Blazegraph is a very niche product and requires a lot of time for setting it up and adjusting it for your workload.
If Blazegraph peaks your interest then you should also look into Yarc platform by Cray.
Should you want to look more into graphs, but don't want to spend endless nights just trying to load your data then I would recommend Stardog, which has just been a pleasure to work with.
I can imagine that with extensive tuning, BlazeGraph provides a good database. Just don't expect it to have the polish and convenience of a modern RDBMS or a shiny NoSQL store :)
Here's a spreadsheet showing WikiData's evaluation of each candidate graph database:
Basically as far as I can see, the main reason BlazeGraph was chosen was this: [they] had me out to their office (a house an hour and half from mine)
I'm sure BlazeGraph is fine. We were doing a very similar evaluation at the same time, and the Titan situation screwed us over too. But we took a look at BlazeGraph after Wikidata chose it, and found it pretty rudimentary at that time.
 "As you can also see we didn't finish filling them all out. But we've still pretty much settled on BlazeGraph anyway. Let me first explain what
BlazeGraph is and then defend our decision to stop spreadsheet work" https://lists.wikimedia.org/pipermail/wikidata-tech/2015-Mar...
There's now an updated user's guide on the wiki: https://wiki.blazegraph.com/wiki/index.php/Main_Page
and additional code samples: https://github.com/blazegraph/blazegraph-samples/
I would happily pay for a developer license or something like that.
The original work was funded by DARPA and presented at the
2014 SIGMOD conference in a paper entitled, MapGraph: A
High Level API for Graphs . This work is available in
open source. Later work, in collaboration with the
University of Utah SCI Institute  and funded by DARPA,
applied multi-core techniques running on over 750 M cores
on the Titan Supercomputer to extend this to Multi-GPU
traversal with Breadth First Search (BFS). On a cluster of
64 NVIDIA K40 GPUs, it demonstrated a throughput of 32
Billion Traversed Edges Per Second (32 GTEPS), traversing a
scale-free graph of 4.3 billion directed edges in 0.15
seconds, which was featured in a presentation IEEE Bigdata
VIDEO: Blazegraph GPU and DASL at Super Computing 2015 (https://vimeo.com/148519808)
It's GPLv2 and now has support for SPARQL and TinkePop3/Gremlin.
The owner of www.blazegraph.com has configured their website improperly. To protect your information from being stolen, Firefox has not connected to this website."
Can the GPU be used to accelerate shortest-path queries (e.g. dijkstra's algorithm) and if so, where can I read more about how that's achieved?
Dijkstra's algorithm which, as mentioned by Davidson et al. , is a "sequential algorithm [that] is poorly suited for parallel architectures like GPUs that require large numbers of parallel threads for efficient execution."
Instead, we have variants of the algebraic formulation of the Bellman-Ford algorithm as given in Kepner and Gilbert's book .
 Andrew A. Davidson, Sean Baxter, Michael Garland, and John D. Owens: "Work-Efficient Parallel GPU Methods for Single-Source Shortest Paths." In Proceedings of the IEEE 28th International Parallel and Distributed Processing Symposium (IPDPS), 2014. http://dx.doi.org/10.1109/IPDPS.2014.45
 Kepner and Gilbert: "Graph Algorithms in the Language of Linear Algebra."
It'd be awesome to have Blazegraph as a backend for Spark's Pregel queries.
With Tensorflow bindings in place, and the BIDMach/BIDMat libraries, it is very nice seeing Spark getting some serious GPU attention.