# HeAT -- a Distributed and GPU-accelerated Tensor Framework for Data Analytics

arXiv: https://arxiv.org/abs/2007.13552 · HTML: https://mlsanity.com/paper/2007.13552/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [JUWELS Booster -- A Supercomputer for Large-Scale AI Research](https://mlsanity.com/paper/2108.11976/) (2021)
- [Accelerating Neural Network Training with Distributed Asynchronous and Selective Optimization (DASO)](https://mlsanity.com/paper/2104.05588/) (2021)
- [Memory-efficient array redistribution through portable collective communication](https://mlsanity.com/paper/2112.01075/) (2021)

## Abstract (arXiv)

To cope with the rapid growth in available data, the efficiency of data analysis and machine learning libraries has recently received increased attention. Although great advancements have been made in traditional array-based computations, most are limited by the resources available on a single computation node. Consequently, novel approaches must be made to exploit distributed resources, e.g. distributed memory architectures. To this end, we introduce HeAT, an array-based numerical programming framework for large-scale parallel processing with an easy-to-use NumPy-like API. HeAT utilizes PyTorch as a node-local eager execution engine and distributes the workload on arbitrarily large high-performance computing systems via MPI. It provides both low-level array computations, as well as assorted higher-level algorithms. With HeAT, it is possible for a NumPy user to take full advantage of their available resources, significantly lowering the barrier to distributed data analysis. When compared to similar frameworks, HeAT achieves speedups of up to two orders of magnitude.