GCGT

Source code for the paper: GPU-based Compressed Graph Traversal (by desert0616)

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NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better GCGT alternative or higher similarity.

GCGT reviews and mentions

Posts with mentions or reviews of GCGT. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-07-03.
  • Vectorizing Graph Neural Networks
    4 projects | news.ycombinator.com | 3 Jul 2023
    > I believe you are not guaranteed for the edge data of adjacent nodes to be adjacent in memory

    The edge data of a particular node is contiguous, but yes, the edge data of a collection of nodes is not contiguous. You can reorder (permute) the graph for some metric as a preprocessing step so that you get better locality. This only works for static graphs though.

    > For float-based edge data I think quantization works well, and I believe you can further compress the ROW/COL indices

    Yes, index compression is pretty well studied and understood. The challenge here is mostly good compression ratio and high decompression performance. There are a couple of works that I'm aware of that do this for gpus. This repo by Mo Sha et al. (https://github.com/desert0616/GCGT) is pretty good, and I also did some work in this space (https://github.com/pgera/efg).

Stats

Basic GCGT repo stats
1
7
10.0
almost 4 years ago

desert0616/GCGT is an open source project licensed under MIT License which is an OSI approved license.

The primary programming language of GCGT is Cuda.


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