.

julia> using MPI, MPIClusterManagers, Distributed

julia> man = MPIManager(np = 4);

julia> addprocs(man);

julia> @everywhere using LinearAlgebra, Elemental

julia> @mpi_do man A = Elemental.DistMatrix(Float64);

julia> @mpi_do man Elemental.gaussian!(A, 1000, 800);

julia> @mpi_do man U, s, V = svd(A);

julia> @mpi_do man println(s[1])
    From worker 5:  59.639990420817696
    From worker 4:  59.639990420817696
    From worker 2:  59.639990420817696
    From worker 3:  59.639990420817696

SVD example with DistributedArrays on 4 processors

This example is slightly different from the ones above in that it only calculates the singular values. However, it uses the DistributedArrays.jl package, and has a single thread of control. Note, we do not need to use @mpi_do explicitly in this case.

julia> using MPI, MPIClusterManagers, Distributed

julia> man = MPIManager(np = 4);

julia> addprocs(man);

julia> using DistributedArrays, Elemental

julia> A = drandn(1000, 800);

julia> Elemental.svdvals(A)[1:5]
5-element SubArray{Float64,1,DistributedArrays.DArray{Float64,2,Array{Float64,2}},Tuple{UnitRange{Int64}},0}:
 59.4649
 59.1984
 59.0309
 58.7178
 58.389

Truncated SVD

The iterative SVD algorithm is implemented in pure Julia, but the factorized matrix as well as the Lanczos vectors are stored as distributed matrices in Elemental. Notice, that TSVD.jl doesn't depend on Elemental and is only using Elemental.jl through generic function calls.

julia> using MPI, MPIClusterManagers, Distributed

julia> man = MPIManager(np = 4);

julia> addprocs(man);

julia> @mpi_do man using Elemental, TSVD, Random

julia> @mpi_do man A = Elemental.DistMatrix(Float64);

julia> @mpi_do man Elemental.gaussian!(A, 5000, 2000);

julia> @mpi_do man Random.seed!(123) # to avoid different initial vectors on the workers

julia> @mpi_do man r = tsvd(A, 5);

julia> @mpi_do man println(r[2][1:5])
    From worker 3:  [1069.6059089732858,115.44260091060129,115.08319164529792,114.87007788947226,114.48092348847719]
    From worker 5:  [1069.6059089732858,115.44260091060129,115.08319164529792,114.87007788947226,114.48092348847719]
    From worker 2:  [1069.6059089732858,115.44260091060129,115.08319164529792,114.87007788947226,114.48092348847719]
    From worker 4:  [1069.6059089732858,115.44260091060129,115.08319164529792,114.87007788947226,114.48092348847719]

Linear Regression

@mpi_do man A = Elemental.DistMatrix(Float32)
@mpi_do man B = Elemental.DistMatrix(Float32)
@mpi_do man copyto!(A, Float32[2 1; 1 2])
@mpi_do man copyto!(B, Float32[4, 5])

Run distributed ridge regression ½|A*X-B|₂² + λ|X|₂²

@mpi_do man X = Elemental.ridge(A, B, 0f0)

Run distributed lasso regression ½|A*X-B|₂² + λ|X|₁ (only supported in recent versions of Elemental)

@mpi_do man X = Elemental.bpdn(A, B, 0.1f0)

Coverage

Right now, the best way to see if a specific function is available, is to look through the source code. We are looking for help to prepare Documenter.jl based documentation for this package, and also to add more functionality from the Elemental library.

Used By Packages

No packages found.