Graph algorithms via SuiteSparse:GraphBLAS: triangle counting and K-truss (2018) [pdf] | Hacker News Reader

Graph algorithms via SuiteSparse:GraphBLAS: triangle counting and K-truss (2018) [pdf]

faculty.cse.tamu.edu

15 pointsespeed4 comments

Has anyone compared GraphBLAS to nvgraph, custinger, gunrock, etc.?

GraphBLAS is an open standard, and I believe the implementation in the link provided targets single-threaded CPU, which rules it out for comparison with nvgraph, custinger and gunrock.

My recent work actually implements some GraphBLAS operations for the GPU and compares them to Gunrock in breadth-first-search [1]. Our findings are our implementation of a subset of GraphBLAS is comparable to Gunrock in performance for power law graphs, but not for mesh graphs. Gunrock uses a different load-balancer in Advance for those graphs and the load-balancer we use in the analogous operation (matrix-vector multiplication) is not yet specialized for mesh graphs.

The code is open-source, so feel free to check it out! [2]

[1] https://arxiv.org/pdf/1804.03327.pdf

[2] https://github.com/owensgroup/push-pull

Re: nvgraph, NVIDIA is one of Tim Davis's research sponsors and uses his code, as is (and does) Google. Among other things, he also wrote the open-source NVIDIA CULA sparse matrix library. See the list of libraries at the bottom of this page (scroll down) http://faculty.cse.tamu.edu/davis/research.html

Re: cuStinger, it's no longer...it's now Hornet, which uses CUB by NVIDIA Research https://github.com/hornet-gt/hornet

Re: gunrock, they have plans on adding GraphBLAS as part of their backend: https://github.com/gunrock/gunrock/issues?utf8=%E2%9C%93&q=i...

Re: GraphBLAS, see my comment from yesterday...

Log(Graph): A Near-Optimal High-Performance Graph Representation (2018)

https://news.ycombinator.com/item?id=18099520

For an overview of GraphBLAS in the context of Heterogeneous High-Performance Computing (HHPC) systems such as NVIDIA GPUs or 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

And 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

... That makes it sound like the implementations are not meant for direct long-term use, as folks assume them uncompetitive with tuned GPU versions that show up in hornet et al, and in practice they will be used when no GPU equiv is available / out of convenience? Likewise, even when used directly, it will be by framework / lib devs, so some sort of hornet-blas?

To be clear, the work has been interesting to me for years, so this is purely a practitioner's question as we are not in a position to ship-all-the-things.

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