GCGT
duckdb-pgq
| GCGT | duckdb-pgq | |
|---|---|---|
| 1 | 1 | |
| 7 | 34 | |
| - | - | |
| 10.0 | 0.0 | |
| almost 4 years ago | 22 days ago | |
| Cuda | C++ | |
| MIT License | MIT License |
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GCGT
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Vectorizing Graph Neural Networks
> 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).
duckdb-pgq
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Vectorizing Graph Neural Networks
You might be interested in duckdb-pgq[1], working on implementing graph queries support in duckdb. There are some papers online about it as well if you are interested.
1: https://github.com/cwida/duckdb-pgq
What are some alternatives?
efg - GPU based Compressed Graph Traversal
nodevectors - Fastest network node embeddings in the west