Heat

Goals

Heat is a flexible and seamless open-source software for high performance data
analytics and machine learning. It provides highly optimized algorithms and data
structures for tensor computations using CPUs, GPUs and distributed cluster
systems on top of MPI. The goal of Heat is to fill the gap between data
analytics and machine learning libraries with a strong focus on single-node
performance, and traditional high-performance computing (HPC). Heat's generic
Python-first programming interface integrates seamlessly with the existing data
science ecosystem and makes it as effortless as using numpy to write scalable
scientific and data science applications.

Heat allows you to tackle your actual Big Data challenges that go beyond the
computational and memory needs of your laptop and desktop.

Features

• High-performance n-dimensional tensors
• CPU, GPU and distributed computation using MPI
• Powerful data analytics and machine learning methods
• Abstracted communication via split tensors
• Python API

Citing Heat

If you find Heat helpful for your research, please mention it in your publications. You can cite:

• Götz, M., Debus, C., Coquelin, D., Krajsek, K., Comito, C., Knechtges, P., Hagemeier, B., Tarnawa, M., Hanselmann, S., Siggel, S., Basermann, A. & Streit, A. (2020). HeAT - a Distributed and GPU-accelerated Tensor Framework for Data Analytics. In 2020 IEEE International Conference on Big Data (Big Data) (pp. 276-287). IEEE, DOI: 10.1109/BigData50022.2020.9378050.

@inproceedings{heat2020,
    title={{HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics}},
    author={
      Markus Götz and
      Charlotte Debus and
      Daniel Coquelin and
      Kai Krajsek and
      Claudia Comito and
      Philipp Knechtges and
      Björn Hagemeier and
      Michael Tarnawa and
      Simon Hanselmann and
      Martin Siggel and
      Achim Basermann and
      Achim Streit
    },
    booktitle={2020 IEEE International Conference on Big Data (Big Data)},
    year={2020},
    pages={276-287},
    month={December},
    publisher={IEEE},
    doi={10.1109/BigData50022.2020.9378050}
}

Acknowledgements

This work is supported by the Helmholtz Association Initiative and Networking Fund
under project number ZT-I-0003 and the Helmholtz AI platform grant.