Comments (4)
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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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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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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Thank you very much for your response, I have understood.
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Related Issues (10)
- The CUDA code is not provided in the source code, can you provide it?CUDA code HOT 1
- Can you provide a runtime environment based on Docker images? HOT 1
- How to obtain test results for accuracy? HOT 6
- Testing the dc_dgl installation result prompts ''CUDA: an illegal memory access was encountered'' HOT 2
- When I run the preprocess/PA.py,I encounter the ValueError: NULL pointer access error HOT 7
- Question for function "DUCATI.CacheConstructor.separate_features_idx" HOT 2
- Index error while running run_allocate file for UK Union dataset HOT 1
- Mismatch in Number of Nodes for Friendster Dataset HOT 1
- Request for explaining functional correctness of fast_reorder HOT 1
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