Distributed [1] is very new and seems to have similar core architectural goals as TensorFlow. But perhaps I'm being too politically correct: both make use of very course-grain parallelism relative to a typical distributed-memory linear algebra library (e.g., the current communication mechanisms of both are likely to be too course-grain to efficiently support distributed dense matrix inversion or eigensolvers; not that this is likely to be a design goal of either).

[1] http://matthewrocklin.com/blog/work/2015/06/23/Distributed/

I think distributed's scheduler has seen much iteration since then and is now based on tornado. I wonder if that changes your assessment.

Also what do you think of Julia's native distributed capabilities and this library here: https://github.com/shashi/ComputeFramework.jl