For the bioinformatics datasets I work with it is not cost efficient to load everything into a database. For some of these datasets (e.g. GWAS - Genome Wide Association Studies which are essentially sparse matrices) it might be interesting to explore with graph queries. I guess my ideal would be to have a GraphBLAS equivalent to Spark SQL queries working across files in cloud storage / NAS.

There are several teams working on distributed GraphBLAS (the GraphBLAS/D4M model was designed to run on supercomputers [0]). Kepner's team at MIT is one [1].

NB: D4M was the original name before it was changed to GraphBLAS and became a standard.

[0] GraphBLAS: Building Blocks For High Performance Graph Analytics https://crd.lbl.gov/news-and-publications/news/2017/graphbla...

[1] A Billion Updates per Second Using 30,000 Hierarchical In-Memory D4M Databases https://arxiv.org/abs/1902.00846

http://www.mit.edu/~kepner/

This is very cool. I wonder if the Graphblas and the Dask team should collaborate.

Dask has a production grade distributed computing system (that is cloud compatible with kubernetes, yarn, EMR, Dataproc,etc).

RAPIDS has picked up Dask for multi-gpu aspects of cudf (think spark/pandas on GPUs), and as cugraph is single GPU (https://github.com/rapidsai/cugraph) for going fast on ~billion row datasets... I'm guessing dask+cugraph will be happening for the next 100-1000X, if not already.

Graph partitioning is a weird world, so will be interesting to see!