guest ::
| Home > Publications database > Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning |
| Home > Publications database > Scaling data-intensive analytics with Heat: a Python library for massively-parallel array computing and machine learning |
| Poster (After Call) | FZJ-2024-07444 |
; ; ; ; ; ; ; ;
2024
Abstract: 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 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 short: Heats objective is to make array computing and machine learning as easy on a CPU/GPU-cluster as it is on a workstation.Our poster provides an overview of Heats design principles, its current features and capabilities, and discusses its role in the ecosystem of distributed array computing and machine learning in Python.
|
The record appears in these collections: |