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beebs

The graph does need to fit into GPU ram. We use graph partitioning for multi-node, Multi-GPU configurations.

Dijkstra's algorithm which, as mentioned by Davidson et al. [1], 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 [2].

[1] 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

[2] Kepner and Gilbert: "Graph Algorithms in the Language of Linear Algebra."

We've definitely been working on improving the ease of getting started. The number of configuration options is a strength and a challenge.

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/

Should be all resolved. Tuning EC-2 up for the /. effect...
We definitely see Spark + Scala + Blazegraph DASL to be a sweet spot for combining the ease of Spark with the GPU performance. Have a submission in to the Hadoop Summit in June on it: https://hadoopsummit.uservoice.com/forums/344955-data-scienc...
We've seen cases where the newer Thawte root certs were not included in the trust chain. That might be the issue.
Got any questions on Blazegraph GPU? Just let us know...