[ONLINE] Parallel Data Analysis with R
ZOOM
ONLINE
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This course is organised in collaboration with Euro Czechia and EuroCC Slovenia.
Annotation
This training is focused on different approaches to how the R code can be parallelised, going from a local and single-node approach with the parallel package to multi-node parallelisation with the Rmpi package.
The second part is focused on increasing the efficiency of computation by introducing Rcpp for seamless integration of C++ code into R code.
Benefits for the attendees, what will they learn:
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Learn how to accelerate R computations on multicore workstations and HPC systems using R parallel programming frameworks and best practices.
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Develop scalable and reproducible data-analysis workflows that run efficiently on laptops and Slurm-based supercomputers.
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Performance optimisation techniques include vectorisation, parallel processing, compiled C++ code with Rcpp, and efficient data management with modern tools.
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Gain hands-on experience with real-world computational workloads, such as large-scale data analysis.
Prerequisites:
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Basic programming skills in R.
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Familiarity with fundamental R concepts, including vectors, data frames, functions, and package management.
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Experience with basic data analysis tasks such as data manipulation, visualisation, or statistical modelling in R.
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Basic understanding of computer architecture concepts (CPU cores, memory, processes) is beneficial but not required.
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Prior experience with Linux or HPC systems is helpful, but no previous knowledge is assumed.
Technical requirements
Participants should have a laptop and internet access.
Level
intermediate
Language
English
Tutors
prof. dr. Janez Povh is a professor of mathematics at the University of Ljubljana and director at Rudolfovo – Science and Technology Centre, Novo mesto. He holds BSc, MSc and PhD in Mathematics. His research work focuses on (i) the development of methods and tools for solving complex optimisation problems, and on (ii) the analysis and modelling of big data. Due to the high computational complexity of these areas, his work strongly relies on using supercomputers and quantum computers. He uses the best available supercomputers in the EU and some of the few available quantum computers, especially those based on quantum annealing.
Tomáš Martinovič obtained his Ph.D. in computational sciences at IT4Innovations, VSB - Technical University of Ostrava in 2018. From 2015 to 2018 he worked in a team focused on the analysis of complex dynamical systems, where he worked on scalable implementations of algorithms from the field of nonlinear time series analysis. Since the start of 2022, he leads a team focused on machine learning/AI and operations research with the defined objective of research and transfer of knowledge in cooperation with industry.
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.