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Vectorizing Graph Neural Networks (2020)
- VHRanger 3y agoYes, people working on graph based ML realize quickly that the underlying data structures most originally academic libraries (networkX, PyG, etc.) use are bad. I wrote about this before [1] and based a node embedding library around the concept [2]. The NetworkX style graphs are laid out as a bunch of items in a heap with pointers to each other. That works at extreme scales, because everything is on a cluster's RAM and you don't mind paying the latency costs of fetch operations. But it makes little sense for graphs with < 5B nodes to be honest. Here's the dream (remains to be implemented by someone): Laying out the graph as a CSR sparse matrix makes way more sense because of data locality. You have an array for edges per node, an index pointer array, and then one matrix with a row per edge for edge data, and one matrix with a row per node for node data. Ideally you code the entire thing with apache arrow memory to ease access to other libraries/languages/ At larger scales, you could just leave the CSR array data on NVMe drives, and you'd still operate at 500mb/s random query throughput with hand coded access, ~150mb/s with mmap. [1] https://www.singlelunch.com/2019/08/01/700x-faster-node2vec-models-fastest-random-walks-on-a-graph/ https://www.singlelunch.com/2019/08/01/700x-faster-node2vec-... [2] https://github.com/VHRanger/nodevectors https://github.com/VHRanger/nodevectors
- Epa095 3y agoYou 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 https://github.com/cwida/duckdb-pgq
- bootsmann 3y agoPyG supports sparse adjacency matrices actually, but I remember it being a pain to get right the last time I tried it.
- fock 3y agoisn't it the default? At least I have the feeling it was in pre-PyG-days.
- anonymousDan 3y agoDoes the design differ for training vs inference/prediction?
- VHRanger 3y agoDepends on the model application? If you have to carry around the graph for inference, then you want it compact. If you just fold the graph into some model, then it doesnt really matter by inference time
- WinLychee 3y agoHave seen this coded in practice, holding the entire graph in memory (millions of vertices, billions of edges). It was pretty darn fast. A question I have wrt memory access for CSR: is the access pattern suboptimal still? While there is less pointer chasing, I believe you are not guaranteed for the edge data of adjacent nodes to be adjacent in memory, thus there is still potential for many cache misses. Another open question is whether you can further compress the CSR representation. For float-based edge data I think quantization works well, and I believe you can further compress the ROW/COL indices (delta encoding for the former). Compression potentially helps here because you can fit more in cache and can shove more edges into the CPU.
- pgera 3y ago> 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 for your average access pattern. 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 https://github.com/desert0616/GCGT) is pretty good, and I also did some work in this space (https://github.com/pgera/efg https://github.com/pgera/efg).