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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].

by espeed 7y ago

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/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 https://arxiv.org/abs/1902.00846 http://www.mit.edu/~kepner/ http://www.mit.edu/~kepner/

sandGorgon 7y ago
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).
- lmeyerov 7y ago
  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 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!