ParSpMatVec.jl

Shared-memory implementation of parallel sparse matrix vector product in Julia
Author JuliaInv
Popularity
5 Stars
Updated Last
11 Months Ago
Started In
January 2016

Build Status Coverage Status Build status

ParSpMatVec.jl

Shared-memory implementation of parallel sparse matrix vector product in Julia. We thank Roman Shekhtman (UBC) for providing the Fortran code.

Installation

To install on a unix machine, follow these steps

Pkg.add("ParSpMatVec")
Pkg.test("ParSpMatVec")

The first line downloads the package and (on unix) compiles the Fortran codes (gfortran is used by default). Currently there is no automatic build procedure for Windows. Pull requests are welcome.

The second line tests the package.

Usage

Currently, we do not overload the matrix vector product in Base (this might be added in the future). Let A be a sparse matrix, alpha and beta floating point numbers and x and y be real- or complex values vectors of appropriate size. Then, the following commands are equivalent

nproc = 4; # choose number of OMP threads
yt = copy(y)
y = beta*y + alpha * A*x
ParSpMatVec.A_mul_B!( alpha, A, x, beta, yt, nproc)

Similarly, for the transpose matrix-vector product:

yt= copy(y)
y = beta*y + alpha * A'*x
ParSpMatVec.Ac_mul_B!( alpha, A, x, beta, yt, nproc)

The last input, nproc, determines how many OpenMP threads are used. Note that, due to the compressed column storage, products with the adjoint of A are expected to scale better.

ToDo

A few things to do:

  • automatic build on Windows

Required Packages

Used By Packages

No packages found.