Hi Michel - You've been busy with this and the Postgres lib. Good work. The Gremlin User Group has been working on a new universal graph DSL and compiler that can compile down to any of the graph backends. GraphBLAS has been part of the discussion since the start. An early draft of mm-ADT was just released last week...<p>mm-ADT: A Multi-Model Abstract Data Type <a href="http://rredux.com/mm-adt/" rel="nofollow">http://rredux.com/mm-adt/</a>
This looks really interesting, potentially makes GraphBLAS much more accessible for exploratory work. A few questions for the author:<p>Does this work in blocking or non-blocking mode? Naively I imagine there might be more opportunity for the GraphBLAS implementation to optimize execution in non-blocking mode.<p>Is there a way to efficiently store and load matrices to and from files? Ideally in a such a way that the data is just mmap'ed or copied directly into memory on load?<p>Does this only work with SuiteSparse or could it potentially work with a GPU implementation like <a href="https://github.com/gunrock/graphblast" rel="nofollow">https://github.com/gunrock/graphblast</a> too?
Interesting - would be interested in a comparison between GraphBLAS (which I had not heard of until just now) and, for example, graph-tool's (<a href="https://graph-tool.skewed.de/" rel="nofollow">https://graph-tool.skewed.de/</a>) underlying algorithms (Boost Graph Library).
GraphBLAS is also the engine behind RedisGraph. Sparse adjacency matrices are interesting from a graph database perspective in particular because they are typically faster and more compact than the mainstream index-free adjacency systems (e.g. Neo4j).
Very interesting, didn't know about graphblas!<p>Does anybody here know about the advantages with respect to scipy.sparse ? Does scipy.sparse use graphblas internally?