The Partnership for Advanced Computing in Europe (PRACE) is an international non-profit association with its seat in Brussels. The PRACE Research Infrastructure provides a persistent world-class high performance computing service for scientists and researchers from academia and industry in Europe.
Learn how to identify and solve software bugs in your codeWant to improve your ability to identify and fix bugs in code?On this course, you’ll discover how to reduce bugs during software development. You’ll learn with examples in both C and Fortran programming languages and understand how to catch bugs early using compiler features and writing tests for your code.You’ll learn to find the bugs in your code using the best tools available including debuggers and code analysers. You’ll also look at parallel programs and explore tools for debugging parallel code at scale.By the end of the course, you’ll feel confident writing high-quality and clean code.This is an intermediate level course aimed at people with some programming experience. Although examples will be given in C and Fortran, the principles (and most of the tools) are transferable to other programming languages.
Speed up Python programs using optimisation and parallelisation techniquesThe Python programming language is popular in scientific computing because of the benefits it offers for fast code development. The performance of pure Python programs is often suboptimal, but there are ways to make them faster and more efficient.On this course, you’ll find out how to identify performance bottlenecks, perform numerical computations efficiently, and extend Python with compiled code. You’ll learn various ways to optimise and parallelise Python programs, particularly in the context of scientific and high performance computing.The course is designed for Python programmers who want to speed up their codes. You should be familiar with the basics of the Python programming language.The software needed is in the virtual machine that you will need to download and run to complete this course. You will also need a local machine with 15GB free disk space and 2GB RAM.Optionally, you can receive instructions to install the Python environment utilised in the course (Python, Numpy, Cython, mpi4py).
You will experience how to use RHadoop tool to manage and analyse big data.This course will give you access to a virtual environment with installations of Hadoop, R and Rstudio to get hands-on experience with big data management. Several unique examples from statistical learning and related R code for map-reduce operations will be available for testing and learning.Those with basic knowledge in statistical learning and R will better understand the methods behind and how to run them in parallel using map-reduce functions and Hadoop data storage. At the end of the course you will get access to RHadoop on a supercomputer at University of Ljubljana.This course is designed for people interested in data science, computational statistics and machine learning and have basic experiences with them. It will be also useful for advanced undergraduate students and first year PhD students in data analysis, statistics or bioinformatics, who wish to understand how to manage big data with Hadoop using R programming language.We expect that the learners will also have basic experiences with linux and bash and working experiences with R and matrix operations. They should be also capable to download and run virtual machine.All software needed to actively participate the course is provided within the virtual machine that the followers are supposed to download and run on the local machine. No extra software is needed.You will need a modest local machine with 15GB free disk space and 2GB RAM.