unobin VS CoinBLAS

I'm working on tool called Unobin that lets you write Ansible-esque playbooks and compile them to a standalone binary with all of the templates and files baked in.

I'm trying to solve the "works on my machine" problem that's very prevalent in the devops world. One person's playbook (or Terraform module or Chef cookbook or whatever) works fine on the developer's machine due to assumptions made during the development. But transfer that code to another machine and it's a whole 'nother story. There is often a lack of rigor around dependency management and versioning, along with hidden dependencies such as programs that need to exist on the machine where the playbook is run. I want to make it possible to include all of the dependencies directly in the binary, so you truly need just one binary (unobin) to download and run.

I've made a lot of progress and have what I feel is a solid POC at this point. It lacks many modules but I'm just writing them as I need them. Right now I'm working on a reference playbook to build a Concourse CI stack, and that's giving me solid feedback to myself about what's missing.

https://github.com/cloudboss/unobin

When you consider that a graph and a matrix are isomorphic, doing vector matrix multiplication takes a vector with a set value, say row 4, and multiplies it by a matrix where row 4 has values present that represent edges to the nodes that are adjacent to it (ie "adjacency" matrix). The result is a vector with the next "step" in a BFS across the graph, do that in a loop and you step across the whole graph.

A cool result of this is, for example, taking an adjacency matrix and squaring it is the "Friend of a Friend" graph. It takes every node/row and multiplies it by itself, returning a matrix that are adjacent to the adjacencies of each node, ie, the friends (adjacencies of the adjacencies) of friends (adjacencies) of the nodes.

Deeper traversal are just higher nodes, a matrix cubed are the friends of the friends of the friends.

A picture is worth a thousand words, see figure 7 of this paper:

https://arxiv.org/pdf/1606.05790.pdf

Also check out figure 8, this shows how incidence matrices can work to represent hyper and multi graphs. An pair of incidence matrices reprsent two graphs, one from nodes to edges and the other from edges to nodes, these are n by m and m by n. When you multiply them, you get a square adjacency matrix that "projects" the incidence into an adjacency. This can be used to collapse hypergraphs into simple graphs that use different semirings to combine the multiple edges.

For some pretty pictures of this kind of stuff, check out CoinBLAS (note I am not a crypto-bro, it was just a very handy extremely large multi-graph that I could easily download in chunks to play with):

https://github.com/Graphegon/CoinBLAS/

Python wrapper around The GraphBLAS API:

https://github.com/michelp/pygraphblas

For an upcoming paper we've open sourced using pygraphblas to analyse the bitcoin graph using the GAP benchmarks on a server with 1TB of RAM:

https://github.com/Graphegon/CoinBLAS