Handling and analyzing massive data sets is highly important for the vast majority of research communities, but it is also challenging, especially for those communities without a background in high-performance computing (HPC). The Helmholtz Analytics Toolkit (Heat) library offers a solution to this problem by providing memory-distributed and hardware-accelerated array manipulation, data analytics, and machine learning algorithms in Python, targeting the usage by non-experts in HPC.

In this presentation, we will provide an overview of Heat's current features and capabilities and discuss its role in the ecosystem of distributed array computing and machine learning in Python.