EHPCSW18/PRACEdays18
Workshop 1
Title: Parallel computing in bioinformatics research
Target audience: master students, PhD students, postdoctoral researchers, other students and researchers
Brief contents: Short introductory lecture will cover basic concepts of parallel computing principles for life sciences. Hands-on tutorial will introduce the subject of metagenomics. Students will prepare and run dedicated workflow for the analysis of metagenomics data on HPC.
Organisers: dr. Andrej Kastrin, dr. Brane Leskošek, University of Ljubljana, Faculty of medicine
Contributors: ELIXIR Slovenia (Institute of Biostatistics and Medical Informatics (ULMF), Jožef Stefan Institute (IJS) and Arnes) and ELIXIR Spain (University of Malaga (UMA)).
Time slot: Wednesday 30 May 2018, Seminar 1, 14:30 – 16:30
Prerequisites:
- Basic knowledge with Linux command line
- No previous experience with HPC is required
- Desktop computer or laptop with MS Windows, Linux, or Mac OS X platform with relative new web browser installed (Firefox or Chrome)
Programme
- Motivation to use HPC system (B. Leskošek, A. Kastrin, and P. Ferk, ULMF)
Big data explosion in the life sciences, high performance computing technology in the era of massive data in bioinformatics; a basic overview of metagenomics;
- HPC ecosystem - HPC facilities available to researchers in Europe and Slovenia (J. J. Javoršek, JSI)
An overview on basic HPC facilities available to life science researchers in Europe with special attention to Slovenia.
- HPC platforms architecture and parallel computing (O. Trelles, UMA)
Overview of basic HPC platforms and their architecture.
- Using HPC system for metabolomic analysis (E. Perez Wohlfeil, UMA)
A dedicated workflow for basic metabolomic analysis; tutorial about access and user interface to software environment (batch schedulers and resource allocation); running jobs and dealing with errors; how to compile code and build applications using additional libraries; performance tuning.
Workshop 2
Title: Parallel computing in chemistry and chemical engineering
Target audience: master students, PhD students, postdoctoral researchers, other students and researchers
Brief contents: Lectures will cover basic concepts of parallel computing principles for chemistry and chemical engineering. Hands-on tutorial will introduce the subject of molecular dynamics, Monte Carlo methods and Lattice Boltzmann Method.
Organisers: dr. Tomaž Urbič and dr. Jurij Reščič (University of Ljubljana, Faculty of chemistry and chemical engineering - ULFCCI)
Contributor: dr. Franci Merzel, National Institute of Chemistry in Ljubljana
Time slot: Thursday 31 May 2018, Seminar 2, 14:30 – 16:30
Prerequisites:
- Basic knowledge of Linux and concepts of computational chemistry
Desktop or Laptop with Linux OS.
Programme
- Atomistic methods for materials and biological systems:
- An overview of basic applications in simulating dynamical processes
in complex systems. - Tutorial on performing molecular dynamics: system construction, and
apropriate simulation protocols, running sumulations on HPC facilities
Workshop 3
Title: Big data analysis with RHadoop
Target audience: master students, PhD students, postdoctoral researchers, other students and researchers
Brief contents: Short introductory lecture will cover basic concepts of big data management and analysis using RHadoop. Students will get access to HPC available at ULFME where all necessary software will be preinstalled. Within this working space they will create, store, load big data files and perform basic statistics above them.
Organisers: dr. Leon Kos and dr. Janez Povh, University of Ljubljana, Faculty of mechanical engineering.
Contributors: dr. Janez Povh, dr. Leon Kos, Timotej Hrga
Time slot: Tuesday May 29 2018, Seminar 5, 14:30 – 16:30
Prerequisites:
- Basic knowledge of Linux command line
- Basic knowledge of R and statistics;
- Own laptop.
Programme
- Introduction to big data and Hadoop (dr. Janez Povh)
- Connecting to HPC at ULFME and first experiences with it (dr. Leon Kos)
- Hands-on exercises (dr. Janez Povh, Timotej Hrga):
- how to create (retrieve) big data, store it into the distributed file system and load it back;
- how to write and run few simple map-reduce functions for basic big data analyses (computing group centroids, finding the outliers, word count example).
