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:
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.
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...
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