This is the repo for Skywalker, an Efficient Alias-method-based Graph Sampling and Random Walk framework on GPUs.

Graph sampling and random walk operations, capturing the structural properties of graphs, are playing an important role today as we cannot directly adopt computing-intensive algorithms on large-scale graphs. Existing system frameworks for these tasks are not only spatially and temporally inefficient, but many also lead to biased results. This paper presents Skywalker, a high-throughput, quality-preserving random walk and sampling framework based on GPUs. Skywalker makes three key contributions: first, it takes the first step to realize efficient biased sampling with the alias method on a GPU. Second, it introduces well-crafted load-balancing techniques to effectively utilize the massive parallelism of GPUs. Third, it accelerates alias table construction and reduce the GPU memory requirement with efficient memory management scheme. We show that Skywalker greatly outperforms the state-of-the-art CPU-based and GPUbased baselines, in a wide spectrum of workload scenarios.

For details, please first refer to our 2021 PACT paper "Skywalker: Efficient Alias-Method-Based Graph Sampling and Random Walk on GPUs" by Pengyu Wang, Chao Li, Jing Wang, Taolei Wang, Lu Zhang, Jingwen Leng, Quan Chen, and Minyi Guo. If you have any questions, please be free to contact us.

Beyond the contributions mentioned before, we further extend the framework to multi-gpu version and make a series of optimizations. The new work, named Skywalker+, has been submitted to TPDS.

git clone https://github.com/wpybtw/skywalker_artifact --recursive

Note that Cmake is not correctly setted yet. We use cmake to build glfags and then make.

cd build
cmake ..
make -j
cd ..
make

When evaluating Skywalker, we use 7 commonly used Graph datasets: web-Google, Livejournal, Orkut, Arabic-2005, UK-2005, Friendster, and SK-2005. The datasets can be downloaded from SNAP and Webgraph. You can also execute Skywalker on your preferred datasets, as long as the datasets are processed correctly as mentioned in the section of Preprosessing.

Skywalker uses Galios graph format (.gr) as the input. Other formats like Edgelist (form SNAP) or Matrix Market can be transformed into it with GALOIS' graph-convert tool. Compressed graphs like Webgraph need to be uncompressed first. Here is an example:

wget http:https://snap.stanford.edu/data/wiki-Vote.txt.gz
gzip -d wiki-Vote.txt.gz
$GALOIS_PATH/build/tools/graph-convert/graph-convert -edgelist2gr  ~/data/wiki-Vote.txt  ~/data/wiki-Vote.gr

We implemented four different algorithms in Skywalker, namely DeepWalk, PPR, Node2vec and Neighbour Sampling all based on alias method. We support both online and offline sampling, that's constructing the alias table on the fly or for all vertices in one graph dataset at once as a preprocessing procedure. The source code files are placed under ./src and ./include folders. The configuration of Skywalker is set through gflags, and the default values are written in main.cu in the src folder. You can easily checkout what different flags mean with the annotation and change the configuration simply by editting the command line. Here are several samples demonstrating the inputs of Skywalker:

Execute Deepwalk:

./bin/main --deepwalk --bias=1 --ol=0 --buffer --input ~/data/friendster.w.gr   -v -n 40000

Here, --deepwalk points out what algorithms we are going to execute. --bias=1 --ol=0 means that we want to do biased Deepwalk in offline mode. --buffer means we want to use GPU buffer to enhance performance, --input sets the input graph, -v means print more information and -n assigns the batch size.

Execute node2vec:

 ./bin/main --gmgraph --bias=1 --ol=0 --buffer --input ~/data/friendster.w.gr  --ngpu 4 --node2vec -n 40000

Besides the basic settings in Skywalker, Skywalker+ enables more options. You can assign what kind of memory you are going to use and the number of GPUs to execute Skywalker in multi-GPU pattern.

If you just want to verify the results in our paper, you can simply use the scripts in ./scripts folder where we store all the scripts we used in generating the data when doing the evaluation. As long as you have stored and preprocessed the datasets correctly, all you need to change is the paths in the scripts and the results will automatically be stored in csv format.

Run scriopts:

bash ./scripts/biased.sh

Notice that we also execute several other Graph sampling and random walk frameworks as comparison. Although we also write the configurations of these framworks in our scripts, you may have to also implement their frameworks and set the paths, or you may generate errors in some scripts.

"--newsampler" indicates using Sampler_new for correct result layout. Is it only for unbiased and offline sampling?

Our team has been working on related technologies since 2017. Thank you to everyone for contributing to this project.

Correspondence to:

• Pengyu Wang ([email protected])
• Cheng Xu ([email protected])
• Chao Li ([email protected])
• Jieping Ye([email protected])
• Jing Wang ([email protected])
• Taolei Wang ([email protected])
• Lu Zhang ([email protected])
• Yue Wu([email protected])
• Jingwen Leng ([email protected])
• Quan Chen ([email protected])
• Minyi Guo ([email protected])