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The GraphBLAS GPU code is in the works [1]. For storage see the new RedisGraph 1.0 implementation released last year and 'michelp has a Python Postgres implemen

by espeed 7y ago

The GraphBLAS GPU code is in the works [1]. For storage see the new RedisGraph 1.0 implementation released last year and 'michelp has a Python Postgres implementation in development. [1] Sparse versus dense in GraphBLAS: sometimes dense is better http://aldenmath.com/sparse-verses-dense-in-graphblas-sometimes-dense-is-better/ http://aldenmath.com/sparse-verses-dense-in-graphblas-someti... [2] RedisGraph: A Graph Database Module for Redis http://graphblas.org/?title=Graph_BLAS_Forum#Graph_analysis_systems_that_integrate_GraphBLAS http://graphblas.org/?title=Graph_BLAS_Forum#Graph_analysis_... [3] Graph Processing with Postgres and GraphBLAS https://news.ycombinator.com/item?id=19379800 https://news.ycombinator.com/item?id=19379800

laurencerowe 7y ago
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.
- 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!

by espeed 7y ago

laurencerowe 7y ago

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.

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!