Parallel execution may be slower than serial

Nobody has claimed this yet.

@madeleineudell Thanks for this package, here is the output of my initial testing:
## Sample program:

```
using ParallelSparseRegression

m,n,p = 2048,1024,.1
A = sprand(m,n,p)
x0 = Base.shmem_randn(n)
b = A*x0
rho = 1
lambda = 1
quiet = false
maxiters = 100

params = Params(rho,quiet,maxiters)

# Lasso
@time z_lasso = lasso(A,b,lambda; params=params)
```

Calling the following program with different addprocs values gives the following:
## Output without addprocs:

1000 : 1.76e+00 1.27e-01 5.54e-03 4.09e+01
elapsed time: 24.422318823 seconds (6440755392 bytes allocated)
## Output with addprocs(3):

1000 : 2.15e+00 1.12e-01 6.07e-03 4.65e+01
elapsed time: 90.979009048 seconds (12805856436 bytes allocated)
## Output with addprocs(7):

1000 : 1.75e+00 1.47e-01 5.74e-03 4.21e+01
elapsed time: 228.324713722 seconds (28927210844 bytes allocated)
## Full output with values for every iteration:

https://gist.github.com/ingenieroariel/9095001

Start by running the sample Julia program with no workers, addprocs(3), and addprocs(7), then compare its timings and allocations with the linked gist. Trace the parallel execution path used by lasso and determine why additional workers increase runtime; done means the cause is identified and the reported parallel behavior is addressed or documented with a verified benchmark.

Written by the indexing model from the issue text.