Application of scalable deep learning in High energy physics usgin Nurion HPC ( Kisti )

• Abstract in Korean Supercomputing Conference & Korean Society for Computational Science and Engeering (http://myrgst.com/ksc2021/file/kscse/2021CSE_Autumn_Poster06.pdf)
• Abstract in Korean Physics Society (http://www.kps.or.kr/conference/event/content/program/search_result_abstract.php?id=1692&tid=162)
• Current Github organization: (https://github.com/nurion4hep)

Awards

• https://github.com/ico1036/Nurion/blob/master/Images/KPS_2020_Awards.pdf
• https://github.com/ico1036/Nurion/blob/master/Images/KSC_2021_Awards.jpg

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AtoZ in Nurion

1. Clone the offical Nurion repository

git clone https://github.com/nurion4hep/HEP-CNN.git

2. Install PyTorch and related libs in Nurion

./HEP-CNN/scripts/install_torch_nurion.sh

3. Setup Conda Environment

source HEP-CNN/scripts/setup.sh

• h5py, pandas, matplotlib 등 python lib는 이 conda 환경 위에서 사용합시다

4. Copy data from Jiwoong's directory to HEP-CNN/data

• 64x64 iamge

  • Kisti server: /xrootd/store/user/jiwoong/HEP_CNN_OFFICIAL/trackpt_hdf5_32PU_64x64
  • Nurion : /scratch/hpc22a03/HEP-CNN/data/trackpt_hdf5_32PU_64x64

• 224x224 image

  • Kisti server: /xrootd/store/user/jiwoong/HEP_CNN_OFFICIAL/trackpt_hdf5_32PU_224x224
  • Nurion : /scratch/hpc22a03/HEP-CNN/data/trackpt_hdf5_32PU_224x224

5. Set data path

vi HEP-CNN/run/config.yml

6. Make distributed-running script

qsub run.sh