This course is organised in collaboration with EuroCC Czechia and EuroCC Germany.
Annotation
JAX is a high-performance Python library for numerical computing. Although it is popular for machine learning applications, its true superpower for computational scientists lies in its features that enable efficient numerical computing. JAX provides a familiar, NumPy-style API that seamlessly compiles and runs high-performance code on GPUs and TPUs without requiring specialised parallel programming paradigms.
This course is tailored specifically for computational scientists and HPC researchers. It introduces JAX features that benefit HPC-focused simulation codes. With practical hands-on exercises, you will explore how to improve numerical computation using JAX.
Level
beginner, intermediate
Prerequisites
- Basic programming skills in Python.
- Familiarity with NumPy or SciPy.
- Basic understanding of HPC systems.
- Familiarity with the fundamental concepts of MPI is helpful but not mandatory.
Language
English
Tutor
Saheed Bolarinwa
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Acknowledgements
EuroCC 3 has received funding from the European High-Performance Computing Joint Undertaking (JU) under Grant Agreement No. 101306701. The JU receives support from European Union‘s Digital Europe Programme and Germany, Albania, Austria, Belgium, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Finland, France, Greece, Hungary, Iceland, Ireland, Italy, Latvia, Lithuania, Luxembourg, Malta, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Türkiye, Kosovo. The EuroCC 3 project received funding from the Ministry of Education, Youth and Sports of the Czech Republic.
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This course was supported by the Ministry of Education, Youth and Sports of the Czech Republic through the e-INFRA CZ (ID:90254).
All presentations and educational materials of this course are provided under the Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license.