Höchstleistungsrechenzentrum Stuttgart: Julia for High-Performance Computing

Prerequisites and content levels

Prerequisites

Basic programming experience in any language.
Familiarity with UNIX/Linux (esp. basic terminal usage) is recommended.
Elemental Julia and/or HPC knowledge is a plus.

(If you want to follow along on your personal laptop, make sure to have Julia 1.9 and Jupyter installed and working.)

Content levels

Basic: 7 hours
Intermediate: 11 hours
Advanced: 5 hours

Learn more about course curricula and content levels.

Instructor

Dr. Carsten Bauer (Paderborn Center for Parallel Computing)

Learning outcomes

After this course, participants will:

have a basic understanding of Julia’s fundamental design and its compilation pipeline
know how to write efficient, serial Julia and avoid typical pitfalls
know how to profile and benchmark code performance
be able to parallelize Julia using multithreading, distributed computing and GPU acceleration
be familiar with common (remote) development workflows

Agenda

Local registration starts on first course day at 8:30.

The course runs from Tuesday to Friday. Days start at 9 AM and end at 5 PM (earlier end on Friday).
On the first evening there will be a social event (Stuttgart city tour + dinner). A detailed schedule will
follow.
Among others, the following topics will be covered in the course:

• Julia fundamentals

Julia’s type system and multiple dispatch paradigm
Code specialization and compilation
Duck typing and generic programming
Development workflows and reproducibility

• Performance programming

Benchmarking and profiling (statistical and instrumented)
Type instabilities
Syntactic loop fusion
SIMD

• Parallelisation

Multithreading
Distributed computing (e.g. MPI)
GPU acceleration via array abstractions and CUDA kernels

Moreover, there will be an experience talk by Dr. Bernat Font from Barcelona Supercomputing Center who will share his experience of using Julia for HPC, comparing multi-threaded CPU execution and GPU execution.
Although it won’t be identical, you may check out the content and schedule of last years course
at github.com/carstenbauer/JuliaHLRS22 for further information.

Course material

Slides and Jupyter notebooks will be available for all participants on GitHub.
The material can be found on https://github.com/carstenbauer/JuliaHLRS23 .

HLRS concept for on-site courses

Besides the content of the training itself, an important aspect of this event is the scientific exchange among the participants. We try to facilitate such communication by

a social event on the evening of the first course day,
offering common coffee and lunch breaks and
working together in groups of two during the exercises.

Registration-information

Register via the button at the top of this page.
We encourage you to register to the waiting list if the course is full. Places might become available.

Registration closes on September 24, 2023.

Fees

Students without master’s degree or equivalent: 35 Euro
PhD students or employees at a German university or public research institute: 75 Euro
PhD students or employees at a university or public research institute in an EU, EU-associated or PRACE country other than Germany: 150 Euro
PhD students or employees at a university or public research institute outside of EU, EU-associated or PRACE countries: 300 Euro
Other participants, e.g., from industry, other public service providers, or government: 780 Euro

Link to the EU and EU-associated (Horizon Europe), and PRACE countries.

Our course fees include coffee breaks (in classroom courses only).

Contact

Lucienne Dettki, phone 0711 685 63894, dettki(at)hlrs.de
training(at)hlrs.de

HLRS Training Collaborations in HPC

HLRS is part of the Gauss Centre for Supercomputing (GCS), together with JSC in Jülich and LRZ in Garching near Munich. EuroCC@GCS is the German National Competence Centre (NCC) for High-Performance Computing. HLRS is also a member of the Baden-Württemberg initiative bwHPC.

This course is provided within the framework of the bwHPC training program.

Further courses

See the training overview and the Supercomputing Academy pages.