Parallel sparse matrix (CSR) operations?

Is there some appetite for parallelizing the basic sparse matrix operations?

The existing CSR methods for matrix construction, multiply, matvec, binop, etc. are written in sequential C++ and offer some low-hanging fruit for parallelism. The main loop for most of these is (or can easily be made) embarrassingly parallel. As a proof of concept I converted the outer loops of many methods to parallel STL algorithms and saw a 3-5x speedup on my 6-core laptop (on medium/large versions of existing benchmarks).

Operations on small matrices should remain sequential to avoid paying parallel overhead when it won't be amortized.

The parallel STL algorithms I used are implemented with a thread pool. This avoids the TBB requirement for GCC's parallel STL and will work on all platforms. I avoided OpenMP as per docs. If there is a different preferred way to write parallel C++ in the SciPy codebase, let me know.

If folks are interested I'd be happy to contribute a workable PR. The code is cleaner than I expected it to be.

-Adam
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