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/