• PRACE Training Centres (PTCs)

  • PRACE operates ten PRACE Training Centres (PTCs) and they have established a state-of-the-art curriculum for training in HPC and scientific computing. PTCs carry out and coordinate training and education activities that enable both European academic researchers and European industry to utilise the computational infrastructure available through PRACE and provide top-class education and training opportunities for computational scientists in Europe.
    With approximately 100 training events each year, the ten PRACE Training Centres (PTCs) are based at:

    PTC training events are advertised on the following pages. Registration is free and open to all (pending availability):
    https://events.prace-ri.eu/category/2/

    The following figure depicts the location of the PTC centers throughout Europe.

    PATC events this month:

    February 2020
    Mon Tue Wed Thu Fri Sat Sun
     
    1
     
    2
     
    BIOWEEK 2020

    The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:

    3.2.2020           Using modern HPC environment efficiently
    4.2-5.2.2020    Data analysis with R
    6.2-7.2.2020    Introduction to RNA-seq data analysis

    You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.

    Lecturers (CSC, Finland):  Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri

    Language:  English
    Price:           Free of charge

    Using modern HPC environment efficiently


    Description

    Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.


    Learning outcomes

    After the course, participants:


    will have basic skills in using object storage
    can use object storage services as part of their analysis workflows
    can run batch jobs using Slurm
    will have basic skills in using Singularity containers in HPC environment



    Prerequisites

    The participants are required to have basic skills/knowledge in Command line usage / Linux basics


    Program: see Timetable menu

    Data analysis with R


    Description

    R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.

    The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)


    Learning outcomes

    After attending this course, participants will be able to:


    Navigate RStudio
    Understand R syntax and how to write R code
    Import and export data using R
    Use tidyverse for data wrangling
    Use ggplot2 for creating high-quality plots
    Employ t-tests, linear regression and Chi-squared tests in R



    Prerequisites


    No prior experience of programming or using R is expected
    No data analysis or statistical experience is required (but is likely to be beneficial)



    Program: see Timetable menu

    Introduction to RNA-seq data analysis


    Description

    After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.


    Learning outcomes

    After the course, participants:


    can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps, 
    can run some RNA-seq analysis tools on command line
    import and start analysing their RNA-seq data in R



    Prerequisites

    The participants are required to have basic skills/knowledge in:


    Command line usage / Linux basics
    Current CSC services
    R and Rstudio


    Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses. 


    Program: see Timetable menu
    events.prace-ri.eu/event/965/
    Feb 3 8:00 to Feb 7 15:00
    Scope of the course

    The Python programming language has become more and more popular among researchers for its simplicity and the availability of specific programming libraries, and at the same time the correct exploitation of heterogeneous architectures presents challenges for the development of parallel applications. In order to bring these two topics together, this course is focused on the use of Python on CPU and GPU platforms for scientific computing in general.

     

    General description

    The basic concepts of good programming practices in Python and general parallel programming will be introduced, and then GPU computing will be explained combining the essential theory concepts with hands-on sessions. The proposed exercises will be tested in the supercomputing facilities provided by SURFsara using Python with different programming libraries:


    numba
    PyCUDA
    mpi4py


     

    IMPORTANT INFORMATION: WAITING LIST

    If the course gets fully booked, no more registrations are accepted through this website. However, you can be included in the waiting list: for that, please send an email to training@surfsara.nl and you'll be informed when a place becomes available.
    events.prace-ri.eu/event/946/
    Feb 3 9:00 to Feb 4 17:45
    Registration is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course Convener:  Maria-Ribera Sancho

    Objectives: The course brings together key information technologies used in manipulating, storing, and analysing data including:


    the basic tools for statistical analysis
    techniques for parallel processing
    tools for access to unstructured data
    storage solutions


    Learning outcomes: Students will be introduced to systems that can accept, store, and analyse large volumes of unstructured data. The learned skills can be used in data intensive application areas.

    Level: For trainees with some theoretical and practical knowledge

    Agenda:

    Day 1 (Feb 3)

    9:30 – 13:00 Introduction to Big Data (David Carrera, Data Centric Computing Group Manager, BSC)

    The goal of this session is to introduce the students in the technologies associated with Big Data: data challenges, cloud computing, processing, and internet of things. An overview of the technologies will be provided, both from a technical and from a business model point of view.


    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 – 16:00 Practical Data Analytics for Solving Real World Problems (José Carlos Carrasco Jiménez, Researcher, BSC)
    Data analytics has changed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will propose practical tips gained through our experience at BSC in big data analytics projects. We will also discover how to overcome some of the most challenging tasks in practical data analytics.
    16:00 – 16:30 Coffee break
    16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez, Researcher, BSC)
    This session will focus on several key methods and algorithms (both serial and parallel) that enable to discover global properties on data while dealing with Big Data:
    Network Science
    Multi Constrained and Multi-Objective Optimization
    Examples using the above approaches and some hands-on exercise

     

    Day 2 (Feb 4)

    9:30 – 13:00 Big Data Management (Albert Abelló, UPC, inLab FIB)
    Big Data has many definitions and facets, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically, we'll focus on the Apache Hadoop ecosystem and its two basic components, namely HBase and MapReduce engine.
    11:00 - 11:30 Coffee break
    Hands-on exercise
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 NoSQL databases (Oscar Romero, Dept. of Service and Information System Engineering, UPC-BarcelonaTech)
    The relational model has dominated data storage systems since the mid 1970s. However, the changing storage needs over the past decade have given rise to new models for storing data, collectively known as NoSQL. In this presentation, we will focus on two of the most common types of NoSQL databases: document-oriented databases and graph databases and explain the use cases suitable for each of them.
    16:00 - 16:30 Coffee break
    16:30 - 18:00 Multidisciplinary research and data analytics: Smart Cities (Maria Cristina Marinescu, Computer Applications in Science&Engineering, BSC) 

    A huge quantity of data is produced in cities from many types of sources: IoT, social network, other text sources, images, etc. Data integration is the first and more difficult step to ensure data quality and be able to then analyze these data and get insight hat may help improve quality of life, sustainability, and resilience of the urban fabrics. This session focuses on the variety aspect of big data, and modeling as a way to capture common sense and enable semantic reasoning.

    Day 3 (Feb 5)

    9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    11:00 - 11:30 Coffee break
    Apache Spark has become a consolidated technology for large-scale processing in a fast and general way, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java, Scala, Python and R), extensive documentation and development tools. This course introduces Apache Spark, as well as some of its core libraries for data manipulation, machine learning, data streams and graph analytics.
    13:00 – 14:00 Lunch Break
    14:00 – 15:30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    16:00 – 16:15 Coffee break
    15:30 – 17:00 European project on Big Data

    Day 4 (Feb 6)

    9:30 – 13:00 Practical Introduction to Python Deep Learning  (Jordi Torres, Emerging Technologies for Artificial Intelligence Group Manager - Computer Sciences, BSC)
    Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to companies and research centers, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. The purpose of this short course is to gradually start the student off to the basics of Python Deep Learning, in a practical way through a guided, hands-on learning without becoming too technical, ensuring that the student learn enough of the basics to get literate in Deep Learning. Using the Keras API of TensorFlow library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation. The course content will be as follows:

    PART 1: INTRODUCTION
    1. What is Deep Learning?
    2. Work environment
    3. Python and its libraries

    PART 2: FUNDAMENTALS OF DEEP LEARNING
    4. Densely connected neural networks.
    5. Neural networks in Keras
    6. How a neural network is trained
    7. Parameters and hyperparameters in neural networks
    8. Convolutional neural networks.

    PART 3: DEEP LEARNING TECHNIQUES
    9. Stages of a Deep Learning project
    10. Data to train neural networks
    11. Data Augmentation and Transfer Learning
    12. Advanced neural network architectures

    PART 4: GENERATIVE DEEP LEARNING

    13. Recurrent neural networks
    14. Generative Adversarial Networks

    Important prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python prior to starting the course.

    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja, UPC – Barcelona Tech)
    Data contains information. We will try to contextualize the flow of apparently “new” concepts such as data mining, business intelligence, big data, data science and how they relate to the old school of exploratory statistics. We will also introduce an overview of the main steps of a data mining problem, and we will illustrate them through sound examples of application.
    16:00 - 16:30 Coffee break
    16:30 – 18:00 Data analytics in societal challenges modeling: smart mobility and other related fields (Dra. Mari Paz Linares i Jamie Arjona (UPC, inLab FIB)
    Internet of Things, Big Data, Smart cities or Industry 4.0 are concepts that have raised in the last years with promises of solving daily human issues. In this session we will present how a combination of Internet of Things and Big Data can attack certain challenges and alleviate them.

    Day 5 (Feb 7)

    9:30 – 13:00 Data Visualization Therory (Luz Calvo, User Experience And Interaction Designer, BSC and Juan Felipe Gomez Celis, FrontEnd Developer, BSC)
    Therory



    Basic concepts
    Human perception
    Design
    Colour
    Audience / Validation / Bad practices
    Visualisation design process


    11:00 - 11:30 Coffee break

    Tools for data visualization
    – Tableau
    – Data Wrapper
    – RawGraphs
    – Flourish
    – Carto

    Data visualisation with d3.js


    END of COURSE

     

     
    events.prace-ri.eu/event/910/
    Feb 3 9:30 to Feb 7 16:30
    BIOWEEK 2020

    The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:

    3.2.2020           Using modern HPC environment efficiently
    4.2-5.2.2020    Data analysis with R
    6.2-7.2.2020    Introduction to RNA-seq data analysis

    You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.

    Lecturers (CSC, Finland):  Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri

    Language:  English
    Price:           Free of charge

    Using modern HPC environment efficiently


    Description

    Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.


    Learning outcomes

    After the course, participants:


    will have basic skills in using object storage
    can use object storage services as part of their analysis workflows
    can run batch jobs using Slurm
    will have basic skills in using Singularity containers in HPC environment



    Prerequisites

    The participants are required to have basic skills/knowledge in Command line usage / Linux basics


    Program: see Timetable menu

    Data analysis with R


    Description

    R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.

    The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)


    Learning outcomes

    After attending this course, participants will be able to:


    Navigate RStudio
    Understand R syntax and how to write R code
    Import and export data using R
    Use tidyverse for data wrangling
    Use ggplot2 for creating high-quality plots
    Employ t-tests, linear regression and Chi-squared tests in R



    Prerequisites


    No prior experience of programming or using R is expected
    No data analysis or statistical experience is required (but is likely to be beneficial)



    Program: see Timetable menu

    Introduction to RNA-seq data analysis


    Description

    After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.


    Learning outcomes

    After the course, participants:


    can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps, 
    can run some RNA-seq analysis tools on command line
    import and start analysing their RNA-seq data in R



    Prerequisites

    The participants are required to have basic skills/knowledge in:


    Command line usage / Linux basics
    Current CSC services
    R and Rstudio


    Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses. 


    Program: see Timetable menu
    events.prace-ri.eu/event/965/
    Feb 3 8:00 to Feb 7 15:00
    Registration is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course Convener:  Maria-Ribera Sancho

    Objectives: The course brings together key information technologies used in manipulating, storing, and analysing data including:


    the basic tools for statistical analysis
    techniques for parallel processing
    tools for access to unstructured data
    storage solutions


    Learning outcomes: Students will be introduced to systems that can accept, store, and analyse large volumes of unstructured data. The learned skills can be used in data intensive application areas.

    Level: For trainees with some theoretical and practical knowledge

    Agenda:

    Day 1 (Feb 3)

    9:30 – 13:00 Introduction to Big Data (David Carrera, Data Centric Computing Group Manager, BSC)

    The goal of this session is to introduce the students in the technologies associated with Big Data: data challenges, cloud computing, processing, and internet of things. An overview of the technologies will be provided, both from a technical and from a business model point of view.


    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 – 16:00 Practical Data Analytics for Solving Real World Problems (José Carlos Carrasco Jiménez, Researcher, BSC)
    Data analytics has changed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will propose practical tips gained through our experience at BSC in big data analytics projects. We will also discover how to overcome some of the most challenging tasks in practical data analytics.
    16:00 – 16:30 Coffee break
    16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez, Researcher, BSC)
    This session will focus on several key methods and algorithms (both serial and parallel) that enable to discover global properties on data while dealing with Big Data:
    Network Science
    Multi Constrained and Multi-Objective Optimization
    Examples using the above approaches and some hands-on exercise

     

    Day 2 (Feb 4)

    9:30 – 13:00 Big Data Management (Albert Abelló, UPC, inLab FIB)
    Big Data has many definitions and facets, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically, we'll focus on the Apache Hadoop ecosystem and its two basic components, namely HBase and MapReduce engine.
    11:00 - 11:30 Coffee break
    Hands-on exercise
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 NoSQL databases (Oscar Romero, Dept. of Service and Information System Engineering, UPC-BarcelonaTech)
    The relational model has dominated data storage systems since the mid 1970s. However, the changing storage needs over the past decade have given rise to new models for storing data, collectively known as NoSQL. In this presentation, we will focus on two of the most common types of NoSQL databases: document-oriented databases and graph databases and explain the use cases suitable for each of them.
    16:00 - 16:30 Coffee break
    16:30 - 18:00 Multidisciplinary research and data analytics: Smart Cities (Maria Cristina Marinescu, Computer Applications in Science&Engineering, BSC) 

    A huge quantity of data is produced in cities from many types of sources: IoT, social network, other text sources, images, etc. Data integration is the first and more difficult step to ensure data quality and be able to then analyze these data and get insight hat may help improve quality of life, sustainability, and resilience of the urban fabrics. This session focuses on the variety aspect of big data, and modeling as a way to capture common sense and enable semantic reasoning.

    Day 3 (Feb 5)

    9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    11:00 - 11:30 Coffee break
    Apache Spark has become a consolidated technology for large-scale processing in a fast and general way, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java, Scala, Python and R), extensive documentation and development tools. This course introduces Apache Spark, as well as some of its core libraries for data manipulation, machine learning, data streams and graph analytics.
    13:00 – 14:00 Lunch Break
    14:00 – 15:30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    16:00 – 16:15 Coffee break
    15:30 – 17:00 European project on Big Data

    Day 4 (Feb 6)

    9:30 – 13:00 Practical Introduction to Python Deep Learning  (Jordi Torres, Emerging Technologies for Artificial Intelligence Group Manager - Computer Sciences, BSC)
    Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to companies and research centers, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. The purpose of this short course is to gradually start the student off to the basics of Python Deep Learning, in a practical way through a guided, hands-on learning without becoming too technical, ensuring that the student learn enough of the basics to get literate in Deep Learning. Using the Keras API of TensorFlow library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation. The course content will be as follows:

    PART 1: INTRODUCTION
    1. What is Deep Learning?
    2. Work environment
    3. Python and its libraries

    PART 2: FUNDAMENTALS OF DEEP LEARNING
    4. Densely connected neural networks.
    5. Neural networks in Keras
    6. How a neural network is trained
    7. Parameters and hyperparameters in neural networks
    8. Convolutional neural networks.

    PART 3: DEEP LEARNING TECHNIQUES
    9. Stages of a Deep Learning project
    10. Data to train neural networks
    11. Data Augmentation and Transfer Learning
    12. Advanced neural network architectures

    PART 4: GENERATIVE DEEP LEARNING

    13. Recurrent neural networks
    14. Generative Adversarial Networks

    Important prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python prior to starting the course.

    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja, UPC – Barcelona Tech)
    Data contains information. We will try to contextualize the flow of apparently “new” concepts such as data mining, business intelligence, big data, data science and how they relate to the old school of exploratory statistics. We will also introduce an overview of the main steps of a data mining problem, and we will illustrate them through sound examples of application.
    16:00 - 16:30 Coffee break
    16:30 – 18:00 Data analytics in societal challenges modeling: smart mobility and other related fields (Dra. Mari Paz Linares i Jamie Arjona (UPC, inLab FIB)
    Internet of Things, Big Data, Smart cities or Industry 4.0 are concepts that have raised in the last years with promises of solving daily human issues. In this session we will present how a combination of Internet of Things and Big Data can attack certain challenges and alleviate them.

    Day 5 (Feb 7)

    9:30 – 13:00 Data Visualization Therory (Luz Calvo, User Experience And Interaction Designer, BSC and Juan Felipe Gomez Celis, FrontEnd Developer, BSC)
    Therory



    Basic concepts
    Human perception
    Design
    Colour
    Audience / Validation / Bad practices
    Visualisation design process


    11:00 - 11:30 Coffee break

    Tools for data visualization
    – Tableau
    – Data Wrapper
    – RawGraphs
    – Flourish
    – Carto

    Data visualisation with d3.js


    END of COURSE

     

     
    events.prace-ri.eu/event/910/
    Feb 3 9:30 to Feb 7 16:30
    Scope of the course

    The Python programming language has become more and more popular among researchers for its simplicity and the availability of specific programming libraries, and at the same time the correct exploitation of heterogeneous architectures presents challenges for the development of parallel applications. In order to bring these two topics together, this course is focused on the use of Python on CPU and GPU platforms for scientific computing in general.

     

    General description

    The basic concepts of good programming practices in Python and general parallel programming will be introduced, and then GPU computing will be explained combining the essential theory concepts with hands-on sessions. The proposed exercises will be tested in the supercomputing facilities provided by SURFsara using Python with different programming libraries:


    numba
    PyCUDA
    mpi4py


     

    IMPORTANT INFORMATION: WAITING LIST

    If the course gets fully booked, no more registrations are accepted through this website. However, you can be included in the waiting list: for that, please send an email to training@surfsara.nl and you'll be informed when a place becomes available.
    events.prace-ri.eu/event/946/
    Feb 3 9:00 to Feb 4 17:45
    BIOWEEK 2020

    The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:

    3.2.2020           Using modern HPC environment efficiently
    4.2-5.2.2020    Data analysis with R
    6.2-7.2.2020    Introduction to RNA-seq data analysis

    You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.

    Lecturers (CSC, Finland):  Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri

    Language:  English
    Price:           Free of charge

    Using modern HPC environment efficiently


    Description

    Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.


    Learning outcomes

    After the course, participants:


    will have basic skills in using object storage
    can use object storage services as part of their analysis workflows
    can run batch jobs using Slurm
    will have basic skills in using Singularity containers in HPC environment



    Prerequisites

    The participants are required to have basic skills/knowledge in Command line usage / Linux basics


    Program: see Timetable menu

    Data analysis with R


    Description

    R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.

    The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)


    Learning outcomes

    After attending this course, participants will be able to:


    Navigate RStudio
    Understand R syntax and how to write R code
    Import and export data using R
    Use tidyverse for data wrangling
    Use ggplot2 for creating high-quality plots
    Employ t-tests, linear regression and Chi-squared tests in R



    Prerequisites


    No prior experience of programming or using R is expected
    No data analysis or statistical experience is required (but is likely to be beneficial)



    Program: see Timetable menu

    Introduction to RNA-seq data analysis


    Description

    After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.


    Learning outcomes

    After the course, participants:


    can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps, 
    can run some RNA-seq analysis tools on command line
    import and start analysing their RNA-seq data in R



    Prerequisites

    The participants are required to have basic skills/knowledge in:


    Command line usage / Linux basics
    Current CSC services
    R and Rstudio


    Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses. 


    Program: see Timetable menu
    events.prace-ri.eu/event/965/
    Feb 3 8:00 to Feb 7 15:00
    Registration is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course Convener:  Maria-Ribera Sancho

    Objectives: The course brings together key information technologies used in manipulating, storing, and analysing data including:


    the basic tools for statistical analysis
    techniques for parallel processing
    tools for access to unstructured data
    storage solutions


    Learning outcomes: Students will be introduced to systems that can accept, store, and analyse large volumes of unstructured data. The learned skills can be used in data intensive application areas.

    Level: For trainees with some theoretical and practical knowledge

    Agenda:

    Day 1 (Feb 3)

    9:30 – 13:00 Introduction to Big Data (David Carrera, Data Centric Computing Group Manager, BSC)

    The goal of this session is to introduce the students in the technologies associated with Big Data: data challenges, cloud computing, processing, and internet of things. An overview of the technologies will be provided, both from a technical and from a business model point of view.


    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 – 16:00 Practical Data Analytics for Solving Real World Problems (José Carlos Carrasco Jiménez, Researcher, BSC)
    Data analytics has changed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will propose practical tips gained through our experience at BSC in big data analytics projects. We will also discover how to overcome some of the most challenging tasks in practical data analytics.
    16:00 – 16:30 Coffee break
    16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez, Researcher, BSC)
    This session will focus on several key methods and algorithms (both serial and parallel) that enable to discover global properties on data while dealing with Big Data:
    Network Science
    Multi Constrained and Multi-Objective Optimization
    Examples using the above approaches and some hands-on exercise

     

    Day 2 (Feb 4)

    9:30 – 13:00 Big Data Management (Albert Abelló, UPC, inLab FIB)
    Big Data has many definitions and facets, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically, we'll focus on the Apache Hadoop ecosystem and its two basic components, namely HBase and MapReduce engine.
    11:00 - 11:30 Coffee break
    Hands-on exercise
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 NoSQL databases (Oscar Romero, Dept. of Service and Information System Engineering, UPC-BarcelonaTech)
    The relational model has dominated data storage systems since the mid 1970s. However, the changing storage needs over the past decade have given rise to new models for storing data, collectively known as NoSQL. In this presentation, we will focus on two of the most common types of NoSQL databases: document-oriented databases and graph databases and explain the use cases suitable for each of them.
    16:00 - 16:30 Coffee break
    16:30 - 18:00 Multidisciplinary research and data analytics: Smart Cities (Maria Cristina Marinescu, Computer Applications in Science&Engineering, BSC) 

    A huge quantity of data is produced in cities from many types of sources: IoT, social network, other text sources, images, etc. Data integration is the first and more difficult step to ensure data quality and be able to then analyze these data and get insight hat may help improve quality of life, sustainability, and resilience of the urban fabrics. This session focuses on the variety aspect of big data, and modeling as a way to capture common sense and enable semantic reasoning.

    Day 3 (Feb 5)

    9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    11:00 - 11:30 Coffee break
    Apache Spark has become a consolidated technology for large-scale processing in a fast and general way, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java, Scala, Python and R), extensive documentation and development tools. This course introduces Apache Spark, as well as some of its core libraries for data manipulation, machine learning, data streams and graph analytics.
    13:00 – 14:00 Lunch Break
    14:00 – 15:30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    16:00 – 16:15 Coffee break
    15:30 – 17:00 European project on Big Data

    Day 4 (Feb 6)

    9:30 – 13:00 Practical Introduction to Python Deep Learning  (Jordi Torres, Emerging Technologies for Artificial Intelligence Group Manager - Computer Sciences, BSC)
    Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to companies and research centers, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. The purpose of this short course is to gradually start the student off to the basics of Python Deep Learning, in a practical way through a guided, hands-on learning without becoming too technical, ensuring that the student learn enough of the basics to get literate in Deep Learning. Using the Keras API of TensorFlow library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation. The course content will be as follows:

    PART 1: INTRODUCTION
    1. What is Deep Learning?
    2. Work environment
    3. Python and its libraries

    PART 2: FUNDAMENTALS OF DEEP LEARNING
    4. Densely connected neural networks.
    5. Neural networks in Keras
    6. How a neural network is trained
    7. Parameters and hyperparameters in neural networks
    8. Convolutional neural networks.

    PART 3: DEEP LEARNING TECHNIQUES
    9. Stages of a Deep Learning project
    10. Data to train neural networks
    11. Data Augmentation and Transfer Learning
    12. Advanced neural network architectures

    PART 4: GENERATIVE DEEP LEARNING

    13. Recurrent neural networks
    14. Generative Adversarial Networks

    Important prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python prior to starting the course.

    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja, UPC – Barcelona Tech)
    Data contains information. We will try to contextualize the flow of apparently “new” concepts such as data mining, business intelligence, big data, data science and how they relate to the old school of exploratory statistics. We will also introduce an overview of the main steps of a data mining problem, and we will illustrate them through sound examples of application.
    16:00 - 16:30 Coffee break
    16:30 – 18:00 Data analytics in societal challenges modeling: smart mobility and other related fields (Dra. Mari Paz Linares i Jamie Arjona (UPC, inLab FIB)
    Internet of Things, Big Data, Smart cities or Industry 4.0 are concepts that have raised in the last years with promises of solving daily human issues. In this session we will present how a combination of Internet of Things and Big Data can attack certain challenges and alleviate them.

    Day 5 (Feb 7)

    9:30 – 13:00 Data Visualization Therory (Luz Calvo, User Experience And Interaction Designer, BSC and Juan Felipe Gomez Celis, FrontEnd Developer, BSC)
    Therory



    Basic concepts
    Human perception
    Design
    Colour
    Audience / Validation / Bad practices
    Visualisation design process


    11:00 - 11:30 Coffee break

    Tools for data visualization
    – Tableau
    – Data Wrapper
    – RawGraphs
    – Flourish
    – Carto

    Data visualisation with d3.js


    END of COURSE

     

     
    events.prace-ri.eu/event/910/
    Feb 3 9:30 to Feb 7 16:30
    Annotation

    The CUDA computing platform enables the acceleration of CPU-only applications to run on the world’s fastest massively parallel GPUs. You experience C/C++ application acceleration by:


    Accelerating CPU-only applications to run their latent parallelism on GPUs
    Utilizing essential CUDA memory management techniques to optimize accelerated applications
    Exposing accelerated application potential for concurrency and exploiting it with CUDA streams
    Leveraging command line and visual profiling to guide and check your work.


    This training is a part of NVIDIA AI & HPC ACADEMY 2020.

    The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

    The workshop is co-organized by LRZ, IT4Innovations and NVIDIA Deep Learning Institute (DLI) for the Partnership for Advanced Computing in Europe (PRACE). Both IT4Innovations and LRZ, as part of GCS, are PRACE Training Centres, serve as European hubs and key drivers of advanced high-quality training for researchers working in the computational sciences.

    NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.

    All instructors are NVIDIA certified University Ambassadors.

    Level

    Beginner

    Language

    English

    Purpose of the course

    Upon completion, you will be able to accelerate and optimize existing C/C++ CPU-only applications using the most essential CUDA tools and techniques. You will understand an iterative style of CUDA development that will allow you to ship accelerated applications fast.

    About the tutor

    Dr. Momme Allalen received his Ph.D. in theoretical Physics from the University of Osnabrück in 2006. He worked in the field of molecular magnetics through modelling techniques such as the exact numerical diagonalisation of the Heisenberg model. He joined the Leibniz Computing Centre (LRZ) in 2007 working in the High Performance Computing group. His tasks include user support, optimisation and parallelisation of scientific application codes, and benchmarking for characterising and evaluating the performance of high-end supercomputers. Momme is an NVIDIA DLI certified instructor for Fundamentals of Accelerated Computing with CUDA C/C++. His research interests are various aspects of parallel computing and new programming languages and paradigms on novel HPC architectures.

    NVIDIA Deep Learning Institute

    The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

    Acknowledgement

    This event was partially supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project "e-Infrastruktura CZ – LM2018140“ and partially by the PRACE-6IP project - the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823767. We would like to also thank Bayncore Labs for their contributions to this event.

      
    events.prace-ri.eu/event/970/
    Feb 5 8:30 17:00
    BIOWEEK 2020

    The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:

    3.2.2020           Using modern HPC environment efficiently
    4.2-5.2.2020    Data analysis with R
    6.2-7.2.2020    Introduction to RNA-seq data analysis

    You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.

    Lecturers (CSC, Finland):  Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri

    Language:  English
    Price:           Free of charge

    Using modern HPC environment efficiently


    Description

    Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.


    Learning outcomes

    After the course, participants:


    will have basic skills in using object storage
    can use object storage services as part of their analysis workflows
    can run batch jobs using Slurm
    will have basic skills in using Singularity containers in HPC environment



    Prerequisites

    The participants are required to have basic skills/knowledge in Command line usage / Linux basics


    Program: see Timetable menu

    Data analysis with R


    Description

    R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.

    The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)


    Learning outcomes

    After attending this course, participants will be able to:


    Navigate RStudio
    Understand R syntax and how to write R code
    Import and export data using R
    Use tidyverse for data wrangling
    Use ggplot2 for creating high-quality plots
    Employ t-tests, linear regression and Chi-squared tests in R



    Prerequisites


    No prior experience of programming or using R is expected
    No data analysis or statistical experience is required (but is likely to be beneficial)



    Program: see Timetable menu

    Introduction to RNA-seq data analysis


    Description

    After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.


    Learning outcomes

    After the course, participants:


    can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps, 
    can run some RNA-seq analysis tools on command line
    import and start analysing their RNA-seq data in R



    Prerequisites

    The participants are required to have basic skills/knowledge in:


    Command line usage / Linux basics
    Current CSC services
    R and Rstudio


    Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses. 


    Program: see Timetable menu
    events.prace-ri.eu/event/965/
    Feb 3 8:00 to Feb 7 15:00
    Registration is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course Convener:  Maria-Ribera Sancho

    Objectives: The course brings together key information technologies used in manipulating, storing, and analysing data including:


    the basic tools for statistical analysis
    techniques for parallel processing
    tools for access to unstructured data
    storage solutions


    Learning outcomes: Students will be introduced to systems that can accept, store, and analyse large volumes of unstructured data. The learned skills can be used in data intensive application areas.

    Level: For trainees with some theoretical and practical knowledge

    Agenda:

    Day 1 (Feb 3)

    9:30 – 13:00 Introduction to Big Data (David Carrera, Data Centric Computing Group Manager, BSC)

    The goal of this session is to introduce the students in the technologies associated with Big Data: data challenges, cloud computing, processing, and internet of things. An overview of the technologies will be provided, both from a technical and from a business model point of view.


    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 – 16:00 Practical Data Analytics for Solving Real World Problems (José Carlos Carrasco Jiménez, Researcher, BSC)
    Data analytics has changed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will propose practical tips gained through our experience at BSC in big data analytics projects. We will also discover how to overcome some of the most challenging tasks in practical data analytics.
    16:00 – 16:30 Coffee break
    16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez, Researcher, BSC)
    This session will focus on several key methods and algorithms (both serial and parallel) that enable to discover global properties on data while dealing with Big Data:
    Network Science
    Multi Constrained and Multi-Objective Optimization
    Examples using the above approaches and some hands-on exercise

     

    Day 2 (Feb 4)

    9:30 – 13:00 Big Data Management (Albert Abelló, UPC, inLab FIB)
    Big Data has many definitions and facets, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically, we'll focus on the Apache Hadoop ecosystem and its two basic components, namely HBase and MapReduce engine.
    11:00 - 11:30 Coffee break
    Hands-on exercise
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 NoSQL databases (Oscar Romero, Dept. of Service and Information System Engineering, UPC-BarcelonaTech)
    The relational model has dominated data storage systems since the mid 1970s. However, the changing storage needs over the past decade have given rise to new models for storing data, collectively known as NoSQL. In this presentation, we will focus on two of the most common types of NoSQL databases: document-oriented databases and graph databases and explain the use cases suitable for each of them.
    16:00 - 16:30 Coffee break
    16:30 - 18:00 Multidisciplinary research and data analytics: Smart Cities (Maria Cristina Marinescu, Computer Applications in Science&Engineering, BSC) 

    A huge quantity of data is produced in cities from many types of sources: IoT, social network, other text sources, images, etc. Data integration is the first and more difficult step to ensure data quality and be able to then analyze these data and get insight hat may help improve quality of life, sustainability, and resilience of the urban fabrics. This session focuses on the variety aspect of big data, and modeling as a way to capture common sense and enable semantic reasoning.

    Day 3 (Feb 5)

    9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    11:00 - 11:30 Coffee break
    Apache Spark has become a consolidated technology for large-scale processing in a fast and general way, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java, Scala, Python and R), extensive documentation and development tools. This course introduces Apache Spark, as well as some of its core libraries for data manipulation, machine learning, data streams and graph analytics.
    13:00 – 14:00 Lunch Break
    14:00 – 15:30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    16:00 – 16:15 Coffee break
    15:30 – 17:00 European project on Big Data

    Day 4 (Feb 6)

    9:30 – 13:00 Practical Introduction to Python Deep Learning  (Jordi Torres, Emerging Technologies for Artificial Intelligence Group Manager - Computer Sciences, BSC)
    Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to companies and research centers, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. The purpose of this short course is to gradually start the student off to the basics of Python Deep Learning, in a practical way through a guided, hands-on learning without becoming too technical, ensuring that the student learn enough of the basics to get literate in Deep Learning. Using the Keras API of TensorFlow library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation. The course content will be as follows:

    PART 1: INTRODUCTION
    1. What is Deep Learning?
    2. Work environment
    3. Python and its libraries

    PART 2: FUNDAMENTALS OF DEEP LEARNING
    4. Densely connected neural networks.
    5. Neural networks in Keras
    6. How a neural network is trained
    7. Parameters and hyperparameters in neural networks
    8. Convolutional neural networks.

    PART 3: DEEP LEARNING TECHNIQUES
    9. Stages of a Deep Learning project
    10. Data to train neural networks
    11. Data Augmentation and Transfer Learning
    12. Advanced neural network architectures

    PART 4: GENERATIVE DEEP LEARNING

    13. Recurrent neural networks
    14. Generative Adversarial Networks

    Important prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python prior to starting the course.

    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja, UPC – Barcelona Tech)
    Data contains information. We will try to contextualize the flow of apparently “new” concepts such as data mining, business intelligence, big data, data science and how they relate to the old school of exploratory statistics. We will also introduce an overview of the main steps of a data mining problem, and we will illustrate them through sound examples of application.
    16:00 - 16:30 Coffee break
    16:30 – 18:00 Data analytics in societal challenges modeling: smart mobility and other related fields (Dra. Mari Paz Linares i Jamie Arjona (UPC, inLab FIB)
    Internet of Things, Big Data, Smart cities or Industry 4.0 are concepts that have raised in the last years with promises of solving daily human issues. In this session we will present how a combination of Internet of Things and Big Data can attack certain challenges and alleviate them.

    Day 5 (Feb 7)

    9:30 – 13:00 Data Visualization Therory (Luz Calvo, User Experience And Interaction Designer, BSC and Juan Felipe Gomez Celis, FrontEnd Developer, BSC)
    Therory



    Basic concepts
    Human perception
    Design
    Colour
    Audience / Validation / Bad practices
    Visualisation design process


    11:00 - 11:30 Coffee break

    Tools for data visualization
    – Tableau
    – Data Wrapper
    – RawGraphs
    – Flourish
    – Carto

    Data visualisation with d3.js


    END of COURSE

     

     
    events.prace-ri.eu/event/910/
    Feb 3 9:30 to Feb 7 16:30
    Annotation

    You learn the basics of OpenACC, a high-level programming language for programming on GPUs. Discover how to accelerate the performance of your applications beyond the limits of CPU-only programming with simple pragmas. You will learn:


    How to profile and optimize your CPU-only applications to identify hot spots for acceleration
    How to use OpenACC directives to GPU accelerate your codebase
    How to optimize data movement between the CPU and GPU accelerator


    The lectures are interleaved with many hands-on sessions using Jupyter Notebooks. The exercises will be done on a fully configured GPU-accelerated workstation in the cloud.

    This training is a part of NVIDIA AI & HPC ACADEMY 2020.

    The workshop is co-organized by LRZ, IT4Innovations and NVIDIA Deep Learning Institute (DLI) for the Partnership for Advanced Computing in Europe (PRACE). Both IT4Innovations and LRZ, as part of GCS, are PRACE Training Centres, serve as European hubs and key drivers of advanced high-quality training for researchers working in the computational sciences.

    NVIDIA DLI offers hands-on training for developers, data scientists, and researchers looking to solve challenging problems with deep learning.

    All instructors are NVIDIA certified University Ambassadors.

    Level

    Beginner

    Language

    English

    Purpose of the course

    Upon completion, you'll be ready to use OpenACC to GPU accelerate CPU-only applications.

    About the tutor

    Dr. Volker Weinberg studied physics at the Ludwig Maximilian University of Munich and later worked at the research centre DESY. He received his PhD from the Free University of Berlin for his studies in the field of lattice QCD. Since 2008 he is working in the HPC group at the Leibniz Supercomputing Centre and is education and training coordinator at LRZ. Volker is an NVIDIA Deep Learning Institute (DLI) certified OpenACC instructor participating in the University Ambassador program. Within PRACE, the Partnership for Advanced Computing in Europe, he is leading the workpackage WP5 "HPC Commissioning and Prototyping".

    NVIDIA Deep Learning Institute

    The NVIDIA Deep Learning Institute delivers hands-on training for developers, data scientists, and engineers. The program is designed to help you get started with training, optimizing, and deploying neural networks to solve real-world problems across diverse industries such as self-driving cars, healthcare, online services, and robotics.

    Acknowledgement

    This event was partially supported by The Ministry of Education, Youth and Sports from the Large Infrastructures for Research, Experimental Development and Innovations project "e-Infrastruktura CZ – LM2018140“ and partially by the PRACE-6IP project - the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 823767. We would like to also thank Bayncore Labs for their contributions to this event.

     
    events.prace-ri.eu/event/971/
    Feb 6 8:30 17:00
    BIOWEEK 2020

    The Bioweek 2020 is a five-day comprehensive training in bioinformatics consisting of three separate courses:

    3.2.2020           Using modern HPC environment efficiently
    4.2-5.2.2020    Data analysis with R
    6.2-7.2.2020    Introduction to RNA-seq data analysis

    You can participate in ALL or only SELECTED Bioweek's courses. We have planned the Bioweek 2020 so that we are building on top of the previous knowledge. For example, the last course (basics of RNA-seq data analysis) has command line, R/RStudio and CSC services usage as prerequisites.

    Lecturers (CSC, Finland):  Ari-Matti Saren, Kimmo Mattila, Jesse Harrison, Anni Pyysing, Maria Lehtivaara, Laxmana Yetukuri

    Language:  English
    Price:           Free of charge

    Using modern HPC environment efficiently


    Description

    Using a modern HPC environment efficiently requires not only a good understanding of your scientific problem, but also some familiarity with the technical aspects of the system. On this course we will take a look at some of the most essential ones: Using modern storage services, running jobs through a batch job system and some solutions for getting your analysis environment running on a HPC platform.


    Learning outcomes

    After the course, participants:


    will have basic skills in using object storage
    can use object storage services as part of their analysis workflows
    can run batch jobs using Slurm
    will have basic skills in using Singularity containers in HPC environment



    Prerequisites

    The participants are required to have basic skills/knowledge in Command line usage / Linux basics


    Program: see Timetable menu

    Data analysis with R


    Description

    R is a language that has become one of the most popular tools for data manipulation, visualization and statistics. While there are many R courses, learning these skills can involve a steep learning curve, especially for people with no experience in programming or data analysis. This two-day course aims to help with this initial difficulty by equipping learners with essential skills in data wrangling, plotting and running commonly used statistical tests in R.

    The course topics include data importing and exporting, handling complex data sets and creating publication-ready plots with R. We also cover statistical theory and tests including t-tests, linear regression and the Chi-squared test. The course materials are available on GitHub (feel free to have a look at them before the course starts!)


    Learning outcomes

    After attending this course, participants will be able to:


    Navigate RStudio
    Understand R syntax and how to write R code
    Import and export data using R
    Use tidyverse for data wrangling
    Use ggplot2 for creating high-quality plots
    Employ t-tests, linear regression and Chi-squared tests in R



    Prerequisites


    No prior experience of programming or using R is expected
    No data analysis or statistical experience is required (but is likely to be beneficial)



    Program: see Timetable menu

    Introduction to RNA-seq data analysis


    Description

    After learning the basics of command line usage, HPC and R/Rstudio, it's time to put your new skills in use and use them in RNA-seq data analysis! This course is aimed at bioscientists who are planning on analysing their RNA-seq data. During the course, we will learn the basic steps in RNA-seq analysis and how to use some of the most common analysis tools on command line and in R.


    Learning outcomes

    After the course, participants:


    can name and discuss the different stages of a basic RNA-seq analysis and common tools used in these steps, 
    can run some RNA-seq analysis tools on command line
    import and start analysing their RNA-seq data in R



    Prerequisites

    The participants are required to have basic skills/knowledge in:


    Command line usage / Linux basics
    Current CSC services
    R and Rstudio


    Bioweek is structured so that this course builds on top of the earlier courses. This means that the prerequisite skills can be learned on the two courses organised earlier on the same week. If you have no prior knowledge or suspect your knowledge on these subjects, please consider participating in all the three courses. 


    Program: see Timetable menu
    events.prace-ri.eu/event/965/
    Feb 3 8:00 to Feb 7 15:00
    Registration is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course Convener:  Maria-Ribera Sancho

    Objectives: The course brings together key information technologies used in manipulating, storing, and analysing data including:


    the basic tools for statistical analysis
    techniques for parallel processing
    tools for access to unstructured data
    storage solutions


    Learning outcomes: Students will be introduced to systems that can accept, store, and analyse large volumes of unstructured data. The learned skills can be used in data intensive application areas.

    Level: For trainees with some theoretical and practical knowledge

    Agenda:

    Day 1 (Feb 3)

    9:30 – 13:00 Introduction to Big Data (David Carrera, Data Centric Computing Group Manager, BSC)

    The goal of this session is to introduce the students in the technologies associated with Big Data: data challenges, cloud computing, processing, and internet of things. An overview of the technologies will be provided, both from a technical and from a business model point of view.


    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 – 16:00 Practical Data Analytics for Solving Real World Problems (José Carlos Carrasco Jiménez, Researcher, BSC)
    Data analytics has changed the way we make decisions. We see the benefits and the advances in many fields that go from financial to medical and industrial applications due to the integration of advanced data analytics. In this course we will propose practical tips gained through our experience at BSC in big data analytics projects. We will also discover how to overcome some of the most challenging tasks in practical data analytics.
    16:00 – 16:30 Coffee break
    16:30 – 18:00 Hands-on (José Carlos Carrasco Jiménez, Researcher, BSC)
    This session will focus on several key methods and algorithms (both serial and parallel) that enable to discover global properties on data while dealing with Big Data:
    Network Science
    Multi Constrained and Multi-Objective Optimization
    Examples using the above approaches and some hands-on exercise

     

    Day 2 (Feb 4)

    9:30 – 13:00 Big Data Management (Albert Abelló, UPC, inLab FIB)
    Big Data has many definitions and facets, we'll pay attention to the problems we have to face to store it and how we can process it. More specifically, we'll focus on the Apache Hadoop ecosystem and its two basic components, namely HBase and MapReduce engine.
    11:00 - 11:30 Coffee break
    Hands-on exercise
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 NoSQL databases (Oscar Romero, Dept. of Service and Information System Engineering, UPC-BarcelonaTech)
    The relational model has dominated data storage systems since the mid 1970s. However, the changing storage needs over the past decade have given rise to new models for storing data, collectively known as NoSQL. In this presentation, we will focus on two of the most common types of NoSQL databases: document-oriented databases and graph databases and explain the use cases suitable for each of them.
    16:00 - 16:30 Coffee break
    16:30 - 18:00 Multidisciplinary research and data analytics: Smart Cities (Maria Cristina Marinescu, Computer Applications in Science&Engineering, BSC) 

    A huge quantity of data is produced in cities from many types of sources: IoT, social network, other text sources, images, etc. Data integration is the first and more difficult step to ensure data quality and be able to then analyze these data and get insight hat may help improve quality of life, sustainability, and resilience of the urban fabrics. This session focuses on the variety aspect of big data, and modeling as a way to capture common sense and enable semantic reasoning.

    Day 3 (Feb 5)

    9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    11:00 - 11:30 Coffee break
    Apache Spark has become a consolidated technology for large-scale processing in a fast and general way, with “programmer-friendly” interfaces and official bindings for many of the most used languages (Java, Scala, Python and R), extensive documentation and development tools. This course introduces Apache Spark, as well as some of its core libraries for data manipulation, machine learning, data streams and graph analytics.
    13:00 – 14:00 Lunch Break
    14:00 – 15:30 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral, Computer Sciences - Data Centric Computing, BSC)
    16:00 – 16:15 Coffee break
    15:30 – 17:00 European project on Big Data

    Day 4 (Feb 6)

    9:30 – 13:00 Practical Introduction to Python Deep Learning  (Jordi Torres, Emerging Technologies for Artificial Intelligence Group Manager - Computer Sciences, BSC)
    Artificial Intelligence is changing our lives, and solutions based on Deep Learning are leading this transformation. Deep Learning is now of major interest to companies and research centers, since it can be applied to many areas of activity. But getting started in this technology is not an easy task. The purpose of this short course is to gradually start the student off to the basics of Python Deep Learning, in a practical way through a guided, hands-on learning without becoming too technical, ensuring that the student learn enough of the basics to get literate in Deep Learning. Using the Keras API of TensorFlow library allows the development of Deep Learning models and abstracts much of the mathematical complexity involved in its implementation. The course content will be as follows:

    PART 1: INTRODUCTION
    1. What is Deep Learning?
    2. Work environment
    3. Python and its libraries

    PART 2: FUNDAMENTALS OF DEEP LEARNING
    4. Densely connected neural networks.
    5. Neural networks in Keras
    6. How a neural network is trained
    7. Parameters and hyperparameters in neural networks
    8. Convolutional neural networks.

    PART 3: DEEP LEARNING TECHNIQUES
    9. Stages of a Deep Learning project
    10. Data to train neural networks
    11. Data Augmentation and Transfer Learning
    12. Advanced neural network architectures

    PART 4: GENERATIVE DEEP LEARNING

    13. Recurrent neural networks
    14. Generative Adversarial Networks

    Important prerequisites to enroll in this course: It is assumed that the student has a basic knowledge of Python prior to starting the course.

    11:00 - 11:30 Coffee break
    13:00 – 14:00 Lunch Break
    14:00 - 16:00 From Data Mining to Data Science (Tomàs Aluja, UPC – Barcelona Tech)
    Data contains information. We will try to contextualize the flow of apparently “new” concepts such as data mining, business intelligence, big data, data science and how they relate to the old school of exploratory statistics. We will also introduce an overview of the main steps of a data mining problem, and we will illustrate them through sound examples of application.
    16:00 - 16:30 Coffee break
    16:30 – 18:00 Data analytics in societal challenges modeling: smart mobility and other related fields (Dra. Mari Paz Linares i Jamie Arjona (UPC, inLab FIB)
    Internet of Things, Big Data, Smart cities or Industry 4.0 are concepts that have raised in the last years with promises of solving daily human issues. In this session we will present how a combination of Internet of Things and Big Data can attack certain challenges and alleviate them.

    Day 5 (Feb 7)

    9:30 – 13:00 Data Visualization Therory (Luz Calvo, User Experience And Interaction Designer, BSC and Juan Felipe Gomez Celis, FrontEnd Developer, BSC)
    Therory



    Basic concepts
    Human perception
    Design
    Colour
    Audience / Validation / Bad practices
    Visualisation design process


    11:00 - 11:30 Coffee break

    Tools for data visualization
    – Tableau
    – Data Wrapper
    – RawGraphs
    – Flourish
    – Carto

    Data visualisation with d3.js


    END of COURSE

     

     
    events.prace-ri.eu/event/910/
    Feb 3 9:30 to Feb 7 16:30
    8
     
    9
     
    Accelerator Programming
    GPU programming using CUDA

    10 - 11 February 2020

    Description

    The focus is to understand the basics of accelerator programming with the CUDA parallel computing platform model.

    CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach known as GPGPU. The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

    The course also contains performance and best practice considerations, e.g., gpu libraries, performance optimizations, tools for debugging and profiling.

    After the course the participants should have the basic skills needed for utilizing CUDA and OpenACC in new, or existing (own code) programs.

    Prerequisites

    The course addresses participants who are familiar with the C/C++ or Fortran programming languages and have working experience with the Linux operating system and the use of the command line. Experience with parallel programming or gpu programming (MPI,OpenMP and CUDA) is not required.

    Bring your own laptop in order to be able to participate in the training hands on. Hands on work will be done in pairs so if you don’t have a laptop you might work with a colleague.

    Course language is English.

    Registration

    The maximum number of participants is 30.

    Registrations will be evaluated on a first-come, first-served basis. GRNET is responsible for the selection of the participants on the basis of the training requirements and the technical skills of the candidates. GRNET will also seek to guarantee the maximum possible geographical coverage with the participation of candidates from many countries.

    Venue

    GRNET headquarters

    Address: 2nd  Floor, 7, Kifisias Av. GR 115 23 Athens

    Information on how to reach GRNET headquarters ia available on GRNET website: grnet.gr/en/contact-us/  

    Accommodation options near GRNET can be found at: grnet.gr/wp-content/up.....n.pdf

    ARIS - System Information

    ARIS is the name of the Greek supercomputer, deployed and operated by GRNET (Greek Research and Technology Network) in Athens. ARIS consists of 532 computational nodes seperated in four “islands” as listed here:



    426 thin nodes: Regular compute nodes without accelerator.


    44 gpu nodes: “2 x NVIDIA Tesla k40m” accelerated nodes.


    18 phi nodes: “2 x INTEL Xeon Phi 7120p” accelerated nodes.


    44 fat nodes: Fat compute nodes have larger number of cores and memory per core than a thin node.


    1 ml node: Machine Learning node consisting of 1 server, containing 2 Intel E5-2698v4 processors, 512 GB of central memory and 8 NVIDIA V100 GPU card.



    All the nodes are connected via Infiniband network and share 2PB GPFS storage.The infrastructure also has an IBM TS3500 library of maximum storage capacity of about 6 PB. Access to the system is provided by two login nodes.

    About Tutors

    Dr. Dellis (Male) holds a B.Sc. in Chemistry (1990) and PhD in Computational Chemistry (1995) from the National and Kapodistrian University of Athens, Greece. He has extensive HPC and grid computing experience. He was using HPC systems in computational chemistry research projects on fz-juelich machines (2003-2005). He received an HPC-Europa grant on BSC (2009). In EGEE/EGI projects he acted as application support and VO software manager for SEE VO, grid sites administrator (HG-02, GR-06), NGI_GRNET support staff (2008-2014). In PRACE 1IP/2IP/3IP/4IP/5IP he was involved in benchmarking tasks either as group member or as BCO (2010-2017). Currently he holds the position of “Senior HPC Applications Support Engineer” at GRNET S.A. where he is responsible for activities related to user consultations, porting, optimization and running HPC applications at national and international resources.

    Nikolaos Nikoloutsakos holds a diploma of Engineering in Computer Engineering and Informatics (2014) from the University of Patras, Greece. From 2015 he works as software engineer at GRNET S.A. where he is part of the user application support team for the ARIS HPC system. He has been involved in major national and European projects, such as PRACE and EUDAT. His main research interests include parallel programming models, co-processor programming using GPUs and Intel Xeon Phis.

    Dr. Ioannis E. Venetis received his PhD in 2006 from the Computer Engineering and Informatics Department at the University of Patras, Greece. Currently he teaches "Parallel Processing" and "Software and Programming for High Performance Systems" at the same Department. He has participated in numerous research projects in the area of Parallel Computing. His main research interests include parallel programming models, run-time systems for supporting such models, co-processor programming (especially using GPUs and the Intel Xeon Phi) and parallelization of computationally demanding applications.

    About GRNET

    GRNET – National Infrastructures for Research and Technology, is the national network, cloud computing and IT e-Infrastructure and services provider. It supports hundreds of thousands of users in the key areas of Research, Education, Health and Culture.

    GRNET provides an integrated environment of cutting-edge technologies integrating a country-wide dark fiber network, data centers, a high performance computing system and Internet, cloud computing, high-performance computing, authentication and authorization services, security services, as well as audio, voice and video services.

    GRNET scientific and advisory duties address the areas of information technology, digital technologies, communications, e-government, new technologies and their applications, research and development, education, as well as the promotion of Digital Transformation.

    Through international partnerships and the coordination of EC co-funded projects, it creates opportunities for know-how development and exploitation, and contributes, in a decisive manner, to the development of Research and Science in Greece and abroad.

    National Infrastructures for Research and Technology – Networking Research and Education

    www.grnet.gr, hpc.grnet.gr

     
    events.prace-ri.eu/event/978/
    Feb 10 9:00 to Feb 11 16:00
    Accelerator Programming
    GPU programming using CUDA

    10 - 11 February 2020

    Description

    The focus is to understand the basics of accelerator programming with the CUDA parallel computing platform model.

    CUDA is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach known as GPGPU. The CUDA platform is a software layer that gives direct access to the GPU’s virtual instruction set and parallel computational elements, for the execution of compute kernels.

    The course also contains performance and best practice considerations, e.g., gpu libraries, performance optimizations, tools for debugging and profiling.

    After the course the participants should have the basic skills needed for utilizing CUDA and OpenACC in new, or existing (own code) programs.

    Prerequisites

    The course addresses participants who are familiar with the C/C++ or Fortran programming languages and have working experience with the Linux operating system and the use of the command line. Experience with parallel programming or gpu programming (MPI,OpenMP and CUDA) is not required.

    Bring your own laptop in order to be able to participate in the training hands on. Hands on work will be done in pairs so if you don’t have a laptop you might work with a colleague.

    Course language is English.

    Registration

    The maximum number of participants is 30.

    Registrations will be evaluated on a first-come, first-served basis. GRNET is responsible for the selection of the participants on the basis of the training requirements and the technical skills of the candidates. GRNET will also seek to guarantee the maximum possible geographical coverage with the participation of candidates from many countries.

    Venue

    GRNET headquarters

    Address: 2nd  Floor, 7, Kifisias Av. GR 115 23 Athens

    Information on how to reach GRNET headquarters ia available on GRNET website: grnet.gr/en/contact-us/  

    Accommodation options near GRNET can be found at: grnet.gr/wp-content/up.....n.pdf

    ARIS - System Information

    ARIS is the name of the Greek supercomputer, deployed and operated by GRNET (Greek Research and Technology Network) in Athens. ARIS consists of 532 computational nodes seperated in four “islands” as listed here:



    426 thin nodes: Regular compute nodes without accelerator.


    44 gpu nodes: “2 x NVIDIA Tesla k40m” accelerated nodes.


    18 phi nodes: “2 x INTEL Xeon Phi 7120p” accelerated nodes.


    44 fat nodes: Fat compute nodes have larger number of cores and memory per core than a thin node.


    1 ml node: Machine Learning node consisting of 1 server, containing 2 Intel E5-2698v4 processors, 512 GB of central memory and 8 NVIDIA V100 GPU card.



    All the nodes are connected via Infiniband network and share 2PB GPFS storage.The infrastructure also has an IBM TS3500 library of maximum storage capacity of about 6 PB. Access to the system is provided by two login nodes.

    About Tutors

    Dr. Dellis (Male) holds a B.Sc. in Chemistry (1990) and PhD in Computational Chemistry (1995) from the National and Kapodistrian University of Athens, Greece. He has extensive HPC and grid computing experience. He was using HPC systems in computational chemistry research projects on fz-juelich machines (2003-2005). He received an HPC-Europa grant on BSC (2009). In EGEE/EGI projects he acted as application support and VO software manager for SEE VO, grid sites administrator (HG-02, GR-06), NGI_GRNET support staff (2008-2014). In PRACE 1IP/2IP/3IP/4IP/5IP he was involved in benchmarking tasks either as group member or as BCO (2010-2017). Currently he holds the position of “Senior HPC Applications Support Engineer” at GRNET S.A. where he is responsible for activities related to user consultations, porting, optimization and running HPC applications at national and international resources.

    Nikolaos Nikoloutsakos holds a diploma of Engineering in Computer Engineering and Informatics (2014) from the University of Patras, Greece. From 2015 he works as software engineer at GRNET S.A. where he is part of the user application support team for the ARIS HPC system. He has been involved in major national and European projects, such as PRACE and EUDAT. His main research interests include parallel programming models, co-processor programming using GPUs and Intel Xeon Phis.

    Dr. Ioannis E. Venetis received his PhD in 2006 from the Computer Engineering and Informatics Department at the University of Patras, Greece. Currently he teaches "Parallel Processing" and "Software and Programming for High Performance Systems" at the same Department. He has participated in numerous research projects in the area of Parallel Computing. His main research interests include parallel programming models, run-time systems for supporting such models, co-processor programming (especially using GPUs and the Intel Xeon Phi) and parallelization of computationally demanding applications.

    About GRNET

    GRNET – National Infrastructures for Research and Technology, is the national network, cloud computing and IT e-Infrastructure and services provider. It supports hundreds of thousands of users in the key areas of Research, Education, Health and Culture.

    GRNET provides an integrated environment of cutting-edge technologies integrating a country-wide dark fiber network, data centers, a high performance computing system and Internet, cloud computing, high-performance computing, authentication and authorization services, security services, as well as audio, voice and video services.

    GRNET scientific and advisory duties address the areas of information technology, digital technologies, communications, e-government, new technologies and their applications, research and development, education, as well as the promotion of Digital Transformation.

    Through international partnerships and the coordination of EC co-funded projects, it creates opportunities for know-how development and exploitation, and contributes, in a decisive manner, to the development of Research and Science in Greece and abroad.

    National Infrastructures for Research and Technology – Networking Research and Education

    www.grnet.gr, hpc.grnet.gr

     
    events.prace-ri.eu/event/978/
    Feb 10 9:00 to Feb 11 16:00
    Important: For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    With the increasing prevalence of multicore processors, shared-memory programming models are essential. OpenMP is a popular, portable, widely supported, and easy-to-use shared-memory model.

    Since its advent in 1997, the OpenMP programming model has proved to be a key driver behind parallel programming for shared-memory architectures.  Its powerful and flexible programming model has allowed researchers from various domains to enable parallelism in their applications.  Over the more than two decades of its existence, OpenMP has tracked the evolution of hardware and the complexities of software to ensure that it stays as relevant to today’s high performance computing community as it was in 1997.

    This workshop will cover a wide range of  topics, reaching from the basics of OpenMP programming using the "OpenMP Common Core" to really advanced topics. During each day lectures will be mixed with hands-on sessions on the LRZ system IvyMUC.

    Preliminary Agenda





     


    Day 1


    Day 2


    Day 3




    09:00-10:30


    Introduction to the OpenMP common core


    Tasking


    Tools for Performance and Correctness




    10:30-10:45


    Coffee Break


    Coffee Break


    Coffee Break




    10:45-12:00


    Decomposing code into patterns for parallelization


    Tasking


    Offloading to Accelerators




    12:00-13:00


    Lunch Break


    Lunch Break


    Lunch Break




    13:00-14:45


    Beyond OpenMP common core with tasking and offloading


    Host Performance: NUMA


    Other Advanced Features of OpenMP 5.0




    14:45-15:00


    Coffee Break


    Coffee Break


    Coffee Break




    15:00-17:00


    Hands-on time with Parallelware Trainer


    Host Performance: SIMD


    Roadmap / Outlook
    (until 16:30)




     


    17:00-18:00 Guided SuperMUC-NG Tour


    19:00 Social Event (tbc.)


     





    Day 1

    The first day will cover basic parallel programming with OpenMP using the Parallelware Trainer Software by Appentra Solutions (www.appentra.com/produ.....iner/).

    We will present a unique, productivity-oriented approach by introducing its usage based on common motifs in scientific code, and how each one will be parallelized. This will enable attendees to focus on the parallelization of components and how components combine in real applications.

    Attendees will use active learning through a carefully selected set of exercises, building knowledge on parallelization of key motifs (e.g. matrix multiplication, map reduce) that are valid across multiple scientific codes in everything from CFD to Molecular Simulation.

    Appentra’s Parallelware tools are based on over 10 years of research by co-founder and CEO, Dr. Manuel Arenaz, who will be the lecturer of the first day. Parallelware  enables the identification of opportunities for parallelization and the provision of appropriate parallelization methods using state-of-the-art industrial standards. Parallelware Trainer was developed specifically to help improve the experience of HPC training, providing an interactive learning environment that uses examples that are the same or similar to real codes. Parallelware Trainer provides support for OpenMP (including multi-threading, offloading and tasking) and OpenACC (for offloading), providing users with the opportunity to use GPU services with either OpenMP or OpenACC.

    Topics covered on Day 1 include:


    The OpenMP Common Core
    Beyond the OpenMP Common Core
    Parallelization with multi-threading, offloading and tasking paradigms
    Using Parallelware Trainer: A walk-through with PI example
    Practicals: Examples codes PI, MANDELBROT, HEAT and LULESHmk
    Worksheet: Parallelizing PI and LULESHmk with OpenMP
     Decomposing code into patterns for parallelization


    Day 2 and 3

    Day 2 and 3 will cover advanced topics like:


    Mastering Tasking with OpenMP, Taskloops, Dependencies and Cancellation
    Host Performance: SIMD / Vectorization
    Host Performance: NUMA Aware Programming, Memory Access, Task Affinity, Memory Management
    Tool Support for Performance and Correctness, VI-HPS Tools
    Offloading to Accelerators
    Other Advanced Features of OpenMP 5.0
    Future Roadmap of OpenMP


    Developers usually find OpenMP easy to learn. However, they are often disappointed with the performance and scalability of the resulting code. This disappointment stems not from shortcomings of OpenMP but rather with the lack of depth with which it is employed. The lectures on Day 2 and Day 3 will address this critical need by exploring the implications of possible OpenMP parallelization strategies, both in terms of correctness and performance.

    We cover tasking with OpenMP and host performance, putting a focus on performance aspects, such as data and thread locality on NUMA architectures, false sharing, and exploitation of vector units. Also tools for performance and correctness will be presented.

    Current trends in hardware bring co-processors such as GPUs into the fold. A modern platform is often a heterogeneous system with CPU cores, GPU cores, and other specialized accelerators. OpenMP has responded by adding directives that map code and data onto a device, the target directives. We will also explore these directives as they apply to programming GPUs.

    OpenMP 5.0 features will be highlighted and the future roadmap of OpenMP will be presented.

    All topics are accompanied with extensive case studies and we discuss the corresponding language features in-depth.

    Topics might be still subject to change.

    For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    The course is organized as a PRACE training event by LRZ in collaboration with Appentra Solutions, Intel and RWTH Aachen.

    Lecturers

    Dr. Manuel Arenaz is CEO at Appentra Solutions and professor of computer science at the University of A Coruña (Spain). Holds a PhD on advanced compiler techniques for automatic parallelization of scientific codes. After 10+ years teaching parallel programming at undergraduate and PhD levels, he strongly believes that the next generation of STEM engineers needs to be educated in HPC technologies to address the digital revolution challenge. Recently, he co-founded Appentra Solutions to commercialize products and services that take advantage of Parallware, a new technology for semantic analysis of scientific HPC codes.

    Dr.  Michael Klemm holds an M.Sc.  and a Doctor of Engineering degree from the Friedrich-Alexander-University Erlangen-Nuremberg, Germany.  Michael Klemm is a Principal Engineer in the Compute Ecosystem Engineering organization of the Intel Architecture, Graphics, and Software group at Intel in Germany.  His areas of interest include compiler construction, design of programming languages, parallel programming, and performance analysis and tuning.  Michael Klemm joined the OpenMP organization in 2009 and was appointed CEO of the OpenMP ARB in 2016.

    Dr. Christian Terboven is a senior scientist and leads the HPC group at RWTH Aachen University. His research interests center around Parallel Programming and related Software Engineering aspects. Dr. Terboven has been involved in the Analysis, Tuning and Parallelization of several large-scale simulation codes for various architectures. He is responsible for several research projects in the area of programming models and approaches to improve the productivity and efficiency of modern HPC systems. He is further co-author of the new book “Using OpenMP – The Next Step“, www.openmp.org/tech/us.....step/
    events.prace-ri.eu/event/947/
    Feb 11 9:00 to Feb 13 16:30
    The registration to this course is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course convener: Mariano Vazquez

    Lecturers:  Mariano Vázquez (BSC), Okba Hamitou (Atos), Ricard Borrell (BSC), Diana Fernandez Velez (BSC), Carlos Teijeiro Barjas (SURFsara), Ben Czaja (UvA), Paul Melis (SURFsara), Cristobal Samaniego (BSC), David Oks (BSC), Alfonso Santiago(BSC), Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL).

    Objectives:The objetive of this course is to give a panorama on the use of HPC-based computational mechanics in Engineering and Environment through the projects BSC are carrying on. This panorama includes the basics of what is behind the main tools: computational mechanics and parallelization. The training is delivered in collaboration with the center of excellence CompBioMed.

    Learning outcomes: The course gives a wide perspective and the latest trends of how HPC helps in industrial, clinical and research applications allowing to achieve more realistic multiphysics simulations.  In addition, the student has the opportunity of running Jobs in Marenostrum supercomputer.

    Level: INTERMEDIATE: For trainees with some theoretical and practical knowledge

    Day 1 (Feb. 11)

    Session 1 / 9:00am – 1:00 pm
    9:00-9:15 Welcome (Mariano Vázquez, BSC)

    9:15-11:00 Compilation and Optimization in the HPC environment (O. Hamitou)

    11:00-11:20h Coffee Break

    11:20-13:00 Parallel algorithms for Computational Mechanics (Guillaume Houzeaux - Ricard Borrell, BSC)

    13:00-14:00 Lunch Break

     

    Session 2 / 2:00pm – 4:00 pm 

    14:00-16:00 Data Visulization for Researchers Crash Course (Diana Fernandez Velez, BSC)

    16:00-18:00 Visit to MareNostrum

     

    Day 2 (Feb. 12)

     

    9:00-10:50 Introduction to HPC in Computational Modelling (Carlos Teijeiro Barjas, SURFsara)

    10:50-11:10h Coffee Break

    11:10-13:00 Computational Hemodynamics on HPC (UvA) (Ben Czaja, UvA)

    13:00-14:00 Lunch Break

     
    Session 4 / 2:00pm – 6:00 pm (2 h lectures, 2 h practical)


    14:00-15:30 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

    15:30-17:00 Cardiac Modelling (J. Aguado-Sierra)

     

    Day 3 (Feb. 13)

     

    Session 5 / 9:00am – 1:00 pm (4 h lectures)

    9:00-11:00 Fluid-Structure Interaction methods for biomechanics (C. Samaniego, D. Oks, A. Santiago)

    11:00-11:20h Coffee Break

    11:20-13:00 hands-on onFluid-Structure Interaction methods for biomechanics(C. Samaniego, D. Oks)

     
    Session 6 / 2:00pm – 6:00 pm 


    14:00-15:00 Introduction to Computer-Aided Drug Design (CADD) and GPCR Modelling (Dr Alexander Heifetz, EVOTEC)

    15:00-16:00 Innovations in HPC-training for medical, science and engineering students (Andrea Townsend-Nicholson, UCL)

    16:00-16:15 Coffee Break

    16:15-18:00 Molecular Medicine: Hands On (Andrea Townsend-Nicholson, UCL)

    END of COURSE

     
    events.prace-ri.eu/event/912/
    Feb 11 9:00 to Feb 13 18:00
    Important: For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    With the increasing prevalence of multicore processors, shared-memory programming models are essential. OpenMP is a popular, portable, widely supported, and easy-to-use shared-memory model.

    Since its advent in 1997, the OpenMP programming model has proved to be a key driver behind parallel programming for shared-memory architectures.  Its powerful and flexible programming model has allowed researchers from various domains to enable parallelism in their applications.  Over the more than two decades of its existence, OpenMP has tracked the evolution of hardware and the complexities of software to ensure that it stays as relevant to today’s high performance computing community as it was in 1997.

    This workshop will cover a wide range of  topics, reaching from the basics of OpenMP programming using the "OpenMP Common Core" to really advanced topics. During each day lectures will be mixed with hands-on sessions on the LRZ system IvyMUC.

    Preliminary Agenda





     


    Day 1


    Day 2


    Day 3




    09:00-10:30


    Introduction to the OpenMP common core


    Tasking


    Tools for Performance and Correctness




    10:30-10:45


    Coffee Break


    Coffee Break


    Coffee Break




    10:45-12:00


    Decomposing code into patterns for parallelization


    Tasking


    Offloading to Accelerators




    12:00-13:00


    Lunch Break


    Lunch Break


    Lunch Break




    13:00-14:45


    Beyond OpenMP common core with tasking and offloading


    Host Performance: NUMA


    Other Advanced Features of OpenMP 5.0




    14:45-15:00


    Coffee Break


    Coffee Break


    Coffee Break




    15:00-17:00


    Hands-on time with Parallelware Trainer


    Host Performance: SIMD


    Roadmap / Outlook
    (until 16:30)




     


    17:00-18:00 Guided SuperMUC-NG Tour


    19:00 Social Event (tbc.)


     





    Day 1

    The first day will cover basic parallel programming with OpenMP using the Parallelware Trainer Software by Appentra Solutions (www.appentra.com/produ.....iner/).

    We will present a unique, productivity-oriented approach by introducing its usage based on common motifs in scientific code, and how each one will be parallelized. This will enable attendees to focus on the parallelization of components and how components combine in real applications.

    Attendees will use active learning through a carefully selected set of exercises, building knowledge on parallelization of key motifs (e.g. matrix multiplication, map reduce) that are valid across multiple scientific codes in everything from CFD to Molecular Simulation.

    Appentra’s Parallelware tools are based on over 10 years of research by co-founder and CEO, Dr. Manuel Arenaz, who will be the lecturer of the first day. Parallelware  enables the identification of opportunities for parallelization and the provision of appropriate parallelization methods using state-of-the-art industrial standards. Parallelware Trainer was developed specifically to help improve the experience of HPC training, providing an interactive learning environment that uses examples that are the same or similar to real codes. Parallelware Trainer provides support for OpenMP (including multi-threading, offloading and tasking) and OpenACC (for offloading), providing users with the opportunity to use GPU services with either OpenMP or OpenACC.

    Topics covered on Day 1 include:


    The OpenMP Common Core
    Beyond the OpenMP Common Core
    Parallelization with multi-threading, offloading and tasking paradigms
    Using Parallelware Trainer: A walk-through with PI example
    Practicals: Examples codes PI, MANDELBROT, HEAT and LULESHmk
    Worksheet: Parallelizing PI and LULESHmk with OpenMP
     Decomposing code into patterns for parallelization


    Day 2 and 3

    Day 2 and 3 will cover advanced topics like:


    Mastering Tasking with OpenMP, Taskloops, Dependencies and Cancellation
    Host Performance: SIMD / Vectorization
    Host Performance: NUMA Aware Programming, Memory Access, Task Affinity, Memory Management
    Tool Support for Performance and Correctness, VI-HPS Tools
    Offloading to Accelerators
    Other Advanced Features of OpenMP 5.0
    Future Roadmap of OpenMP


    Developers usually find OpenMP easy to learn. However, they are often disappointed with the performance and scalability of the resulting code. This disappointment stems not from shortcomings of OpenMP but rather with the lack of depth with which it is employed. The lectures on Day 2 and Day 3 will address this critical need by exploring the implications of possible OpenMP parallelization strategies, both in terms of correctness and performance.

    We cover tasking with OpenMP and host performance, putting a focus on performance aspects, such as data and thread locality on NUMA architectures, false sharing, and exploitation of vector units. Also tools for performance and correctness will be presented.

    Current trends in hardware bring co-processors such as GPUs into the fold. A modern platform is often a heterogeneous system with CPU cores, GPU cores, and other specialized accelerators. OpenMP has responded by adding directives that map code and data onto a device, the target directives. We will also explore these directives as they apply to programming GPUs.

    OpenMP 5.0 features will be highlighted and the future roadmap of OpenMP will be presented.

    All topics are accompanied with extensive case studies and we discuss the corresponding language features in-depth.

    Topics might be still subject to change.

    For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    The course is organized as a PRACE training event by LRZ in collaboration with Appentra Solutions, Intel and RWTH Aachen.

    Lecturers

    Dr. Manuel Arenaz is CEO at Appentra Solutions and professor of computer science at the University of A Coruña (Spain). Holds a PhD on advanced compiler techniques for automatic parallelization of scientific codes. After 10+ years teaching parallel programming at undergraduate and PhD levels, he strongly believes that the next generation of STEM engineers needs to be educated in HPC technologies to address the digital revolution challenge. Recently, he co-founded Appentra Solutions to commercialize products and services that take advantage of Parallware, a new technology for semantic analysis of scientific HPC codes.

    Dr.  Michael Klemm holds an M.Sc.  and a Doctor of Engineering degree from the Friedrich-Alexander-University Erlangen-Nuremberg, Germany.  Michael Klemm is a Principal Engineer in the Compute Ecosystem Engineering organization of the Intel Architecture, Graphics, and Software group at Intel in Germany.  His areas of interest include compiler construction, design of programming languages, parallel programming, and performance analysis and tuning.  Michael Klemm joined the OpenMP organization in 2009 and was appointed CEO of the OpenMP ARB in 2016.

    Dr. Christian Terboven is a senior scientist and leads the HPC group at RWTH Aachen University. His research interests center around Parallel Programming and related Software Engineering aspects. Dr. Terboven has been involved in the Analysis, Tuning and Parallelization of several large-scale simulation codes for various architectures. He is responsible for several research projects in the area of programming models and approaches to improve the productivity and efficiency of modern HPC systems. He is further co-author of the new book “Using OpenMP – The Next Step“, www.openmp.org/tech/us.....step/
    events.prace-ri.eu/event/947/
    Feb 11 9:00 to Feb 13 16:30
    The registration to this course is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course convener: Mariano Vazquez

    Lecturers:  Mariano Vázquez (BSC), Okba Hamitou (Atos), Ricard Borrell (BSC), Diana Fernandez Velez (BSC), Carlos Teijeiro Barjas (SURFsara), Ben Czaja (UvA), Paul Melis (SURFsara), Cristobal Samaniego (BSC), David Oks (BSC), Alfonso Santiago(BSC), Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL).

    Objectives:The objetive of this course is to give a panorama on the use of HPC-based computational mechanics in Engineering and Environment through the projects BSC are carrying on. This panorama includes the basics of what is behind the main tools: computational mechanics and parallelization. The training is delivered in collaboration with the center of excellence CompBioMed.

    Learning outcomes: The course gives a wide perspective and the latest trends of how HPC helps in industrial, clinical and research applications allowing to achieve more realistic multiphysics simulations.  In addition, the student has the opportunity of running Jobs in Marenostrum supercomputer.

    Level: INTERMEDIATE: For trainees with some theoretical and practical knowledge

    Day 1 (Feb. 11)

    Session 1 / 9:00am – 1:00 pm
    9:00-9:15 Welcome (Mariano Vázquez, BSC)

    9:15-11:00 Compilation and Optimization in the HPC environment (O. Hamitou)

    11:00-11:20h Coffee Break

    11:20-13:00 Parallel algorithms for Computational Mechanics (Guillaume Houzeaux - Ricard Borrell, BSC)

    13:00-14:00 Lunch Break

     

    Session 2 / 2:00pm – 4:00 pm 

    14:00-16:00 Data Visulization for Researchers Crash Course (Diana Fernandez Velez, BSC)

    16:00-18:00 Visit to MareNostrum

     

    Day 2 (Feb. 12)

     

    9:00-10:50 Introduction to HPC in Computational Modelling (Carlos Teijeiro Barjas, SURFsara)

    10:50-11:10h Coffee Break

    11:10-13:00 Computational Hemodynamics on HPC (UvA) (Ben Czaja, UvA)

    13:00-14:00 Lunch Break

     
    Session 4 / 2:00pm – 6:00 pm (2 h lectures, 2 h practical)


    14:00-15:30 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

    15:30-17:00 Cardiac Modelling (J. Aguado-Sierra)

     

    Day 3 (Feb. 13)

     

    Session 5 / 9:00am – 1:00 pm (4 h lectures)

    9:00-11:00 Fluid-Structure Interaction methods for biomechanics (C. Samaniego, D. Oks, A. Santiago)

    11:00-11:20h Coffee Break

    11:20-13:00 hands-on onFluid-Structure Interaction methods for biomechanics(C. Samaniego, D. Oks)

     
    Session 6 / 2:00pm – 6:00 pm 


    14:00-15:00 Introduction to Computer-Aided Drug Design (CADD) and GPCR Modelling (Dr Alexander Heifetz, EVOTEC)

    15:00-16:00 Innovations in HPC-training for medical, science and engineering students (Andrea Townsend-Nicholson, UCL)

    16:00-16:15 Coffee Break

    16:15-18:00 Molecular Medicine: Hands On (Andrea Townsend-Nicholson, UCL)

    END of COURSE

     
    events.prace-ri.eu/event/912/
    Feb 11 9:00 to Feb 13 18:00
    Important: For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    With the increasing prevalence of multicore processors, shared-memory programming models are essential. OpenMP is a popular, portable, widely supported, and easy-to-use shared-memory model.

    Since its advent in 1997, the OpenMP programming model has proved to be a key driver behind parallel programming for shared-memory architectures.  Its powerful and flexible programming model has allowed researchers from various domains to enable parallelism in their applications.  Over the more than two decades of its existence, OpenMP has tracked the evolution of hardware and the complexities of software to ensure that it stays as relevant to today’s high performance computing community as it was in 1997.

    This workshop will cover a wide range of  topics, reaching from the basics of OpenMP programming using the "OpenMP Common Core" to really advanced topics. During each day lectures will be mixed with hands-on sessions on the LRZ system IvyMUC.

    Preliminary Agenda





     


    Day 1


    Day 2


    Day 3




    09:00-10:30


    Introduction to the OpenMP common core


    Tasking


    Tools for Performance and Correctness




    10:30-10:45


    Coffee Break


    Coffee Break


    Coffee Break




    10:45-12:00


    Decomposing code into patterns for parallelization


    Tasking


    Offloading to Accelerators




    12:00-13:00


    Lunch Break


    Lunch Break


    Lunch Break




    13:00-14:45


    Beyond OpenMP common core with tasking and offloading


    Host Performance: NUMA


    Other Advanced Features of OpenMP 5.0




    14:45-15:00


    Coffee Break


    Coffee Break


    Coffee Break




    15:00-17:00


    Hands-on time with Parallelware Trainer


    Host Performance: SIMD


    Roadmap / Outlook
    (until 16:30)




     


    17:00-18:00 Guided SuperMUC-NG Tour


    19:00 Social Event (tbc.)


     





    Day 1

    The first day will cover basic parallel programming with OpenMP using the Parallelware Trainer Software by Appentra Solutions (www.appentra.com/produ.....iner/).

    We will present a unique, productivity-oriented approach by introducing its usage based on common motifs in scientific code, and how each one will be parallelized. This will enable attendees to focus on the parallelization of components and how components combine in real applications.

    Attendees will use active learning through a carefully selected set of exercises, building knowledge on parallelization of key motifs (e.g. matrix multiplication, map reduce) that are valid across multiple scientific codes in everything from CFD to Molecular Simulation.

    Appentra’s Parallelware tools are based on over 10 years of research by co-founder and CEO, Dr. Manuel Arenaz, who will be the lecturer of the first day. Parallelware  enables the identification of opportunities for parallelization and the provision of appropriate parallelization methods using state-of-the-art industrial standards. Parallelware Trainer was developed specifically to help improve the experience of HPC training, providing an interactive learning environment that uses examples that are the same or similar to real codes. Parallelware Trainer provides support for OpenMP (including multi-threading, offloading and tasking) and OpenACC (for offloading), providing users with the opportunity to use GPU services with either OpenMP or OpenACC.

    Topics covered on Day 1 include:


    The OpenMP Common Core
    Beyond the OpenMP Common Core
    Parallelization with multi-threading, offloading and tasking paradigms
    Using Parallelware Trainer: A walk-through with PI example
    Practicals: Examples codes PI, MANDELBROT, HEAT and LULESHmk
    Worksheet: Parallelizing PI and LULESHmk with OpenMP
     Decomposing code into patterns for parallelization


    Day 2 and 3

    Day 2 and 3 will cover advanced topics like:


    Mastering Tasking with OpenMP, Taskloops, Dependencies and Cancellation
    Host Performance: SIMD / Vectorization
    Host Performance: NUMA Aware Programming, Memory Access, Task Affinity, Memory Management
    Tool Support for Performance and Correctness, VI-HPS Tools
    Offloading to Accelerators
    Other Advanced Features of OpenMP 5.0
    Future Roadmap of OpenMP


    Developers usually find OpenMP easy to learn. However, they are often disappointed with the performance and scalability of the resulting code. This disappointment stems not from shortcomings of OpenMP but rather with the lack of depth with which it is employed. The lectures on Day 2 and Day 3 will address this critical need by exploring the implications of possible OpenMP parallelization strategies, both in terms of correctness and performance.

    We cover tasking with OpenMP and host performance, putting a focus on performance aspects, such as data and thread locality on NUMA architectures, false sharing, and exploitation of vector units. Also tools for performance and correctness will be presented.

    Current trends in hardware bring co-processors such as GPUs into the fold. A modern platform is often a heterogeneous system with CPU cores, GPU cores, and other specialized accelerators. OpenMP has responded by adding directives that map code and data onto a device, the target directives. We will also explore these directives as they apply to programming GPUs.

    OpenMP 5.0 features will be highlighted and the future roadmap of OpenMP will be presented.

    All topics are accompanied with extensive case studies and we discuss the corresponding language features in-depth.

    Topics might be still subject to change.

    For the hands-on sessions participants need to bring their own laptops with an ssh-client installed.

    The course is organized as a PRACE training event by LRZ in collaboration with Appentra Solutions, Intel and RWTH Aachen.

    Lecturers

    Dr. Manuel Arenaz is CEO at Appentra Solutions and professor of computer science at the University of A Coruña (Spain). Holds a PhD on advanced compiler techniques for automatic parallelization of scientific codes. After 10+ years teaching parallel programming at undergraduate and PhD levels, he strongly believes that the next generation of STEM engineers needs to be educated in HPC technologies to address the digital revolution challenge. Recently, he co-founded Appentra Solutions to commercialize products and services that take advantage of Parallware, a new technology for semantic analysis of scientific HPC codes.

    Dr.  Michael Klemm holds an M.Sc.  and a Doctor of Engineering degree from the Friedrich-Alexander-University Erlangen-Nuremberg, Germany.  Michael Klemm is a Principal Engineer in the Compute Ecosystem Engineering organization of the Intel Architecture, Graphics, and Software group at Intel in Germany.  His areas of interest include compiler construction, design of programming languages, parallel programming, and performance analysis and tuning.  Michael Klemm joined the OpenMP organization in 2009 and was appointed CEO of the OpenMP ARB in 2016.

    Dr. Christian Terboven is a senior scientist and leads the HPC group at RWTH Aachen University. His research interests center around Parallel Programming and related Software Engineering aspects. Dr. Terboven has been involved in the Analysis, Tuning and Parallelization of several large-scale simulation codes for various architectures. He is responsible for several research projects in the area of programming models and approaches to improve the productivity and efficiency of modern HPC systems. He is further co-author of the new book “Using OpenMP – The Next Step“, www.openmp.org/tech/us.....step/
    events.prace-ri.eu/event/947/
    Feb 11 9:00 to Feb 13 16:30
    The registration to this course is now open. Please, bring your own laptop. All the PATC courses at BSC are free of charge.

    Course convener: Mariano Vazquez

    Lecturers:  Mariano Vázquez (BSC), Okba Hamitou (Atos), Ricard Borrell (BSC), Diana Fernandez Velez (BSC), Carlos Teijeiro Barjas (SURFsara), Ben Czaja (UvA), Paul Melis (SURFsara), Cristobal Samaniego (BSC), David Oks (BSC), Alfonso Santiago(BSC), Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL).

    Objectives:The objetive of this course is to give a panorama on the use of HPC-based computational mechanics in Engineering and Environment through the projects BSC are carrying on. This panorama includes the basics of what is behind the main tools: computational mechanics and parallelization. The training is delivered in collaboration with the center of excellence CompBioMed.

    Learning outcomes: The course gives a wide perspective and the latest trends of how HPC helps in industrial, clinical and research applications allowing to achieve more realistic multiphysics simulations.  In addition, the student has the opportunity of running Jobs in Marenostrum supercomputer.

    Level: INTERMEDIATE: For trainees with some theoretical and practical knowledge

    Day 1 (Feb. 11)

    Session 1 / 9:00am – 1:00 pm
    9:00-9:15 Welcome (Mariano Vázquez, BSC)

    9:15-11:00 Compilation and Optimization in the HPC environment (O. Hamitou)

    11:00-11:20h Coffee Break

    11:20-13:00 Parallel algorithms for Computational Mechanics (Guillaume Houzeaux - Ricard Borrell, BSC)

    13:00-14:00 Lunch Break

     

    Session 2 / 2:00pm – 4:00 pm 

    14:00-16:00 Data Visulization for Researchers Crash Course (Diana Fernandez Velez, BSC)

    16:00-18:00 Visit to MareNostrum

     

    Day 2 (Feb. 12)

     

    9:00-10:50 Introduction to HPC in Computational Modelling (Carlos Teijeiro Barjas, SURFsara)

    10:50-11:10h Coffee Break

    11:10-13:00 Computational Hemodynamics on HPC (UvA) (Ben Czaja, UvA)

    13:00-14:00 Lunch Break

     
    Session 4 / 2:00pm – 6:00 pm (2 h lectures, 2 h practical)


    14:00-15:30 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

    15:30-17:00 Cardiac Modelling (J. Aguado-Sierra)

     

    Day 3 (Feb. 13)

     

    Session 5 / 9:00am – 1:00 pm (4 h lectures)

    9:00-11:00 Fluid-Structure Interaction methods for biomechanics (C. Samaniego, D. Oks, A. Santiago)

    11:00-11:20h Coffee Break

    11:20-13:00 hands-on onFluid-Structure Interaction methods for biomechanics(C. Samaniego, D. Oks)

     
    Session 6 / 2:00pm – 6:00 pm 


    14:00-15:00 Introduction to Computer-Aided Drug Design (CADD) and GPCR Modelling (Dr Alexander Heifetz, EVOTEC)

    15:00-16:00 Innovations in HPC-training for medical, science and engineering students (Andrea Townsend-Nicholson, UCL)

    16:00-16:15 Coffee Break

    16:15-18:00 Molecular Medicine: Hands On (Andrea Townsend-Nicholson, UCL)

    END of COURSE

     
    events.prace-ri.eu/event/912/
    Feb 11 9:00 to Feb 13 18:00
    14
     
    15
     
    16
     
    The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required to learn from data with a high accuracy. Easy application examples will foster the theoretical course elements that also will illustrate problems like overfitting followed by mechanisms such as validation and regularization that prevent such problems.

    The tutorial will start from a very simple application example in order to teach foundations like the role of features in data, linear separability, or decision boundaries for machine learning models. In particular this course will point to key challenges in analyzing large quantities of data sets (aka ‘big data’) in order to motivate the use of parallel and scalable machine learning algorithms that will be used in the course. The course targets specific challenges in analyzing large quantities of datasets that cannot be analyzed with traditional serial methods provided by tools such as R, SAS, or Matlab. This includes several challenges as part of the machine learning algorithms, the distribution of data, or the process of performing validation. The course will introduce selected solutions to overcome these challenges using parallel and scalable computing techniques based on the Message Passing Interface (MPI) and OpenMP that run on massively parallel High Performance Computing (HPC) platforms. The course ends with a more recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency.

    Prerequisites:
    Knowledge on job submissions to large HPC machines using batch scripts, knowledge of mathematical basics in linear algebra helpful.

    Participants should bring their own notebooks (with an ssh-client).

    Learning outcome:
    After this course participants will have a general understanding how to approach data analysis problems in a systematic way. In particular this course will provide insights into key benefits of parallelization such as during the n-fold cross-validation process where significant speed-ups can be obtained compared to serial methods. Participants will also get a detailed understanding why and how parallelization provides benefits to a scalable data analyzing process using machine learning methods for big data and a general understanding for which problems deep learning algorithms are useful and how parallel and scalable computing is facilitating the learning process when facing big datasets. Participants will learn that deep learning can actually perform ‘feature learning’ that bears the potential to significantly speed-up data analysis processes that previously required much feature engineering.

    Course slides from the last training in February 2019 can be found at


    www.morrisriedel.de/pra.....rning

    Application
    Applicants will be notified one month before the course starts, whether they are accepted for participitation.

    Instructors: Prof. Dr. Morris Riedel, Dr. Gabriele Cavallaro, Dr. Jenia Jitsev, Jülich Supercomputing Centre

    Contact
    For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
    events.prace-ri.eu/event/960/
    Feb 17 9:30 to Feb 19 16:30
    Participating AiiDA plugin developers will be shown best practices and useful tips on:

     - code development practices (automated testing, continuous integration, code style checks, packaging, distribution …)
     - writing reusable, robust and modular workflows

     - taking full advantage of the AiiDA v1.0 API

    In addition, during the event we will discuss and define common APIs for workflows for the computation of certain materials properties.
    When adopted by plugin developers, these common APIs enable AiiDA users to compute a material's property using different codes without the need to know the interface of each plugin in detail.


    Target audience
    Computational scientists that actively develop/maintain one or more AiiDA plugins (registered on the AiiDA plugin registry) in order to automate calculations with AiiDA.

    Prerequisites

    Being a developer or maintainer of at least one plugin package registered on the AiiDA registry

    Organisers
    Nicola Spallanzani, Giovanni Pizzi, Sebastiaan Huber, Francisco Ramirez, Miki Bonacci, Emanuele Bosoni, Vasily Tseplyaev, Fabio Affinito.

    Tutors

    Giovanni Pizzi, Sebastiaan Huber, Francisco F Ramirez, Leopold Talirz, Aliaksandr Yakutovich.

    Acknowledgements:
    The tutors acknowledge financial support by PRACE and by the EU Centre of Excellence MaX “MAterials design at the eXascale” A H2020-INFRAEDI-2018-1 funded project Grant Agreement n. 824143 and by NCCR MARVEL funded by the Swiss National Science Foundation.




    events.prace-ri.eu/event/957/
    Feb 17 15:00 to Feb 21 14:00
    Participating AiiDA plugin developers will be shown best practices and useful tips on:

     - code development practices (automated testing, continuous integration, code style checks, packaging, distribution …)
     - writing reusable, robust and modular workflows

     - taking full advantage of the AiiDA v1.0 API

    In addition, during the event we will discuss and define common APIs for workflows for the computation of certain materials properties.
    When adopted by plugin developers, these common APIs enable AiiDA users to compute a material's property using different codes without the need to know the interface of each plugin in detail.


    Target audience
    Computational scientists that actively develop/maintain one or more AiiDA plugins (registered on the AiiDA plugin registry) in order to automate calculations with AiiDA.

    Prerequisites

    Being a developer or maintainer of at least one plugin package registered on the AiiDA registry

    Organisers
    Nicola Spallanzani, Giovanni Pizzi, Sebastiaan Huber, Francisco Ramirez, Miki Bonacci, Emanuele Bosoni, Vasily Tseplyaev, Fabio Affinito.

    Tutors

    Giovanni Pizzi, Sebastiaan Huber, Francisco F Ramirez, Leopold Talirz, Aliaksandr Yakutovich.

    Acknowledgements:
    The tutors acknowledge financial support by PRACE and by the EU Centre of Excellence MaX “MAterials design at the eXascale” A H2020-INFRAEDI-2018-1 funded project Grant Agreement n. 824143 and by NCCR MARVEL funded by the Swiss National Science Foundation.




    events.prace-ri.eu/event/957/
    Feb 17 15:00 to Feb 21 14:00
    The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required to learn from data with a high accuracy. Easy application examples will foster the theoretical course elements that also will illustrate problems like overfitting followed by mechanisms such as validation and regularization that prevent such problems.

    The tutorial will start from a very simple application example in order to teach foundations like the role of features in data, linear separability, or decision boundaries for machine learning models. In particular this course will point to key challenges in analyzing large quantities of data sets (aka ‘big data’) in order to motivate the use of parallel and scalable machine learning algorithms that will be used in the course. The course targets specific challenges in analyzing large quantities of datasets that cannot be analyzed with traditional serial methods provided by tools such as R, SAS, or Matlab. This includes several challenges as part of the machine learning algorithms, the distribution of data, or the process of performing validation. The course will introduce selected solutions to overcome these challenges using parallel and scalable computing techniques based on the Message Passing Interface (MPI) and OpenMP that run on massively parallel High Performance Computing (HPC) platforms. The course ends with a more recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency.

    Prerequisites:
    Knowledge on job submissions to large HPC machines using batch scripts, knowledge of mathematical basics in linear algebra helpful.

    Participants should bring their own notebooks (with an ssh-client).

    Learning outcome:
    After this course participants will have a general understanding how to approach data analysis problems in a systematic way. In particular this course will provide insights into key benefits of parallelization such as during the n-fold cross-validation process where significant speed-ups can be obtained compared to serial methods. Participants will also get a detailed understanding why and how parallelization provides benefits to a scalable data analyzing process using machine learning methods for big data and a general understanding for which problems deep learning algorithms are useful and how parallel and scalable computing is facilitating the learning process when facing big datasets. Participants will learn that deep learning can actually perform ‘feature learning’ that bears the potential to significantly speed-up data analysis processes that previously required much feature engineering.

    Course slides from the last training in February 2019 can be found at


    www.morrisriedel.de/pra.....rning

    Application
    Applicants will be notified one month before the course starts, whether they are accepted for participitation.

    Instructors: Prof. Dr. Morris Riedel, Dr. Gabriele Cavallaro, Dr. Jenia Jitsev, Jülich Supercomputing Centre

    Contact
    For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
    events.prace-ri.eu/event/960/
    Feb 17 9:30 to Feb 19 16:30
    Participating AiiDA plugin developers will be shown best practices and useful tips on:

     - code development practices (automated testing, continuous integration, code style checks, packaging, distribution …)
     - writing reusable, robust and modular workflows

     - taking full advantage of the AiiDA v1.0 API

    In addition, during the event we will discuss and define common APIs for workflows for the computation of certain materials properties.
    When adopted by plugin developers, these common APIs enable AiiDA users to compute a material's property using different codes without the need to know the interface of each plugin in detail.


    Target audience
    Computational scientists that actively develop/maintain one or more AiiDA plugins (registered on the AiiDA plugin registry) in order to automate calculations with AiiDA.

    Prerequisites

    Being a developer or maintainer of at least one plugin package registered on the AiiDA registry

    Organisers
    Nicola Spallanzani, Giovanni Pizzi, Sebastiaan Huber, Francisco Ramirez, Miki Bonacci, Emanuele Bosoni, Vasily Tseplyaev, Fabio Affinito.

    Tutors

    Giovanni Pizzi, Sebastiaan Huber, Francisco F Ramirez, Leopold Talirz, Aliaksandr Yakutovich.

    Acknowledgements:
    The tutors acknowledge financial support by PRACE and by the EU Centre of Excellence MaX “MAterials design at the eXascale” A H2020-INFRAEDI-2018-1 funded project Grant Agreement n. 824143 and by NCCR MARVEL funded by the Swiss National Science Foundation.




    events.prace-ri.eu/event/957/
    Feb 17 15:00 to Feb 21 14:00
    The course offers basics of analyzing data with machine learning and data mining algorithms in order to understand foundations of learning from large quantities of data. This course is especially oriented towards beginners that have no previous knowledge of machine learning techniques. The course consists of general methods for data analysis in order to understand clustering, classification, and regression. This includes a thorough discussion of test datasets, training datasets, and validation datasets required to learn from data with a high accuracy. Easy application examples will foster the theoretical course elements that also will illustrate problems like overfitting followed by mechanisms such as validation and regularization that prevent such problems.

    The tutorial will start from a very simple application example in order to teach foundations like the role of features in data, linear separability, or decision boundaries for machine learning models. In particular this course will point to key challenges in analyzing large quantities of data sets (aka ‘big data’) in order to motivate the use of parallel and scalable machine learning algorithms that will be used in the course. The course targets specific challenges in analyzing large quantities of datasets that cannot be analyzed with traditional serial methods provided by tools such as R, SAS, or Matlab. This includes several challenges as part of the machine learning algorithms, the distribution of data, or the process of performing validation. The course will introduce selected solutions to overcome these challenges using parallel and scalable computing techniques based on the Message Passing Interface (MPI) and OpenMP that run on massively parallel High Performance Computing (HPC) platforms. The course ends with a more recent machine learning method known as deep learning that emerged as a promising disruptive approach, allowing knowledge discovery from large datasets in an unprecedented effectiveness and efficiency.

    Prerequisites:
    Knowledge on job submissions to large HPC machines using batch scripts, knowledge of mathematical basics in linear algebra helpful.

    Participants should bring their own notebooks (with an ssh-client).

    Learning outcome:
    After this course participants will have a general understanding how to approach data analysis problems in a systematic way. In particular this course will provide insights into key benefits of parallelization such as during the n-fold cross-validation process where significant speed-ups can be obtained compared to serial methods. Participants will also get a detailed understanding why and how parallelization provides benefits to a scalable data analyzing process using machine learning methods for big data and a general understanding for which problems deep learning algorithms are useful and how parallel and scalable computing is facilitating the learning process when facing big datasets. Participants will learn that deep learning can actually perform ‘feature learning’ that bears the potential to significantly speed-up data analysis processes that previously required much feature engineering.

    Course slides from the last training in February 2019 can be found at


    www.morrisriedel.de/pra.....rning

    Application
    Applicants will be notified one month before the course starts, whether they are accepted for participitation.

    Instructors: Prof. Dr. Morris Riedel, Dr. Gabriele Cavallaro, Dr. Jenia Jitsev, Jülich Supercomputing Centre

    Contact
    For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
    events.prace-ri.eu/event/960/
    Feb 17 9:30 to Feb 19 16:30
    Participating AiiDA plugin developers will be shown best practices and useful tips on:

     - code development practices (automated testing, continuous integration, code style checks, packaging, distribution …)
     - writing reusable, robust and modular workflows

     - taking full advantage of the AiiDA v1.0 API

    In addition, during the event we will discuss and define common APIs for workflows for the computation of certain materials properties.
    When adopted by plugin developers, these common APIs enable AiiDA users to compute a material's property using different codes without the need to know the interface of each plugin in detail.


    Target audience
    Computational scientists that actively develop/maintain one or more AiiDA plugins (registered on the AiiDA plugin registry) in order to automate calculations with AiiDA.

    Prerequisites

    Being a developer or maintainer of at least one plugin package registered on the AiiDA registry

    Organisers
    Nicola Spallanzani, Giovanni Pizzi, Sebastiaan Huber, Francisco Ramirez, Miki Bonacci, Emanuele Bosoni, Vasily Tseplyaev, Fabio Affinito.

    Tutors

    Giovanni Pizzi, Sebastiaan Huber, Francisco F Ramirez, Leopold Talirz, Aliaksandr Yakutovich.

    Acknowledgements:
    The tutors acknowledge financial support by PRACE and by the EU Centre of Excellence MaX “MAterials design at the eXascale” A H2020-INFRAEDI-2018-1 funded project Grant Agreement n. 824143 and by NCCR MARVEL funded by the Swiss National Science Foundation.




    events.prace-ri.eu/event/957/
    Feb 17 15:00 to Feb 21 14:00
    21
     
    22
     
    23
     
    Application deadline:

    January 24th, 2020

    Description:

    Heterogeneous architectures with nodes featuring accelerator cards or sockets are taking an important share in the HPC market, given their superiority in term of flop/watt with respect to CISC and RISC architecture.
    To be effective on heterogeneous architecture applications usually requires important refactoring and adaptation, and many programming paradigms are available, some vendor specific and some other defined by an open standard,
    but without a clear winner yet (e.g. as it is the case for message passing communications where there is MPI, available for all network technologies).

    This school focus on software development techniques to address the implementation of new HPC applications and the re-factory of existing ones, in the era of heterogeneous, energy efficient, massively parallel architectures,
    toward exascale, with theoretical lectures and hands-on sessions on the different most promising programming techniques and paradigms for accelerated computing.

    Software engineering techniques and high productivity languages will complement lectures on parallel programming and porting toward new architectures, to allow the implementation of application that can be maintained across a complex and fast evolving HPC architectures.
     

    Topics:


    Heterogeneous architectures
    Elements of software engineering
    Parallel programming techniques for accelerated computing, including CUDA, OpenMP, OpenACC, SYCL
    Parallel programming techniques for massively parallel applications
    Models for applications integrating MPI, OpenMP OpenACC, CUDA and CUDA Fortran paradigms


    Target audience:

    The school is aimed at PRACE users, final year master students, PhD students, and young researchers in computational sciences and engineering, with different backgrounds, interested in applying the emerging technologies on high performance computing to their research.

    Pre-requisites:

    Good knowledge of parallel programming with MPI and/or OpenMP, knowledge of FORTRAN and C languages. Basic knowledge of parallel computer architectures.

    Admitted students:

    Attendance is free.

    A grant of 300 EUR (for students working abroad) and 150 EUR (for students working in Italy) will be available for participants not funded by their institution and not working or living in the Bologna area. Documentation will be required. Lunches for the 5 days will be provided by Cineca. Each student will be given a two month access to the Cineca's supercomputing resources.

    The number of participants is limited to 25 students.
    Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

    DUE TO PRIVACY REASON THE STUDENTS ADMITTED AND NOT ADMITTED WILL BE CONTACTED VIA EMAIL ON JANUARY, FRIDAY 31st. IF YOU SUBMITTED AND DON'T RECEIVE THE EMAIL, PLEASE WRITE AT corsi.hpc@cineca.it.  

    Acknowledgement:

    The support of CINI for the software engineering module is gratefully acknowledged.

     
    events.prace-ri.eu/event/976/
    Feb 24 9:00 to Feb 28 18:00
    Application deadline:

    January 24th, 2020

    Description:

    Heterogeneous architectures with nodes featuring accelerator cards or sockets are taking an important share in the HPC market, given their superiority in term of flop/watt with respect to CISC and RISC architecture.
    To be effective on heterogeneous architecture applications usually requires important refactoring and adaptation, and many programming paradigms are available, some vendor specific and some other defined by an open standard,
    but without a clear winner yet (e.g. as it is the case for message passing communications where there is MPI, available for all network technologies).

    This school focus on software development techniques to address the implementation of new HPC applications and the re-factory of existing ones, in the era of heterogeneous, energy efficient, massively parallel architectures,
    toward exascale, with theoretical lectures and hands-on sessions on the different most promising programming techniques and paradigms for accelerated computing.

    Software engineering techniques and high productivity languages will complement lectures on parallel programming and porting toward new architectures, to allow the implementation of application that can be maintained across a complex and fast evolving HPC architectures.
     

    Topics:


    Heterogeneous architectures
    Elements of software engineering
    Parallel programming techniques for accelerated computing, including CUDA, OpenMP, OpenACC, SYCL
    Parallel programming techniques for massively parallel applications
    Models for applications integrating MPI, OpenMP OpenACC, CUDA and CUDA Fortran paradigms


    Target audience:

    The school is aimed at PRACE users, final year master students, PhD students, and young researchers in computational sciences and engineering, with different backgrounds, interested in applying the emerging technologies on high performance computing to their research.

    Pre-requisites:

    Good knowledge of parallel programming with MPI and/or OpenMP, knowledge of FORTRAN and C languages. Basic knowledge of parallel computer architectures.

    Admitted students:

    Attendance is free.

    A grant of 300 EUR (for students working abroad) and 150 EUR (for students working in Italy) will be available for participants not funded by their institution and not working or living in the Bologna area. Documentation will be required. Lunches for the 5 days will be provided by Cineca. Each student will be given a two month access to the Cineca's supercomputing resources.

    The number of participants is limited to 25 students.
    Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

    DUE TO PRIVACY REASON THE STUDENTS ADMITTED AND NOT ADMITTED WILL BE CONTACTED VIA EMAIL ON JANUARY, FRIDAY 31st. IF YOU SUBMITTED AND DON'T RECEIVE THE EMAIL, PLEASE WRITE AT corsi.hpc@cineca.it.  

    Acknowledgement:

    The support of CINI for the software engineering module is gratefully acknowledged.

     
    events.prace-ri.eu/event/976/
    Feb 24 9:00 to Feb 28 18:00
    The registration to this course is now open.

    All PATC Courses at BSC do not charge fees.

    PLEASE BRING YOUR OWN LAPTOP.

    Convener: 
    Antonio Peña, Computer Sciences Senior Researcher, Accelerators and Communications for High Performance Computing, BSC

    Objectives: 

    The objective of this course is to learn how to use systems with more than one memory subsystem. We will see the different options on using Intel’s KNL memory subsystems and systems equipped with Intel’s Optane technology.

    Learning Outcomes:

    The students who finish this course will able to leverage applications using multiple memory subsystems

    Level: INTERMEDIATE: for trainees with some theoretical and practical knowledge; those who finished the beginners course

    Prerequisites: Basic skills in C programming.

    Agenda:




    9:00-9:30
    Registration
     


    9:30-10:30
    Introduction to Memory Technologies
    Petar Radojkovic


    10:30-11:00
    Coffee Break
     


    11:00-12:30
    Use of Heterogeneous Memories
    Antonio J. Peña


    12:30-13:00
    Hands-on: Environment Setup
    Marc Jordà


    13:00-14:30
    Lunch
     


    14:30-18:00
    Hands-on
    Marc Jordà



    events.prace-ri.eu/event/913/
    Feb 25 9:00 18:00
    Application deadline:

    January 24th, 2020

    Description:

    Heterogeneous architectures with nodes featuring accelerator cards or sockets are taking an important share in the HPC market, given their superiority in term of flop/watt with respect to CISC and RISC architecture.
    To be effective on heterogeneous architecture applications usually requires important refactoring and adaptation, and many programming paradigms are available, some vendor specific and some other defined by an open standard,
    but without a clear winner yet (e.g. as it is the case for message passing communications where there is MPI, available for all network technologies).

    This school focus on software development techniques to address the implementation of new HPC applications and the re-factory of existing ones, in the era of heterogeneous, energy efficient, massively parallel architectures,
    toward exascale, with theoretical lectures and hands-on sessions on the different most promising programming techniques and paradigms for accelerated computing.

    Software engineering techniques and high productivity languages will complement lectures on parallel programming and porting toward new architectures, to allow the implementation of application that can be maintained across a complex and fast evolving HPC architectures.
     

    Topics:


    Heterogeneous architectures
    Elements of software engineering
    Parallel programming techniques for accelerated computing, including CUDA, OpenMP, OpenACC, SYCL
    Parallel programming techniques for massively parallel applications
    Models for applications integrating MPI, OpenMP OpenACC, CUDA and CUDA Fortran paradigms


    Target audience:

    The school is aimed at PRACE users, final year master students, PhD students, and young researchers in computational sciences and engineering, with different backgrounds, interested in applying the emerging technologies on high performance computing to their research.

    Pre-requisites:

    Good knowledge of parallel programming with MPI and/or OpenMP, knowledge of FORTRAN and C languages. Basic knowledge of parallel computer architectures.

    Admitted students:

    Attendance is free.

    A grant of 300 EUR (for students working abroad) and 150 EUR (for students working in Italy) will be available for participants not funded by their institution and not working or living in the Bologna area. Documentation will be required. Lunches for the 5 days will be provided by Cineca. Each student will be given a two month access to the Cineca's supercomputing resources.

    The number of participants is limited to 25 students.
    Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

    DUE TO PRIVACY REASON THE STUDENTS ADMITTED AND NOT ADMITTED WILL BE CONTACTED VIA EMAIL ON JANUARY, FRIDAY 31st. IF YOU SUBMITTED AND DON'T RECEIVE THE EMAIL, PLEASE WRITE AT corsi.hpc@cineca.it.  

    Acknowledgement:

    The support of CINI for the software engineering module is gratefully acknowledged.

     
    events.prace-ri.eu/event/976/
    Feb 24 9:00 to Feb 28 18:00
    The registration to this course is now open. Please, bring your own laptop.  All the PATC courses at BSC are free of charge.

    Course convener: David Vicente

    Lecturers: David Vicente, Javier Bartolomé, Jorge Rodríguez, Carlos Tripiana, Oscar Hernandez, Félix Ramos, Cristian Morales, Francisco González, Ricard Zarco, Helena Gómez, Pablo Ródenas, Gaurav Saxena y Maicon Faria.

    Objectives: The objective of this course is to present to potential users the new configuration of MareNostrum and a introduction on how to use the new system (batch system, compilers, hardware, MPI, etc).Also It will provide an introduction about RES and PRACE infrastructures and how to get access to the supercomputing resources available.

    Learning Outcomes: The students who finish this course will know the internal architecture of the new MareNostrum, how it works, the ways to get access to this infrastructure and also some information about optimization techniques for its architecture.

    Level: INTERMEDIATE -for trainees with some theoretical and practical knowledge; those who finished the beginners course.

    Prerequisites:  Any potential user of a HPC infrastructure will be welcome

    Agenda:


    DAY 1 (Feb. 26) 09:00 - 17:00                                 

    Session 1 / 09:00 – 13:00 (2:45 h lectures, 0:45h practical)                                       

    9:00. - 9:30 Introduction to BSC, PRACE PATC and this training (David Vicente)

    9:30 - 10:30 MareNostrum 4 – the view from System administration group (Javier Bartolomé)

    10:30 - 11:00 COFFEE BREAK      

    11:00 - 11:45 How to use MN4 – Basics: Batch system, file systems, compilers, modules, DT, BSC commands     (Félix Ramos, Francisco González, Ricard Zarco, Helena Gómez)

    11:45 - 12:30 Hands-on I (Félix Ramos, Francisco González, Ricard Zarco, Helena Gómez)

    12:30 - 13:00 Deep Learning and Big data tools on MN4  (Carlos Tripiana)

    13:00 - 14:15 LUNCH (not hosted)          

    Session 2 / 14:15 – 17:00 (2:15h)                                         

    14:15 - 15:15 How to use MN4 – Parallel programming: OpenMP, Hands-on II (Jorge Rodríguez, Maicon Saul Faria)

    15:15 - 16:00 How to use MN4 – Parallel programming: MPI (Pablo Ródenas, Gaurav Saxena)

    16:00 - 16:30 COFFEE BREAK      

    16:30 - 17:00 How to use MN4 – Parallel programming: MPI Hands-on III (Pablo Ródenas, Gaurav Saxena)

                                              

    DAY 2 (Feb. 27) 09:00 - 13:00                                 

    Session 3 / 09:00 – 13:00 (2:00h lectures, 1:30 h practical)                                       

    9:00 - 9:30 How can I get resources from you? - RES (David Vicente)

    9:30 - 10:00 How can I get Resources from you? – PRACE (Cristian Morales)

    10:00 - 10:30 HPC Architectures (David Vicente)

    10:30 - 11:00 COFFEE BREAK      

    11:00 - 12:00 Containers on HPC (Óscar Hernández)

    12:00 - 13:00 Debugging on MareNostrum, from GDB to DDT (Óscar Hernández, Cristian Morales)


    END of COURSE
    events.prace-ri.eu/event/943/
    Feb 26 9:00 to Feb 27 13:00
    Application deadline:

    January 24th, 2020

    Description:

    Heterogeneous architectures with nodes featuring accelerator cards or sockets are taking an important share in the HPC market, given their superiority in term of flop/watt with respect to CISC and RISC architecture.
    To be effective on heterogeneous architecture applications usually requires important refactoring and adaptation, and many programming paradigms are available, some vendor specific and some other defined by an open standard,
    but without a clear winner yet (e.g. as it is the case for message passing communications where there is MPI, available for all network technologies).

    This school focus on software development techniques to address the implementation of new HPC applications and the re-factory of existing ones, in the era of heterogeneous, energy efficient, massively parallel architectures,
    toward exascale, with theoretical lectures and hands-on sessions on the different most promising programming techniques and paradigms for accelerated computing.

    Software engineering techniques and high productivity languages will complement lectures on parallel programming and porting toward new architectures, to allow the implementation of application that can be maintained across a complex and fast evolving HPC architectures.
     

    Topics:


    Heterogeneous architectures
    Elements of software engineering
    Parallel programming techniques for accelerated computing, including CUDA, OpenMP, OpenACC, SYCL
    Parallel programming techniques for massively parallel applications
    Models for applications integrating MPI, OpenMP OpenACC, CUDA and CUDA Fortran paradigms


    Target audience:

    The school is aimed at PRACE users, final year master students, PhD students, and young researchers in computational sciences and engineering, with different backgrounds, interested in applying the emerging technologies on high performance computing to their research.

    Pre-requisites:

    Good knowledge of parallel programming with MPI and/or OpenMP, knowledge of FORTRAN and C languages. Basic knowledge of parallel computer architectures.

    Admitted students:

    Attendance is free.

    A grant of 300 EUR (for students working abroad) and 150 EUR (for students working in Italy) will be available for participants not funded by their institution and not working or living in the Bologna area. Documentation will be required. Lunches for the 5 days will be provided by Cineca. Each student will be given a two month access to the Cineca's supercomputing resources.

    The number of participants is limited to 25 students.
    Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

    DUE TO PRIVACY REASON THE STUDENTS ADMITTED AND NOT ADMITTED WILL BE CONTACTED VIA EMAIL ON JANUARY, FRIDAY 31st. IF YOU SUBMITTED AND DON'T RECEIVE THE EMAIL, PLEASE WRITE AT corsi.hpc@cineca.it.  

    Acknowledgement:

    The support of CINI for the software engineering module is gratefully acknowledged.

     
    events.prace-ri.eu/event/976/
    Feb 24 9:00 to Feb 28 18:00
    The registration to this course is now open. Please, bring your own laptop.  All the PATC courses at BSC are free of charge.

    Course convener: David Vicente

    Lecturers: David Vicente, Javier Bartolomé, Jorge Rodríguez, Carlos Tripiana, Oscar Hernandez, Félix Ramos, Cristian Morales, Francisco González, Ricard Zarco, Helena Gómez, Pablo Ródenas, Gaurav Saxena y Maicon Faria.

    Objectives: The objective of this course is to present to potential users the new configuration of MareNostrum and a introduction on how to use the new system (batch system, compilers, hardware, MPI, etc).Also It will provide an introduction about RES and PRACE infrastructures and how to get access to the supercomputing resources available.

    Learning Outcomes: The students who finish this course will know the internal architecture of the new MareNostrum, how it works, the ways to get access to this infrastructure and also some information about optimization techniques for its architecture.

    Level: INTERMEDIATE -for trainees with some theoretical and practical knowledge; those who finished the beginners course.

    Prerequisites:  Any potential user of a HPC infrastructure will be welcome

    Agenda:


    DAY 1 (Feb. 26) 09:00 - 17:00                                 

    Session 1 / 09:00 – 13:00 (2:45 h lectures, 0:45h practical)                                       

    9:00. - 9:30 Introduction to BSC, PRACE PATC and this training (David Vicente)

    9:30 - 10:30 MareNostrum 4 – the view from System administration group (Javier Bartolomé)

    10:30 - 11:00 COFFEE BREAK      

    11:00 - 11:45 How to use MN4 – Basics: Batch system, file systems, compilers, modules, DT, BSC commands     (Félix Ramos, Francisco González, Ricard Zarco, Helena Gómez)

    11:45 - 12:30 Hands-on I (Félix Ramos, Francisco González, Ricard Zarco, Helena Gómez)

    12:30 - 13:00 Deep Learning and Big data tools on MN4  (Carlos Tripiana)

    13:00 - 14:15 LUNCH (not hosted)          

    Session 2 / 14:15 – 17:00 (2:15h)                                         

    14:15 - 15:15 How to use MN4 – Parallel programming: OpenMP, Hands-on II (Jorge Rodríguez, Maicon Saul Faria)

    15:15 - 16:00 How to use MN4 – Parallel programming: MPI (Pablo Ródenas, Gaurav Saxena)

    16:00 - 16:30 COFFEE BREAK      

    16:30 - 17:00 How to use MN4 – Parallel programming: MPI Hands-on III (Pablo Ródenas, Gaurav Saxena)

                                              

    DAY 2 (Feb. 27) 09:00 - 13:00                                 

    Session 3 / 09:00 – 13:00 (2:00h lectures, 1:30 h practical)                                       

    9:00 - 9:30 How can I get resources from you? - RES (David Vicente)

    9:30 - 10:00 How can I get Resources from you? – PRACE (Cristian Morales)

    10:00 - 10:30 HPC Architectures (David Vicente)

    10:30 - 11:00 COFFEE BREAK      

    11:00 - 12:00 Containers on HPC (Óscar Hernández)

    12:00 - 13:00 Debugging on MareNostrum, from GDB to DDT (Óscar Hernández, Cristian Morales)


    END of COURSE
    events.prace-ri.eu/event/943/
    Feb 26 9:00 to Feb 27 13:00
    Application deadline:

    January 24th, 2020

    Description:

    Heterogeneous architectures with nodes featuring accelerator cards or sockets are taking an important share in the HPC market, given their superiority in term of flop/watt with respect to CISC and RISC architecture.
    To be effective on heterogeneous architecture applications usually requires important refactoring and adaptation, and many programming paradigms are available, some vendor specific and some other defined by an open standard,
    but without a clear winner yet (e.g. as it is the case for message passing communications where there is MPI, available for all network technologies).

    This school focus on software development techniques to address the implementation of new HPC applications and the re-factory of existing ones, in the era of heterogeneous, energy efficient, massively parallel architectures,
    toward exascale, with theoretical lectures and hands-on sessions on the different most promising programming techniques and paradigms for accelerated computing.

    Software engineering techniques and high productivity languages will complement lectures on parallel programming and porting toward new architectures, to allow the implementation of application that can be maintained across a complex and fast evolving HPC architectures.
     

    Topics:


    Heterogeneous architectures
    Elements of software engineering
    Parallel programming techniques for accelerated computing, including CUDA, OpenMP, OpenACC, SYCL
    Parallel programming techniques for massively parallel applications
    Models for applications integrating MPI, OpenMP OpenACC, CUDA and CUDA Fortran paradigms


    Target audience:

    The school is aimed at PRACE users, final year master students, PhD students, and young researchers in computational sciences and engineering, with different backgrounds, interested in applying the emerging technologies on high performance computing to their research.

    Pre-requisites:

    Good knowledge of parallel programming with MPI and/or OpenMP, knowledge of FORTRAN and C languages. Basic knowledge of parallel computer architectures.

    Admitted students:

    Attendance is free.

    A grant of 300 EUR (for students working abroad) and 150 EUR (for students working in Italy) will be available for participants not funded by their institution and not working or living in the Bologna area. Documentation will be required. Lunches for the 5 days will be provided by Cineca. Each student will be given a two month access to the Cineca's supercomputing resources.

    The number of participants is limited to 25 students.
    Applicants will be selected according to their experience, qualifications and scientific interest BASED ON WHAT WRITTEN IN THE REGISTRATION FORM.

    DUE TO PRIVACY REASON THE STUDENTS ADMITTED AND NOT ADMITTED WILL BE CONTACTED VIA EMAIL ON JANUARY, FRIDAY 31st. IF YOU SUBMITTED AND DON'T RECEIVE THE EMAIL, PLEASE WRITE AT corsi.hpc@cineca.it.  

    Acknowledgement:

    The support of CINI for the software engineering module is gratefully acknowledged.

     
    events.prace-ri.eu/event/976/
    Feb 24 9:00 to Feb 28 18:00
    29
     
     

     


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