Scaling Data-Intensive Analytics with HEAT: A Python Library for Massively-Parallel Array Computing and Machine Learning
Monday, May 13, 2024 3:00 PM to Wednesday, May 15, 2024 4:00 PM · 2 days 1 hr. (Europe/Berlin)
Foyer D-G - 2nd floor
Research Poster
High-Performance Data AnalyticsML Systems and ToolsNumerical LibrariesScalable Application Frameworks
Information
Poster is on display and will be presented at the poster pitch session.
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.
Contributors:
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.
Contributors:
Format
On-site
