Training course
Accelerating Massive Data Processing in Python with Heat
9 Nov 2026 10:00 - 15:00
This hands-on tutorial introduces the Heat library, which is designed to scale Python-based array computing and data science workflows to distributed and GPU-accelerated environments. Heat offers a familiar NumPy-like API while distributing memory-intensive operations using PyTorch and mpi4py.
Practical information:
9 Nov 2026 10:00 - 15:00
5 hours
online or on-site at the Jülich Supercomputing Centre, Building 16.3, Room 211 (Germany)
English
Target audience: Researchers and Research Software Engineers (RSEs) working with large datasets that exceed the memory of a single machine.
Want to register?
- Prerequisites: Participants should have experience with Python and its scientific ecosystem (e.g. NumPy, SciPy). A basic understanding of MPI is helpful but not required. A personal institutional email address and laptop is required
- Price: Price upon request
Georganiseerd door:
Topics covered include:
- Heat Fundamentals: Get started with distributed arrays (DNDarrays), distributed I/O, data decomposition schemes, and array operations.
- Key Functionalities: Explore the multi-node linear algebra, statistics, signal processing, and machine learning capabilities.
- DIY Development: Learn how to use Heat's infrastructure to build your own multi-node, multi-GPU capable research applications.
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