Frugal

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for paper @ ASPLOS‘25’

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# [ASPLOS'25 Artifact] Frugal: Efficient and Economic Embedding Model Training with Commodity GPUs

## Environment Setup

We provide a Dockerfile to simplify the environment setup. Please install `docker` and `nvidia-docker` first by checking [URL](https://docs.docker.com/engine/install/ubuntu/) and [URL2](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html).

In ubuntu, simply:

curl -fsSL https://get.docker.com -o get-docker.sh
	sudo sh ./get-docker.sh
	curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
  	&& curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
	sudo apt-get update
	sudo apt-get install -y nvidia-container-toolkit

After installing docker and nvidia-docker, build the Docker image by running the following command in the `dockerfiles` directory:

cd dockerfiles
	sudo docker build -f Dockerfile.frugal --build-arg uid=$UID  -t frugal .
	cd -

And then start this container, by running the following commands. **Please modify corresponding pathes below**.

sudo docker run --cap-add=SYS_ADMIN --privileged --security-opt seccomp=unconfined --runtime=nvidia --name frugal --net=host -v /home/xieminhui/Frugal:/home/xieminhui/Frugal  -v /dev/shm:/dev/shm -v /dev/hugepages:/dev/hugepages -v /home/xieminhui/FrugalDataset:/home/xieminhui/FrugalDataset -v /home/xieminhui/dgl-data:/home/xieminhui/dgl-data -v /dev:/dev -w /home/xieminhui/Frugal --rm -it --gpus all -d frugal

or 
	
	cd dockerfiles && bash start_docker.sh && cd -

Enter the container.

sudo docker exec -it frugal /bin/bash

**We provide a script for one-click environment initialization**. Simply run the following command **in the docker** to set up the environment:

(inside docker) cd dockerfiles
	(inside docker) bash init_env_inside_docker.sh

## Build Frugal

(inside docker) mkdir build
	(inside docker) cd build
	(inside docker) cmake .. -DCMAKE_BUILD_TYPE=Release

## Evaluate the Artifact

All experiments are consolidated into a single script located at `src/kg/scripts/bench.py`.

Simply run the script to execute all experiments:

(inside docker) cd src/kg/scripts/
	(inside docker) python bench.py

The logs for all experiments will be generated in the `log` folder at the project root.

All experiments' configurations can be checked at `src/kg/scripts/exp_config.py`.

## Plot the figures

We provide three Python notebooks (.ipynb): `ae-micro.ipynb`, `ae-graph.ipynb`, and `ae-rec.ipynb`, which generate all the experimental plots for the microbenchmark, Knowledge Graph (KG), and Recommendation (REC) experiments, respectively.
They are located at `draw_figures` directory.

All figures and their corresponding raw data tables are plotted within each notebook, **with the experiment numbering consistent with that used in the paper (in the form of Exp#7).**

Please install the Jupyter extension in VSCode. Then, please directly open three notebooks in the `draw_figures` directory.

## Thanks for citing our paper

@inproceedings{xie2025frugal,
		title={Frugal: Efficient and Economic Embedding Model Training with Commodity GPUs},
		author={Xie, Minhui and Zeng, Shaoxun and Guo, Hao and Gao, Shiwei and Lu, Youyou},
		booktitle={Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 1},
		pages={509--523},
		year={2025}
	}

If you have any questions, feel free to email me at `xieminhui AT ruc.edu.cn`.

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