Cluster-Based Visual Analytics System for HPC Performance Data

About

• Implementation of two-step DR (PCA+UMAP) with contrastive clusters for feature contributions.
• Interactive mrDMD to adjust metric baselines and compute per-node devation from baseline(s).

Requirements

• Python3
• Note: Tested on macOS Tahoe and Ubuntu 24.04 LTS.

Frontend (React)

Setup

1. cd ui
2. npm install

Usage

1. cd ui
2. npm run start

Backend (Flask)

Setup

1. cd server
2. Ensure you're using Python 3.13. If you have pyenv installed, it should automatically switch Python versions when you cd into server/.
3. python -m venv .venv
4. source .venv/bin/activate (Repeat this whenever you start a new terminal)
5. pip install -r requirements.txt
6. Install CCPCA package

   1. Download ccpca repo as zip from https://github.com/takanori-fujiwara/ccpca:

   2. Download ccpca repo as zip

   3. Unzip in /server

   4. cd ccpca-master

   5. If you're on MacOS and use Homebrew, update the path to Eigen on lines 46 and 50 /ccpca-master/ccpca/presetup.py as follows:

      ...
      print("building cPCA")
      os.system(
          f"c++ -O3 -Wall -mtune=native -march=native -shared -std=c++11 -undefined dynamic_lookup -I/opt/homebrew/include/eigen3/ $(python3 -m pybind11 --includes) cpca.cpp cpca_wrap.cpp -o cpca_cpp{extension_suffix}"
      )
      print("building ccPCA")
      os.system(
          f"c++ -O3 -Wall -mtune=native -march=native -shared -std=c++11 -undefined dynamic_lookup -I/opt/homebrew/include/eigen3/ $(python3 -m pybind11 --includes) cpca.cpp cpca_wrap.cpp ccpca.cpp ccpca_wrap.cpp -o ccpca_cpp{extension_suffix}"
      )
      ...

   6. Install both ccpca/ccpca/ and ccpca/fc_view/ as instructed in https://github.com/takanori-fujiwara/ccpca/blob/master/README.md

Usage

1. cd server
2. source .venv/bin/activate
3. python server.py

References

1. Takanori Fujiwara, Shilpika, Naohisa Sakamoto, Jorji Nonaka, Keiji Yamamoto, and Kwan-Liu Ma, "A Visual Analytics Framework for Reviewing Multivariate Time-Series Data with Dimensionality Reduction". IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1601-1611, 2021. code
2. Takanori Fujiwara, Oh-Hyun Kwon, and Kwan-Liu Ma, "Supporting Analysis of Dimensionality Reduction Results with Contrastive Learning". IEEE Transactions on Visualization and Computer Graphics, 2020. DOI: 10.1109/TVCG.2019.2934251 code
3. Kutz, J. N., Fu, X., and Brunton, S. L., 2016, “Multiresolution Dynamic Mode Decomposition”, SIAM J. Appl. Dyn. Syst., 15 (2), pp. 713-735.
4. B. W. Brunton, L. A. Johnson, J. G. Ojemann, and J. N. Kutz, “Extracting spatial–temporal coherent patterns in large-scale neural recordings using dynamic mode decomposition,” Journal of Neuroscience Methods, vol. 258, 2016.
5. mrDMD code modified from https://humaticlabs.com/blog/mrdmd-python/