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initzhang avatar initzhang commented on September 2, 2024 1

Hi, thank you for your interest in our work!

You need to provide the cache allocation plan and other params to the run_ducati.py script

CUDA_VISIBLE_DEVICES=0 python run_ducati.py --dataset ogbn-papers100M --fanouts 10,25 --fake-dim 128 --adj-budget 0.142 --nfeat-budget 0.858

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BearBiscuit05 avatar BearBiscuit05 commented on September 2, 2024

Hi, with your help I successfully ran the results, thank you very much. After studying the source code, I would like to ask, did you obtain the results by replacing the original neighbor sampling function in dgl with the CSRRowWiseSamplingUniformWithCache function? Are there any other optimizations?

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initzhang avatar initzhang commented on September 2, 2024

Hi, there are two major differences between DUCATI and previous cache works. (1) propose the adj cache design and thus support sampling with cache as you mentioned (2) develop a dual-cache algorithm to solve the cache contention problem.

The first part is implemented in both DUCATI and customized DGL, the second part is implemented only in DUCATI’s code. If you simply substitute the neighbor sampling function, the code cannot work . You can find the details about the contributions in our paper.

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BearBiscuit05 avatar BearBiscuit05 commented on September 2, 2024

Thank you very much for your response, I have understood.

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