Write high-performance, parallel, and GPU-accelerated code — all in one elegant language.
Why this lesson matters
What if you could combine the performance of C or Fortran with the simplicity of Python? Julia for High-Performance Computing makes that possible — a modern language for scientific computing that’s fast, open-source, and easy to write.
💡 Perfect for developers, researchers, and data scientists who want to scale up their code without switching languages.
What you’ll learn
By the end of this Julia for High-Performance Computing lesson, you’ll be able to:
- Decide when and how to use Julia in your next HPC project
- Write efficient, performant Julia code for compute-intensive workloads
- Use Julia’s built-in tools for threading and distributed computing
- Run Julia applications on HPC systems
- Port Julia code to GPUs for accelerated execution
Who it’s for
This course is designed for:
- Researchers, developers, and students with experience in programming (Python, R, C/C++, Fortran, Matlab, etc.)
- Professionals who want to unify productivity and performance in a single language
- Anyone exploring HPC, GPU computing, or large-scale data analysis with Julia
🧠 Before joining, participants should be familiar with basic Julia syntax and HPC fundamentals (threading, multiprocessing, or GPU concepts).
Behind the lesson
This Julia lesson was developed by the ENCCS training team to help researchers and engineers get the most out of modern HPC environments. It builds on the Introduction to Julia course and connects directly to Julia for High-Performance Data Analysis.
🎓 This lesson is completely free and open source.
