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

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.

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.

A few things to do:

- [ ] automatic build on Windows

01/26/2016

3 months ago

53 commits