This comment breaks the site guidelines, which ask: "Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something." Please do better than this when posting here.

https://news.ycombinator.com/newsguidelines.html

Hah, "just" a reinvention of graph analysis with linear-algebra. That's a "paradigm shift." Check out the performance numbers from that youtube video for redis-graph versus other graph databases. Reformulation of graph algorithms as algebraic operations means a huge step up in abstraction, and optimization, and performance. GraphBLAS is what enables that. It's the difference between tensorflow and having to rewrite stochastic gradient descent for every deep net architecture you want to test.

For why somebody would write an entire article about counting triangles, check out the youtube video above. It's the answer to one of the first questions from the audience.

It's ok not to care about graphs, but why come in to a thread about graphs and say "what's the point?" Graphs are important for some areas, and not for others.

I'm not asking the point of graphs, I'm asking what is actually new. Directed acyclical graphs have been around for ages, combining them with linear algebra is not new.

Instead of actually explaining anything that is new, original, unique or just a general step forward, all the replies seem to be saying how silly it is for asking and how smart someone is for counting triangles.

Please take any single one of the sentences of my first paragraph above, to explain what's cool about the comment and the OP. Any one sentence would, if true, make this material far more worthy of hacker interest than the typical HN front page article. That the post above shows all of them makes this comment one of the better contributions to the site in the past week. I'm still making my way through all the material and without a doubt consider it the best information I've learned all week.

Yes, graphs have been around for ages. They're as old as trees! ;-)

And you're right, first-year intro courses present graphs as matrices, it's usually the first graph representation you learn.

However, historically that's not how graphs have been represented in commercial or open-source databases due to the computational complexity and impracticality of supporting different architectures. Many PhD research papers over the years have been about the task of creating one-off implementations for the new hardware of the day.

For an overview of GraphBLAS in the context Heterogeneous High-Performance Computing (HHPC) systems running on NVIDIA GPUs and Intel Xeon Phis, see the 2015 talk Scott McMillan (https://insights.sei.cmu.edu/author/scott-mcmillan/) gave at the CMU Software Engineering Institute:

Graph Algorithms on Future Architectures [video] https://www.youtube.com/watch?v=-sIdS4cz7-4

The figure cited by Scott McMillan in the CMU video is that each new hardware architecture implementation requires about 10,000 lines of code for BFS, and that's just one algorithm. The GraphBLAS standard makes this problem go away.

Furthermore, general on-demand access to GPU and TPU accelerators in cloud data centers just now became a thing. GraphBLAS will make it possible for non PhDs to run graph algos on clusters of accelerators in the cloud at supercomputer speeds.

https://cloud.google.com/gpu/

https://cloud.google.com/tpu/

Having the power of a Graph 500 (https://graph500.org) supercomputer at your fingertips and the ability to tap into that power on demand...well that's new! :-) And kinda crazy cool too, don't ya think?

P.S. A few years back, Jeremy Kepner did a mini-course on D4M (the precursor to GraphBLAS). The videos and material are on MIT OCW...

MIT D4M: Mathematics of Big Data and Machine Learning [video] https://www.youtube.com/watch?v=iCAZLl6nq4c&list=PLUl4u3cNGP...

Discussion: https://news.ycombinator.com/item?id=18105931

> I have no idea what it is supposed to accomplish, let alone what the advantage is

Did you read/view any of the references?

I tend to cite everything so that you have direct access to the source material for reference in case the context isn't clear.

This is not a "mess of buzzwords." It's how we move beyond the limits of Moore's Law. A lot of smart people have been working on various aspects of this for a long time. It's a big deal because it's a big change in how we model data, write algorithms, and think about computation in general, from the design of low-level hardware all the way up the stack to application design. It will behoove you to understand the change that's coming because it's what's next, and it's already here.

Re: your question on "Why did someone write an entire paper about counting triangles?"

Prof Tim Davis wrote the paper. He is one of the world's leading experts on sparse matrix implementations, and after the GraphBLAS Steering Committee finalized the 1.0 spec, they asked him to create the reference C implementation. Triangle counting is a common graph algorithm so it provides a familiar reference point when illustrating and comparing the characteristics of an implementation. Dr. Davis presented the paper this week at the HPEC 2018 conference (http://www.ieee-hpec.org), and in the paper's abstract he articulates precisely why:

Graph algorithms via SuiteSparse:GraphBLAS: triangle counting and K-truss

Abstract — SuiteSparse:GraphBLAS is a full implementation of the GraphBLAS standard, which defines a set of sparse matrix operations on an extended algebra of semirings using an almost unlimited variety of operators and types. When applied to sparse adjacency matrices, these algebraic operations are equivalent to computations on graphs. GraphBLAS provides a powerful and expressive framework for creating graph algorithms based on the elegant mathematics of sparse matrix operations on a semiring. To illustrate GraphBLAS, two graph algorithms are constructed in GraphBLAS and compared with efficient implementations without GraphBLAS: triangle counting and constructing the ktruss of a graph.

http://faculty.cse.tamu.edu/davis/GraphBLAS/HPEC18/Davis_HPE...

Watch the RedisGraph video for a general overview of how it works, but the big picture in the gist of it all is that data used in machine learning, AI, and relational databases is now being modeled in the same way -- as vectors/matrices/tensors -- to take advantage of modern CPU/GPU/TPU hardware accelerators that are being optimized for vector/matrix/tensor multiply-add operations. This means we can now run local and global algorithms in the same way, using the same code and the same data model, and we can return queries in real time -- executed in parallel, both locally or distributed -- it doesn't matter how the data is partitioned, the computation model is the same. Not only is this fast and compact, it unifies and simplifies things.