Hi Nils,
We could add a SharedArray method to the Dierckx methods as Tim suggests,
perhaps by changing the signatures from Vector{Float64} to
Union(Vector{Float64},
SharedArray{Float64, 1}).
I’ve never really used SharedArrays myself, but an easier option might be
to use the sdata function. For example, the following works for me:
xgrid = convert(SharedArray, linspace(0.1, 100., 200))
fval = convert(SharedArray, Float64[xgrid[i]^(-0.25) for i in 1:length(xgrid)])
spl = Spline1D(sdata(xgrid), sdata(fval))
Best,
Kyle
On Fri, Jan 9, 2015 at 11:47 AM, Tim Holy <[email protected]> wrote:
> 1. You (or someone) needs to write those methods. Since the Spline1D
> function
> is defined in the Dierckx package, that's where the SharedArray version
> should
> go, too (if you don't just maintain it on your own).
>
> 2. After
> convert(SharedArray, xgrid)
> xgrid will still be an ordinary array. You've not captured the output of
> the
> convert function.
>
> --Tim
>
> On Friday, January 09, 2015 08:33:20 AM Nils Gudat wrote:
> > Sorry for asking the umptiest question on parallelization, but I can't
> seem
> > to get even the most basic calculations to run, so here's hoping someone
> > can enlighten me.
> > I'd like to make an interpolant created with Dierckx available on all my
> > processors. I figured this would be possible using SharedArrays in the
> > following way:
> >
> > addprocs(3)
> >
> > @everywhere using Dierckx
> >
> > xgrid = linspace(0.1, 100.0, 200)
> >
> > f_val = [xgrid[i]^(-0.25) for i in length(xgrid)]
> >
> > convert(SharedArray, xgrid)
> > convert(SharedArray, f_val)
> >
> > @everywhere interpolant = Spline1D(xgrid, f_val)
> >
> >
> > However, this attempt fails with two errors (or six, two for each core):
> >
> > Spline1D has not method matching Spline1D(::SharedArray{Float64, 1},
> >
> > ::SharedArray{Float64, 1})
> >
> > and
> >
> > xgrid_irr not defined
> >
> > I have a question on each:
> > 1. How can I use SharedArrays in functions that don't have a method for
> > them?
> > 2. Why would xgrid not be defined on the other cores? My understanding
> from
> > reading the SharedArray part
> > <
> http://julia.readthedocs.org/en/latest/manual/parallel-computing/#shared-ar
> > rays-experimental> of the documentation was that their whole purpose is
> to
> > make an array available to all cores?
> >
> > Any hints or tips would be greatly appreciated!
>
>
