search

YukeWang96 / GNNAdvisor_OSDI21

Artifact for OSDI'21 GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs.
https://yukewang96.github.io/OSDI21_AE/
63 stars 20 forks source link

Fair comparison with DGL #2

Closed PeterSwiss closed 2 years ago

PeterSwiss commented 2 years ago

I notice that the baseline models (directly imported from DGL) include some operations such as BatchNorm, while your models in the codebase do not. So do your models produce the correct output as in the original GNN paper? Is that the reason for the shorter latency than DGL in the paper?

YukeWang96 commented 2 years ago

Hi, thanks for your question.

In our comparison DGL baseline, we do not consider any additional batched normalization operation on the feature embedding matrix, as shown in our DGL baseline for the GCN model. https://github.com/YukeWang96/OSDI21_AE/blob/6ea6a211248faba7637025e2269423b42ee923ac/dgl_baseline/gcn.py#L6-L29

Here is the reference to graphconv forward function implementation. https://github.com/dmlc/dgl/blob/a7b5085a5d88bd90a29e5acef929f6278ddc9528/python/dgl/nn/pytorch/conv/graphconv.py#L337-L455

PeterSwiss commented 2 years ago

I see, thanks.