Upcoming PATC and PTC Courses

Upcoming PATC events

Upcoming PTC events


Upcoming PATC events

 
February 2019
Mon Tue Wed Thu Fri Sat Sun
 
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2
 
3
 
Introduction to Biomolecular modelling and Molecular dynamics in HPC

(Classical and Quantum)

4 - 5 February 2019

Purpose of the course

The purpose of this course is to present to existing and potential users of Molecular Dynamics packages the method, the necessary steps for a successful simulation, common practices, common mistakes. The steps for a complete simulation workflow i.e. system setup up to final properties evaluation will be presented using popular software packages.

Outcomes

After the course the participants should be able to efficiently use their prefered MD application (i.e. NAMD, GROMACS, LAMMPS, CP2K), for molecular modelling and molecular dynamics simulations,  how to create configuration files based on their needs, tuning the models, how to efficiently use the resources based on the simulation details, avoid common mistakes.

Prerequisites

Background in Physics/Chemistry/Biology. Programming skills, aware of Parallel environments. 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

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.



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. Zoe Cournia (female) is a Researcher – Assistant Professor level at the Biomedical Research Foundation, Academy of Athens, where she works on anticancer drug design, design of drug delivery systems and biomolecular modeling using computational techniques. She graduated from the Chemistry Department, University of Athens in 2001 and completed her PhD at the University of Heidelberg in Germany in 2006. She then worked as a postdoctoral researcher at the Chemistry Department, Yale University, USA, on computer-aided drug design and in 2009 she became a lecturer at Yale College. She has been awarded the American Association for Cancer Research Angiogenesis Fellowship (2008), the "Woman of Innovation 2009" Award from the Connecticut Technology Council, USA, the Marie Curie Fellowship from the European Union (2010), the "Outstanding Junior Faculty Award" from the American Chemical Society (2014) and the first "Ada Lovelace Award" from the "Partnership for Advanced Computing in Europe" (2016). She is currently teaching at the Master’s program “Information Technologies in Technology and Medicine” at the Department of Informatics and Telecommunications, National University of  Athens.

Dr. Dimitris Tsalikis (male) is a Research Associate at the Department of Chemical Engineering in the University of Patras. His research focuses on the physicochemical characterization and the rheology of polymers, polymer nanocomposites, nanofluidics and formulations via atomistic and mesoscopic simulations and to this he develops novel parallel computational methodologies. He received his Diploma in Chemical Engineering from the University of Patras in 2004 and his Ph.D. (titled: “Computational study of structural relaxation and plastic deformation of glassy polymers”) from the National Technical University of Athens in 2009 under the advisement of Prof. Doros N. Theodorou. In 2011 he joined the research team of Prof. Vlasis Mavrantzas in Patras as a Research Associate. Dr. Tsalikis has a solid experience with high performance computing since 2007 being an active user of Tier1 and Tier0 HPC systems available to scientific community under the frameworks of HPC-Europa, PRACE and LinkSCEEM projects. He is currently teaching at the Master’s program “Polymer Science and Technology” at University of Patras.

 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.

Dr Aristeidis Sotiropoulos received his BSc in Computer Science in 1998 from the University of Crete, Greece and his PhD in Parallel Processing and Cluster Computing in 2004 from the National Technical University of Athens, Greece. His interests mainly focus on the fields of Large Scale Computing & Storage Systems, System Software for Scalable High Speed Interconnects for Computer Clusters and Advanced Microprocessor Architectures. He has published several scientific papers in international journals and conference proceedings. He has received the IEEE IPDPS 2001 best paper award for the paper "Minimizing Completion Time for Loop Tiling with Computation and Communication Overlapping". He has worked in several European and National R&D programs in the field of High Performance Computing, Grid Computing, Cloud Computing and Storage. In 2013, he was appointed as the Head of Operations and Financial Management Services, in charge of 15 people. Currently, he is managing EC projects at GRNET SA, the Greek NREN responsible for the provision of advanced e-infrastructure services to the Greek Academic and Research Community.

About GRNET

GRNET provides Internet connectivity, high-quality e-Infrastructures and advanced services to the Greek Educational, Academic and Research community.

Through its high-speed, high-capacity infrastructure that spans across the entire country, GRNET interconnects more than 150 institutions, including all universities and technological institutions, as well as many research institutes and the public Greek School Network.

GRNET operates the National High Performance Computing system (a Tier-1 in the European HPC ecosystem) and offers user and application support services, that provide Greek scientists with the computing infrastructure and expertise they need for their research enabling them to perform large scale simulations.

GRNET offers innovative IaaS cloud computing services to the Greek and global research & education communities: “ ~okeanos” and “okeanos global” allow users to create multi-layer virtual infrastructure and instantiate virtual computing machines, local networks to interconnect them, and a reliable storage space within seconds, with few, simple mouse clicks.

GRNET aims at contributing towards Greece’s Digital Convergence with the EU, by supporting the development and encouraging the use of e-Infrastructures and services. The right and timely planning strategies, together with the long experience and know-how of its people, guarantee the continuation and enhancement of GRNET’s successful course.

Greek Research and Technology Network – Networking Reserach and Education:

www.grnet.gr, hpc.grnet.gr
events.prace-ri.eu/event/795/
Feb 4 9:00 to Feb 5 17:00
Introduction to Biomolecular modelling and Molecular dynamics in HPC

(Classical and Quantum)

4 - 5 February 2019

Purpose of the course

The purpose of this course is to present to existing and potential users of Molecular Dynamics packages the method, the necessary steps for a successful simulation, common practices, common mistakes. The steps for a complete simulation workflow i.e. system setup up to final properties evaluation will be presented using popular software packages.

Outcomes

After the course the participants should be able to efficiently use their prefered MD application (i.e. NAMD, GROMACS, LAMMPS, CP2K), for molecular modelling and molecular dynamics simulations,  how to create configuration files based on their needs, tuning the models, how to efficiently use the resources based on the simulation details, avoid common mistakes.

Prerequisites

Background in Physics/Chemistry/Biology. Programming skills, aware of Parallel environments. 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

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.



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. Zoe Cournia (female) is a Researcher – Assistant Professor level at the Biomedical Research Foundation, Academy of Athens, where she works on anticancer drug design, design of drug delivery systems and biomolecular modeling using computational techniques. She graduated from the Chemistry Department, University of Athens in 2001 and completed her PhD at the University of Heidelberg in Germany in 2006. She then worked as a postdoctoral researcher at the Chemistry Department, Yale University, USA, on computer-aided drug design and in 2009 she became a lecturer at Yale College. She has been awarded the American Association for Cancer Research Angiogenesis Fellowship (2008), the "Woman of Innovation 2009" Award from the Connecticut Technology Council, USA, the Marie Curie Fellowship from the European Union (2010), the "Outstanding Junior Faculty Award" from the American Chemical Society (2014) and the first "Ada Lovelace Award" from the "Partnership for Advanced Computing in Europe" (2016). She is currently teaching at the Master’s program “Information Technologies in Technology and Medicine” at the Department of Informatics and Telecommunications, National University of  Athens.

Dr. Dimitris Tsalikis (male) is a Research Associate at the Department of Chemical Engineering in the University of Patras. His research focuses on the physicochemical characterization and the rheology of polymers, polymer nanocomposites, nanofluidics and formulations via atomistic and mesoscopic simulations and to this he develops novel parallel computational methodologies. He received his Diploma in Chemical Engineering from the University of Patras in 2004 and his Ph.D. (titled: “Computational study of structural relaxation and plastic deformation of glassy polymers”) from the National Technical University of Athens in 2009 under the advisement of Prof. Doros N. Theodorou. In 2011 he joined the research team of Prof. Vlasis Mavrantzas in Patras as a Research Associate. Dr. Tsalikis has a solid experience with high performance computing since 2007 being an active user of Tier1 and Tier0 HPC systems available to scientific community under the frameworks of HPC-Europa, PRACE and LinkSCEEM projects. He is currently teaching at the Master’s program “Polymer Science and Technology” at University of Patras.

 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.

Dr Aristeidis Sotiropoulos received his BSc in Computer Science in 1998 from the University of Crete, Greece and his PhD in Parallel Processing and Cluster Computing in 2004 from the National Technical University of Athens, Greece. His interests mainly focus on the fields of Large Scale Computing & Storage Systems, System Software for Scalable High Speed Interconnects for Computer Clusters and Advanced Microprocessor Architectures. He has published several scientific papers in international journals and conference proceedings. He has received the IEEE IPDPS 2001 best paper award for the paper "Minimizing Completion Time for Loop Tiling with Computation and Communication Overlapping". He has worked in several European and National R&D programs in the field of High Performance Computing, Grid Computing, Cloud Computing and Storage. In 2013, he was appointed as the Head of Operations and Financial Management Services, in charge of 15 people. Currently, he is managing EC projects at GRNET SA, the Greek NREN responsible for the provision of advanced e-infrastructure services to the Greek Academic and Research Community.

About GRNET

GRNET provides Internet connectivity, high-quality e-Infrastructures and advanced services to the Greek Educational, Academic and Research community.

Through its high-speed, high-capacity infrastructure that spans across the entire country, GRNET interconnects more than 150 institutions, including all universities and technological institutions, as well as many research institutes and the public Greek School Network.

GRNET operates the National High Performance Computing system (a Tier-1 in the European HPC ecosystem) and offers user and application support services, that provide Greek scientists with the computing infrastructure and expertise they need for their research enabling them to perform large scale simulations.

GRNET offers innovative IaaS cloud computing services to the Greek and global research & education communities: “ ~okeanos” and “okeanos global” allow users to create multi-layer virtual infrastructure and instantiate virtual computing machines, local networks to interconnect them, and a reliable storage space within seconds, with few, simple mouse clicks.

GRNET aims at contributing towards Greece’s Digital Convergence with the EU, by supporting the development and encouraging the use of e-Infrastructures and services. The right and timely planning strategies, together with the long experience and know-how of its people, guarantee the continuation and enhancement of GRNET’s successful course.

Greek Research and Technology Network – Networking Reserach and Education:

www.grnet.gr, hpc.grnet.gr
events.prace-ri.eu/event/795/
Feb 4 9:00 to Feb 5 17:00
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:  TBA

Day 1 (Feb 5)

9:30 – 13:00 Introduction to Big Data (Vassil Alexandrov)

Data Science current trends session will focus on results of the latest key studies both in Europe and the USA in the area of Data Science and will outline the major trends, findings and recommendations.

11:00 - 11:30 Coffee break

Data Science definitions and mathematical foundations introduction.

While tackling Big Data problems in many cases elementary or standard statistical approaches fail. New research methods are required to be developed to tackle such problems. Therefore, this session will focus key research methods and approaches for Data Science, ranging from theory creating and theory testing approaches to conceptual-analytical approaches and experimental ones, that are able to lead to discovering global properties on data. These will be mainly deterministic and hybrid (stochastic/deterministic) methods and algorithms.

13:00 – 14:00 Lunch Break

14:00 – 16:00 Introduction to Big Data (Vassil Alexandrov)

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

16:00 – 16:30 Coffee break

16:30 – 18:00 Harnessing the Power of Big Data and Simulation for Societal Challenges (Josep Casanovas and Isa Romanowska)

The scale of challenges our societies face nowadays calls for innovative and creative methods and solutions. Here, we present the role of simulation in modern scientific practice and its complementarity to data driven applications. Focusing on a relatively novel technique – agent-based modelling we show how by using High Performance Computing enabled methods we can simulate large scale processes and mechanisms driving human societies at different scales. The marriage between Big Data and Agent-based modelling is a particularly promising avenue of research that should be explored further to advance our social science toolset.


Day 2 (Feb 6)

9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral)

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 – 16:00 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral)

16:00 – 16:30 Coffee break

16:30 – 18:00 Big IoT Project (Dr. Ernest Teniente)


 

Day 3 (Feb 7)

9:30 – 13:00 Big Data Management (Albert Abelló and Petar Jovanovic)

: 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)

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)

 

Day 4 (Feb 8)

9:30 – 13:00 Practical Data Analytics for Solving Real World Problems (Carlos Carrasco)

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.

11:00 - 11:30 Coffee break

 

END of COURSE

 

 
events.prace-ri.eu/event/761/
Feb 5 9:30 to Feb 8 16:30
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:  TBA

Day 1 (Feb 5)

9:30 – 13:00 Introduction to Big Data (Vassil Alexandrov)

Data Science current trends session will focus on results of the latest key studies both in Europe and the USA in the area of Data Science and will outline the major trends, findings and recommendations.

11:00 - 11:30 Coffee break

Data Science definitions and mathematical foundations introduction.

While tackling Big Data problems in many cases elementary or standard statistical approaches fail. New research methods are required to be developed to tackle such problems. Therefore, this session will focus key research methods and approaches for Data Science, ranging from theory creating and theory testing approaches to conceptual-analytical approaches and experimental ones, that are able to lead to discovering global properties on data. These will be mainly deterministic and hybrid (stochastic/deterministic) methods and algorithms.

13:00 – 14:00 Lunch Break

14:00 – 16:00 Introduction to Big Data (Vassil Alexandrov)

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

16:00 – 16:30 Coffee break

16:30 – 18:00 Harnessing the Power of Big Data and Simulation for Societal Challenges (Josep Casanovas and Isa Romanowska)

The scale of challenges our societies face nowadays calls for innovative and creative methods and solutions. Here, we present the role of simulation in modern scientific practice and its complementarity to data driven applications. Focusing on a relatively novel technique – agent-based modelling we show how by using High Performance Computing enabled methods we can simulate large scale processes and mechanisms driving human societies at different scales. The marriage between Big Data and Agent-based modelling is a particularly promising avenue of research that should be explored further to advance our social science toolset.


Day 2 (Feb 6)

9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral)

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 – 16:00 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral)

16:00 – 16:30 Coffee break

16:30 – 18:00 Big IoT Project (Dr. Ernest Teniente)


 

Day 3 (Feb 7)

9:30 – 13:00 Big Data Management (Albert Abelló and Petar Jovanovic)

: 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)

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)

 

Day 4 (Feb 8)

9:30 – 13:00 Practical Data Analytics for Solving Real World Problems (Carlos Carrasco)

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.

11:00 - 11:30 Coffee break

 

END of COURSE

 

 
events.prace-ri.eu/event/761/
Feb 5 9:30 to Feb 8 16:30
Objective of the session:

In the context of scientific research, Data Management Plans (DMP) are increasingly requested by project funders to describe the data lifecycle of projects from the craddle to the grave. The main difficulty is to fully understand what is exactly required to build such a document. The aim of this training is to assist researchers in putting together their own DMP, first by describing the important concepts to know before starting to draft one, then by having practical work sessions during which their own plan will be drafted with the support of experts.


Day 1 : afternoon : Introduction, DMP + FAIR in theory, tools ;
Day 2 : morning : Long term preservation (metadata, formats, principles),  DMP hands on ; afternoon : DMP hands on ;
Day 3 : morning : EOSC-hub services, CINES visit.


Prerequisites: 
This training is intended for researchers who have a DMP to build as part of their research project. It is recommended that the participants come with an intended use case for which they need to develop a DMP. 

Learning outcomes 
Participants will understand the DMP structure and good practices, and have a first experience from a real use-case.

Trainers
Marjan Grootveld (DANS), Alexia de Casanove et Olivier Rouchon (CINES)
events.prace-ri.eu/event/813/
Feb 6 14:00 to Feb 8 12:00
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:  TBA

Day 1 (Feb 5)

9:30 – 13:00 Introduction to Big Data (Vassil Alexandrov)

Data Science current trends session will focus on results of the latest key studies both in Europe and the USA in the area of Data Science and will outline the major trends, findings and recommendations.

11:00 - 11:30 Coffee break

Data Science definitions and mathematical foundations introduction.

While tackling Big Data problems in many cases elementary or standard statistical approaches fail. New research methods are required to be developed to tackle such problems. Therefore, this session will focus key research methods and approaches for Data Science, ranging from theory creating and theory testing approaches to conceptual-analytical approaches and experimental ones, that are able to lead to discovering global properties on data. These will be mainly deterministic and hybrid (stochastic/deterministic) methods and algorithms.

13:00 – 14:00 Lunch Break

14:00 – 16:00 Introduction to Big Data (Vassil Alexandrov)

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

16:00 – 16:30 Coffee break

16:30 – 18:00 Harnessing the Power of Big Data and Simulation for Societal Challenges (Josep Casanovas and Isa Romanowska)

The scale of challenges our societies face nowadays calls for innovative and creative methods and solutions. Here, we present the role of simulation in modern scientific practice and its complementarity to data driven applications. Focusing on a relatively novel technique – agent-based modelling we show how by using High Performance Computing enabled methods we can simulate large scale processes and mechanisms driving human societies at different scales. The marriage between Big Data and Agent-based modelling is a particularly promising avenue of research that should be explored further to advance our social science toolset.


Day 2 (Feb 6)

9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral)

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 – 16:00 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral)

16:00 – 16:30 Coffee break

16:30 – 18:00 Big IoT Project (Dr. Ernest Teniente)


 

Day 3 (Feb 7)

9:30 – 13:00 Big Data Management (Albert Abelló and Petar Jovanovic)

: 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)

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)

 

Day 4 (Feb 8)

9:30 – 13:00 Practical Data Analytics for Solving Real World Problems (Carlos Carrasco)

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.

11:00 - 11:30 Coffee break

 

END of COURSE

 

 
events.prace-ri.eu/event/761/
Feb 5 9:30 to Feb 8 16:30
Objective of the session:

In the context of scientific research, Data Management Plans (DMP) are increasingly requested by project funders to describe the data lifecycle of projects from the craddle to the grave. The main difficulty is to fully understand what is exactly required to build such a document. The aim of this training is to assist researchers in putting together their own DMP, first by describing the important concepts to know before starting to draft one, then by having practical work sessions during which their own plan will be drafted with the support of experts.


Day 1 : afternoon : Introduction, DMP + FAIR in theory, tools ;
Day 2 : morning : Long term preservation (metadata, formats, principles),  DMP hands on ; afternoon : DMP hands on ;
Day 3 : morning : EOSC-hub services, CINES visit.


Prerequisites: 
This training is intended for researchers who have a DMP to build as part of their research project. It is recommended that the participants come with an intended use case for which they need to develop a DMP. 

Learning outcomes 
Participants will understand the DMP structure and good practices, and have a first experience from a real use-case.

Trainers
Marjan Grootveld (DANS), Alexia de Casanove et Olivier Rouchon (CINES)
events.prace-ri.eu/event/813/
Feb 6 14:00 to Feb 8 12:00
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:  TBA

Day 1 (Feb 5)

9:30 – 13:00 Introduction to Big Data (Vassil Alexandrov)

Data Science current trends session will focus on results of the latest key studies both in Europe and the USA in the area of Data Science and will outline the major trends, findings and recommendations.

11:00 - 11:30 Coffee break

Data Science definitions and mathematical foundations introduction.

While tackling Big Data problems in many cases elementary or standard statistical approaches fail. New research methods are required to be developed to tackle such problems. Therefore, this session will focus key research methods and approaches for Data Science, ranging from theory creating and theory testing approaches to conceptual-analytical approaches and experimental ones, that are able to lead to discovering global properties on data. These will be mainly deterministic and hybrid (stochastic/deterministic) methods and algorithms.

13:00 – 14:00 Lunch Break

14:00 – 16:00 Introduction to Big Data (Vassil Alexandrov)

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

16:00 – 16:30 Coffee break

16:30 – 18:00 Harnessing the Power of Big Data and Simulation for Societal Challenges (Josep Casanovas and Isa Romanowska)

The scale of challenges our societies face nowadays calls for innovative and creative methods and solutions. Here, we present the role of simulation in modern scientific practice and its complementarity to data driven applications. Focusing on a relatively novel technique – agent-based modelling we show how by using High Performance Computing enabled methods we can simulate large scale processes and mechanisms driving human societies at different scales. The marriage between Big Data and Agent-based modelling is a particularly promising avenue of research that should be explored further to advance our social science toolset.


Day 2 (Feb 6)

9:30 – 13:00 Data Analytics with Apache Spark (Josep Lluis Berral)

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 – 16:00 Data Analytics with Apache Spark. Part 2 (Josep Lluis Berral)

16:00 – 16:30 Coffee break

16:30 – 18:00 Big IoT Project (Dr. Ernest Teniente)


 

Day 3 (Feb 7)

9:30 – 13:00 Big Data Management (Albert Abelló and Petar Jovanovic)

: 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)

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)

 

Day 4 (Feb 8)

9:30 – 13:00 Practical Data Analytics for Solving Real World Problems (Carlos Carrasco)

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.

11:00 - 11:30 Coffee break

 

END of COURSE

 

 
events.prace-ri.eu/event/761/
Feb 5 9:30 to Feb 8 16:30
9
 
10
 
Application deadline:

January 16th, 2019

Description:

In the roadmap toward next-generation supercomputers it is evident that heterogeneous architectures are taking an important share in the HPC market, and the consolidation of this kind of architectures requires an important effort in software development and applications refactoring. Moreover, in recent years Arm has started to emerge as a viable architecture for large scale HPC deployments, and more than one exascale projects being develop in the world are based on Arm architectures.

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.

Two days of the school will be reserved for a workshop that will provide an introduction to, and hands on experience working with, the Arm HPC architecture and the accompanying ecosystem. It will be discussed the specifics of the Armv8 architecture, and their implications, and introduce some of the current HPC processors developed on this architecture. For the hands on exercises we will provide tutorials, to work through an introduction to the compiler and maths libraries, as well as the performance tools to be tested on the Arm cluster installed at Cineca. As a more advanced topic for the workshop we will introduce the upcoming vectorisation exstension SVE, and provide a guided tutorial on how to use the Arm instruction emulator to execute next generation code.

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
Arm HPC architecture
Elements of software engineering
Parallel programming techniques for throughput CPUs  (Nvidia and Intel)
Parallel programming techniques for massively parallel applications
Introduction to Python for high performance computing
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 250 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 18TH. 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/836/
Feb 11 9:00 to Feb 15 17:00
Application deadline:

January 16th, 2019

Description:

In the roadmap toward next-generation supercomputers it is evident that heterogeneous architectures are taking an important share in the HPC market, and the consolidation of this kind of architectures requires an important effort in software development and applications refactoring. Moreover, in recent years Arm has started to emerge as a viable architecture for large scale HPC deployments, and more than one exascale projects being develop in the world are based on Arm architectures.

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.

Two days of the school will be reserved for a workshop that will provide an introduction to, and hands on experience working with, the Arm HPC architecture and the accompanying ecosystem. It will be discussed the specifics of the Armv8 architecture, and their implications, and introduce some of the current HPC processors developed on this architecture. For the hands on exercises we will provide tutorials, to work through an introduction to the compiler and maths libraries, as well as the performance tools to be tested on the Arm cluster installed at Cineca. As a more advanced topic for the workshop we will introduce the upcoming vectorisation exstension SVE, and provide a guided tutorial on how to use the Arm instruction emulator to execute next generation code.

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
Arm HPC architecture
Elements of software engineering
Parallel programming techniques for throughput CPUs  (Nvidia and Intel)
Parallel programming techniques for massively parallel applications
Introduction to Python for high performance computing
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 250 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 18TH. 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/836/
Feb 11 9:00 to Feb 15 17:00
Application deadline:

January 16th, 2019

Description:

In the roadmap toward next-generation supercomputers it is evident that heterogeneous architectures are taking an important share in the HPC market, and the consolidation of this kind of architectures requires an important effort in software development and applications refactoring. Moreover, in recent years Arm has started to emerge as a viable architecture for large scale HPC deployments, and more than one exascale projects being develop in the world are based on Arm architectures.

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.

Two days of the school will be reserved for a workshop that will provide an introduction to, and hands on experience working with, the Arm HPC architecture and the accompanying ecosystem. It will be discussed the specifics of the Armv8 architecture, and their implications, and introduce some of the current HPC processors developed on this architecture. For the hands on exercises we will provide tutorials, to work through an introduction to the compiler and maths libraries, as well as the performance tools to be tested on the Arm cluster installed at Cineca. As a more advanced topic for the workshop we will introduce the upcoming vectorisation exstension SVE, and provide a guided tutorial on how to use the Arm instruction emulator to execute next generation code.

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
Arm HPC architecture
Elements of software engineering
Parallel programming techniques for throughput CPUs  (Nvidia and Intel)
Parallel programming techniques for massively parallel applications
Introduction to Python for high performance computing
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 250 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 18TH. 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/836/
Feb 11 9:00 to Feb 15 17:00
Description

This course gives a practical introduction to deep learning, convolutional and recurrent neural networks, GPU computing, and tools to train and apply deep neural networks for natural language processing, images, and other applications.

The course consists of lectures and hands-on exercises. Keras (keras.io/) and PyTorch (pytorch.org/) will be used in the exercise sessions. CSC's Notebooks (notebooks.csc.fi/) environment will be used on the first day of the course, and the Taito-GPU (research.csc.fi/taito-gpu) cluster on the second day.

Learning outcome

After the course the participants should have the skills and knowledge needed to begin applying deep learning for different tasks and utilizing the GPU resources available at CSC for training and deploying their own neural networks.

Prerequisites

The participants are assumed to have working knowledge of Python and suitable background in data analysis, machine learning, or a related field. Previous experience in deep learning is not required, but the fundamentals of machine learning are not covered on this course.  Basic knowledge of a Linux/Unix environment will be assumed.

Agenda (tentative)

Day 1, Wednesday 13.2



   09.00 – 10.30 Lecture: Introduction to deep learning


   10.30 – 11.00 Exercises: Introduction to Notebooks, Keras fundamentals


   11.00 – 12.00 Lecture: Image data, multi-layer percepton networks, convolutional neural networks


   12.00 – 13.00 Lunch


   13.00 – 14.00 Exercises: Image classification with MLPs, CNNs


   14.00 – 15.00 Lecture: Text data, embeddings, neural NLP, recurrent neural networks


   15.00 – 16.00 Exercises: Text sentiment classification with CNNs, RNNs



Day 2, Thursday 14.2



   09.00 – 10.00 Lecture: GPUs, batch jobs, using Taito-GPU


   10.00 – 12.00 Exercises: Image classification


   12.00 – 13.00 Lunch


   13.00 – 14.00 Exercises: Text categorization and labelling


   14.00 – 15.00 Lecture: Cloud, GPU utilization, multiple GPUs


   15.00 – 16.00 Exercises: Using multiple GPUs



Coffee will be served both for the morning and afternoon sessions

Lecturers: 

Markus Koskela (CSC),  Mats Sjöberg (CSC)

 

Language:  English
Price:          Free of charge
events.prace-ri.eu/event/831/
Feb 13 8:00 to Feb 14 15:00
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: Mariano Vazquez and Ruth Aris

Lecturers: Mariano Vázquez (BSC), Marco Verdicchio (SURFsara), Okba Hamitou (Bull), Gábor Závodszky (UvA), João Damas (ACELLERA), Adrià Perez (UPF), Phil Tooley (USFD), Ricard Borrell (BSC), Jazmín Aguado-Sierra (BSC), Dr Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL), Guillermo Marín (BSC) and Paul Melis (SURFsara)

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

Agenda:



Day 1 (Feb. 13)

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

9:00-9:15 Welcome (Mariano Vázquez, BSC)

9:15-11:00 Introduction to HPC in Computational Modelling (Marco Verdicchio, SURFsara)

11:00-13:00 Compilation and Optimization in the HPC environment (Okba Hamitou, Bull)

13:00-14:00 Lunch Break

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

14:00-16:00 Zoom in on blood - Using supercomputers in hemodynamics (UvA) (Gábor Závodszky, UvA)

16:00-18:00 PlayMolecule: Cloud-based Applications for Molecular Discovery (João Damas, ACELLERA, Adrià Perez, UPF)

 

Day 2 (Feb. 14)

Session 3 / 9:00am – 1:00 pm (4 h lectures)

9:00-11:00 Hands on Image Registration with pFIRE (Phil Tooley, USFD)

11:00-12:00 Parallel algorithms for Computational Mechanics (Ricard Borrell, BSC)

12:00-13:00 HPC Multi-scale computational modelling using Alya Red (Jazmín Aguado-Sierra, BSC)

13:00-14:00 Lunch Break

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

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)

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

 

Day 3 (Feb. 15)

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

9:00-10:00 Data Visulization for Researchers Crash Course (Guillermo Marín, BSC)

10:00-12:00 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

12:00-13:00 Molecular Medicine (UCL)

13:00-14:00 Lunch Break

Session 6 / 2:00pm – 4:00 pm

14:00-16:00 Visit to MareNostrum



 

END of COURSE

 

END of COURSE
events.prace-ri.eu/event/762/
Feb 13 9:00 to Feb 15 18:00
Application deadline:

January 16th, 2019

Description:

In the roadmap toward next-generation supercomputers it is evident that heterogeneous architectures are taking an important share in the HPC market, and the consolidation of this kind of architectures requires an important effort in software development and applications refactoring. Moreover, in recent years Arm has started to emerge as a viable architecture for large scale HPC deployments, and more than one exascale projects being develop in the world are based on Arm architectures.

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.

Two days of the school will be reserved for a workshop that will provide an introduction to, and hands on experience working with, the Arm HPC architecture and the accompanying ecosystem. It will be discussed the specifics of the Armv8 architecture, and their implications, and introduce some of the current HPC processors developed on this architecture. For the hands on exercises we will provide tutorials, to work through an introduction to the compiler and maths libraries, as well as the performance tools to be tested on the Arm cluster installed at Cineca. As a more advanced topic for the workshop we will introduce the upcoming vectorisation exstension SVE, and provide a guided tutorial on how to use the Arm instruction emulator to execute next generation code.

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
Arm HPC architecture
Elements of software engineering
Parallel programming techniques for throughput CPUs  (Nvidia and Intel)
Parallel programming techniques for massively parallel applications
Introduction to Python for high performance computing
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 250 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 18TH. 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/836/
Feb 11 9:00 to Feb 15 17:00
Description

This course gives a practical introduction to deep learning, convolutional and recurrent neural networks, GPU computing, and tools to train and apply deep neural networks for natural language processing, images, and other applications.

The course consists of lectures and hands-on exercises. Keras (keras.io/) and PyTorch (pytorch.org/) will be used in the exercise sessions. CSC's Notebooks (notebooks.csc.fi/) environment will be used on the first day of the course, and the Taito-GPU (research.csc.fi/taito-gpu) cluster on the second day.

Learning outcome

After the course the participants should have the skills and knowledge needed to begin applying deep learning for different tasks and utilizing the GPU resources available at CSC for training and deploying their own neural networks.

Prerequisites

The participants are assumed to have working knowledge of Python and suitable background in data analysis, machine learning, or a related field. Previous experience in deep learning is not required, but the fundamentals of machine learning are not covered on this course.  Basic knowledge of a Linux/Unix environment will be assumed.

Agenda (tentative)

Day 1, Wednesday 13.2



   09.00 – 10.30 Lecture: Introduction to deep learning


   10.30 – 11.00 Exercises: Introduction to Notebooks, Keras fundamentals


   11.00 – 12.00 Lecture: Image data, multi-layer percepton networks, convolutional neural networks


   12.00 – 13.00 Lunch


   13.00 – 14.00 Exercises: Image classification with MLPs, CNNs


   14.00 – 15.00 Lecture: Text data, embeddings, neural NLP, recurrent neural networks


   15.00 – 16.00 Exercises: Text sentiment classification with CNNs, RNNs



Day 2, Thursday 14.2



   09.00 – 10.00 Lecture: GPUs, batch jobs, using Taito-GPU


   10.00 – 12.00 Exercises: Image classification


   12.00 – 13.00 Lunch


   13.00 – 14.00 Exercises: Text categorization and labelling


   14.00 – 15.00 Lecture: Cloud, GPU utilization, multiple GPUs


   15.00 – 16.00 Exercises: Using multiple GPUs



Coffee will be served both for the morning and afternoon sessions

Lecturers: 

Markus Koskela (CSC),  Mats Sjöberg (CSC)

 

Language:  English
Price:          Free of charge
events.prace-ri.eu/event/831/
Feb 13 8:00 to Feb 14 15:00
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: Mariano Vazquez and Ruth Aris

Lecturers: Mariano Vázquez (BSC), Marco Verdicchio (SURFsara), Okba Hamitou (Bull), Gábor Závodszky (UvA), João Damas (ACELLERA), Adrià Perez (UPF), Phil Tooley (USFD), Ricard Borrell (BSC), Jazmín Aguado-Sierra (BSC), Dr Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL), Guillermo Marín (BSC) and Paul Melis (SURFsara)

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

Agenda:



Day 1 (Feb. 13)

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

9:00-9:15 Welcome (Mariano Vázquez, BSC)

9:15-11:00 Introduction to HPC in Computational Modelling (Marco Verdicchio, SURFsara)

11:00-13:00 Compilation and Optimization in the HPC environment (Okba Hamitou, Bull)

13:00-14:00 Lunch Break

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

14:00-16:00 Zoom in on blood - Using supercomputers in hemodynamics (UvA) (Gábor Závodszky, UvA)

16:00-18:00 PlayMolecule: Cloud-based Applications for Molecular Discovery (João Damas, ACELLERA, Adrià Perez, UPF)

 

Day 2 (Feb. 14)

Session 3 / 9:00am – 1:00 pm (4 h lectures)

9:00-11:00 Hands on Image Registration with pFIRE (Phil Tooley, USFD)

11:00-12:00 Parallel algorithms for Computational Mechanics (Ricard Borrell, BSC)

12:00-13:00 HPC Multi-scale computational modelling using Alya Red (Jazmín Aguado-Sierra, BSC)

13:00-14:00 Lunch Break

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

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)

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

 

Day 3 (Feb. 15)

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

9:00-10:00 Data Visulization for Researchers Crash Course (Guillermo Marín, BSC)

10:00-12:00 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

12:00-13:00 Molecular Medicine (UCL)

13:00-14:00 Lunch Break

Session 6 / 2:00pm – 4:00 pm

14:00-16:00 Visit to MareNostrum



 

END of COURSE

 

END of COURSE
events.prace-ri.eu/event/762/
Feb 13 9:00 to Feb 15 18:00
Application deadline:

January 16th, 2019

Description:

In the roadmap toward next-generation supercomputers it is evident that heterogeneous architectures are taking an important share in the HPC market, and the consolidation of this kind of architectures requires an important effort in software development and applications refactoring. Moreover, in recent years Arm has started to emerge as a viable architecture for large scale HPC deployments, and more than one exascale projects being develop in the world are based on Arm architectures.

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.

Two days of the school will be reserved for a workshop that will provide an introduction to, and hands on experience working with, the Arm HPC architecture and the accompanying ecosystem. It will be discussed the specifics of the Armv8 architecture, and their implications, and introduce some of the current HPC processors developed on this architecture. For the hands on exercises we will provide tutorials, to work through an introduction to the compiler and maths libraries, as well as the performance tools to be tested on the Arm cluster installed at Cineca. As a more advanced topic for the workshop we will introduce the upcoming vectorisation exstension SVE, and provide a guided tutorial on how to use the Arm instruction emulator to execute next generation code.

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
Arm HPC architecture
Elements of software engineering
Parallel programming techniques for throughput CPUs  (Nvidia and Intel)
Parallel programming techniques for massively parallel applications
Introduction to Python for high performance computing
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 250 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 18TH. 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/836/
Feb 11 9:00 to Feb 15 17:00
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: Mariano Vazquez and Ruth Aris

Lecturers: Mariano Vázquez (BSC), Marco Verdicchio (SURFsara), Okba Hamitou (Bull), Gábor Závodszky (UvA), João Damas (ACELLERA), Adrià Perez (UPF), Phil Tooley (USFD), Ricard Borrell (BSC), Jazmín Aguado-Sierra (BSC), Dr Alexander Heifetz (EVOTEC), Andrea Townsend-Nicholson (UCL), Guillermo Marín (BSC) and Paul Melis (SURFsara)

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

Agenda:



Day 1 (Feb. 13)

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

9:00-9:15 Welcome (Mariano Vázquez, BSC)

9:15-11:00 Introduction to HPC in Computational Modelling (Marco Verdicchio, SURFsara)

11:00-13:00 Compilation and Optimization in the HPC environment (Okba Hamitou, Bull)

13:00-14:00 Lunch Break

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

14:00-16:00 Zoom in on blood - Using supercomputers in hemodynamics (UvA) (Gábor Závodszky, UvA)

16:00-18:00 PlayMolecule: Cloud-based Applications for Molecular Discovery (João Damas, ACELLERA, Adrià Perez, UPF)

 

Day 2 (Feb. 14)

Session 3 / 9:00am – 1:00 pm (4 h lectures)

9:00-11:00 Hands on Image Registration with pFIRE (Phil Tooley, USFD)

11:00-12:00 Parallel algorithms for Computational Mechanics (Ricard Borrell, BSC)

12:00-13:00 HPC Multi-scale computational modelling using Alya Red (Jazmín Aguado-Sierra, BSC)

13:00-14:00 Lunch Break

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

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)

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

 

Day 3 (Feb. 15)

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

9:00-10:00 Data Visulization for Researchers Crash Course (Guillermo Marín, BSC)

10:00-12:00 Visualization applied to 2D/3D scientific datasets (Paul Melis, SURFsara)

12:00-13:00 Molecular Medicine (UCL)

13:00-14:00 Lunch Break

Session 6 / 2:00pm – 4:00 pm

14:00-16:00 Visit to MareNostrum



 

END of COURSE

 

END of COURSE
events.prace-ri.eu/event/762/
Feb 13 9:00 to Feb 15 18:00
16
 
17
 
 

Description:
This course is designed for those users who wish run classical molecular dynamics programs such as GROMACS and NAMD on modern supercomputers. By understanding better the HPC infrastructures and the algorithms used to exploit them, the aim is to give researchers the tools to run simulations in the most efficient way possible on current and future supercomputers.
The course will consist of presentations and practical sessions where students will be able to prepare and run examples of popular programs such as GROMACS and NAMD on the supercomputers of Cineca.

Skills:
By the end of the course each student should be able to:


comprehend the basic principles of classical molecular dynamics (MD).
understand the common algorithms for the optimization and parallelization of MD applications and the factors limiting the performance and parallel scaling.
run and optimize MD simulations on advanced, multicore architectures equipped with both conventional processors and accelerators such as NVIDIA GPUs.
design a simulation project for a computing resource application.
Target Audience:
Scientists with research interests in classical molecular dynamics in computational biology, chemistry or biophysics.


Pre-requisites:
Research interest in classical molecular dynamics with a focus on the simulation of biomolecular systems. Basic knowledge of UNIX and concepts of parallel computing.


Grant:

A grant of 300 EUR (for foreign students) and 150 EUR (for Italian students) will be available for participants not funded by their institution and not working in the Bologna area.
Some documentation will be required and the grant will be paid only after a certified presence of minimum 80% of the lessons and about 1 month after the ending of the course.
For further information about how to submit for the grant, please wait the confirmation email that you are accepted to the course about 3 weeks before the date of the beginning of the lessons. 

The lunch for the 3 days will be provided by Cineca.

Coordinating Teacher: Dr. Andrew Emerson
events.prace-ri.eu/event/829/
Feb 18 9:00 to Feb 20 18:00
 

Description:
This course is designed for those users who wish run classical molecular dynamics programs such as GROMACS and NAMD on modern supercomputers. By understanding better the HPC infrastructures and the algorithms used to exploit them, the aim is to give researchers the tools to run simulations in the most efficient way possible on current and future supercomputers.
The course will consist of presentations and practical sessions where students will be able to prepare and run examples of popular programs such as GROMACS and NAMD on the supercomputers of Cineca.

Skills:
By the end of the course each student should be able to:


comprehend the basic principles of classical molecular dynamics (MD).
understand the common algorithms for the optimization and parallelization of MD applications and the factors limiting the performance and parallel scaling.
run and optimize MD simulations on advanced, multicore architectures equipped with both conventional processors and accelerators such as NVIDIA GPUs.
design a simulation project for a computing resource application.
Target Audience:
Scientists with research interests in classical molecular dynamics in computational biology, chemistry or biophysics.


Pre-requisites:
Research interest in classical molecular dynamics with a focus on the simulation of biomolecular systems. Basic knowledge of UNIX and concepts of parallel computing.


Grant:

A grant of 300 EUR (for foreign students) and 150 EUR (for Italian students) will be available for participants not funded by their institution and not working in the Bologna area.
Some documentation will be required and the grant will be paid only after a certified presence of minimum 80% of the lessons and about 1 month after the ending of the course.
For further information about how to submit for the grant, please wait the confirmation email that you are accepted to the course about 3 weeks before the date of the beginning of the lessons. 

The lunch for the 3 days will be provided by Cineca.

Coordinating Teacher: Dr. Andrew Emerson
events.prace-ri.eu/event/829/
Feb 18 9:00 to Feb 20 18:00
 

Description:
This course is designed for those users who wish run classical molecular dynamics programs such as GROMACS and NAMD on modern supercomputers. By understanding better the HPC infrastructures and the algorithms used to exploit them, the aim is to give researchers the tools to run simulations in the most efficient way possible on current and future supercomputers.
The course will consist of presentations and practical sessions where students will be able to prepare and run examples of popular programs such as GROMACS and NAMD on the supercomputers of Cineca.

Skills:
By the end of the course each student should be able to:


comprehend the basic principles of classical molecular dynamics (MD).
understand the common algorithms for the optimization and parallelization of MD applications and the factors limiting the performance and parallel scaling.
run and optimize MD simulations on advanced, multicore architectures equipped with both conventional processors and accelerators such as NVIDIA GPUs.
design a simulation project for a computing resource application.
Target Audience:
Scientists with research interests in classical molecular dynamics in computational biology, chemistry or biophysics.


Pre-requisites:
Research interest in classical molecular dynamics with a focus on the simulation of biomolecular systems. Basic knowledge of UNIX and concepts of parallel computing.


Grant:

A grant of 300 EUR (for foreign students) and 150 EUR (for Italian students) will be available for participants not funded by their institution and not working in the Bologna area.
Some documentation will be required and the grant will be paid only after a certified presence of minimum 80% of the lessons and about 1 month after the ending of the course.
For further information about how to submit for the grant, please wait the confirmation email that you are accepted to the course about 3 weeks before the date of the beginning of the lessons. 

The lunch for the 3 days will be provided by Cineca.

Coordinating Teacher: Dr. Andrew Emerson
events.prace-ri.eu/event/829/
Feb 18 9:00 to Feb 20 18:00
This course teaches performance engineering approaches on the compute node level. "Performance engineering" as we define it is more than employing tools to identify hotspots and bottlenecks. It is about developing a thorough understanding of the interactions between software and hardware. This process must start at the core, socket, and node level, where the code gets executed that does the actual computational work. Once the architectural requirements of a code are understood and correlated with performance measurements, the potential benefit of optimizations can often be predicted. We introduce a "holistic" node-level performance engineering strategy and apply it to different algorithms from computational science. Architectural details that are relevant for performance, such as pipelining, SIMD, superscalarity, memory hierarchies, etc., are covered in due detail.

The course is a PRACE training event.


Introduction

Our approach to performance engineering
Basic architecture of multicore systems: threads, cores, caches, sockets, memory
The important role of system topology


Tools: topology & affinity in multicore environments

Overview
likwid-topology and likwid-pin


Microbenchmarking for architectural exploration

Properties of data paths in the memory hierarchy
Bottlenecks
OpenMP barrier overhead


Roofline model: basics

Model assumptions and construction
Simple examples
Limitations of the Roofline model


Pattern-based performance engineering
Optimal use of parallel resources

Single Instruction Multiple Data (SIMD)
Cache-coherent Non-Uniform Memory Architecture (ccNUMA)
Simultaneous Multi-Threading (SMT)


Tools: hardware performance counters

Why hardware performance counters?
likwid-perfctr
Validating performance models


Roofline case studies

Dense matrix-vector multiplication
Sparse matrix-vector multiplication
Jacobi (stencil) smoother


Optional: The ECM performance model

events.prace-ri.eu/event/821/
Feb 20 9:00 to Feb 21 17:00
This course teaches performance engineering approaches on the compute node level. "Performance engineering" as we define it is more than employing tools to identify hotspots and bottlenecks. It is about developing a thorough understanding of the interactions between software and hardware. This process must start at the core, socket, and node level, where the code gets executed that does the actual computational work. Once the architectural requirements of a code are understood and correlated with performance measurements, the potential benefit of optimizations can often be predicted. We introduce a "holistic" node-level performance engineering strategy and apply it to different algorithms from computational science. Architectural details that are relevant for performance, such as pipelining, SIMD, superscalarity, memory hierarchies, etc., are covered in due detail.

The course is a PRACE training event.


Introduction

Our approach to performance engineering
Basic architecture of multicore systems: threads, cores, caches, sockets, memory
The important role of system topology


Tools: topology & affinity in multicore environments

Overview
likwid-topology and likwid-pin


Microbenchmarking for architectural exploration

Properties of data paths in the memory hierarchy
Bottlenecks
OpenMP barrier overhead


Roofline model: basics

Model assumptions and construction
Simple examples
Limitations of the Roofline model


Pattern-based performance engineering
Optimal use of parallel resources

Single Instruction Multiple Data (SIMD)
Cache-coherent Non-Uniform Memory Architecture (ccNUMA)
Simultaneous Multi-Threading (SMT)


Tools: hardware performance counters

Why hardware performance counters?
likwid-perfctr
Validating performance models


Roofline case studies

Dense matrix-vector multiplication
Sparse matrix-vector multiplication
Jacobi (stencil) smoother


Optional: The ECM performance model

events.prace-ri.eu/event/821/
Feb 20 9:00 to Feb 21 17:00
Description:
With the steaming out of Moore's law and the end of Dennard's scaling the pace dictated on the performance increase of High Performance Computing Systems among generations has led to power constrained architectures and systems. In addition power consumption represents a significant cost factor in the overall HPC system economy. For this reasons in the recent years researchers, supercomputing centers and major vendors have developed new tools and methodologies to measure and optimize the energy consumption of large scale high performance system installation. Due to the link between energy consumption, power consumption and execution time of the application executed by the final user it is important for these tools and methodology to consider all these aspects empowering the final user and the system administrator with the capability of finding the best configuration given different high level objectives. 

The school will give an introductory course on the fundamental concept of power consumption and energy efficiency in HPC systems. Then it will focuses on the mechanisms that today computing elements and systems provide in terms of monitoring and control the power and energy dissipation. Finally it will introduce and give hand ons on a set of tools for reducing the energy consumption in HPC devices.  The school is organized in four main sessions driving the audience from the physical and engineering principles underlying power consumption in supercomputing systems to the practical usage of state-of-the-art tools for monitoring and controlling the energy efficiency of supercomputing machines and workloads. The tools that will be covered are the MSR-SAFE (LLNL), MERIC (IT4I), COUNTDOWN (UNIBO), GEOPM (Intel) libraries. 

Skills:
By the end of the course, participants will be expected to:


have a good understanding of the principles underlying the power consumption, energy dissipation in high performance computing nodes
recognize trade-offs and implications of changing the power consumption in scientific computing systems during the execution of scientific computing applications
have a clear view on the state-of-the-art and of practice in controlling the power consumption and energy efficiency of supercomputing nodes and processors.
learn the internals and the usage of a set of user-space run-time libraries for controlling/optimizing the power consumption and energy efficiency in x86 computing nodes while executing user's applications.
learn how to use these tools for optimize the energy consumption of your codes.


Target Audience:
Researchers, student, system administrator, application developers who may wish to limit the environmental impact of their computations, as well as reducing the cost of energy provisioning in supercomputing system - promoting a more sustainable supercomputing ecosystem.

Pre-requisites:

Knowledge of Fortran or C/C++. Elementary notions of Linux/Unix.
Knowledge of computing architecture
Experience in running HPC applications or systems Basic knowledge of parallel programming OpenMP and/or MPI 

 

Grant

The course is FREE of charge.
The lunch for the two days will be offered to all the participants and some grants are available. The only requirement to be eligible is to be not funded by your institution to attend the course and to work and live in an institute outside the Bologna area. The grant  will be 200 euros for students working and living outside Italy and 100 euros for students working and living in Italy (outside Bologna area). Some documentation will be required and the grant will be paid only after a certified presence of minimum 80% of the lectures.

Further information about how to request the grant, will be provided at the confirmation of the course: about 3 weeks before the starting date.
events.prace-ri.eu/event/833/
Feb 21 9:00 to Feb 22 17:00
Description:
With the steaming out of Moore's law and the end of Dennard's scaling the pace dictated on the performance increase of High Performance Computing Systems among generations has led to power constrained architectures and systems. In addition power consumption represents a significant cost factor in the overall HPC system economy. For this reasons in the recent years researchers, supercomputing centers and major vendors have developed new tools and methodologies to measure and optimize the energy consumption of large scale high performance system installation. Due to the link between energy consumption, power consumption and execution time of the application executed by the final user it is important for these tools and methodology to consider all these aspects empowering the final user and the system administrator with the capability of finding the best configuration given different high level objectives. 

The school will give an introductory course on the fundamental concept of power consumption and energy efficiency in HPC systems. Then it will focuses on the mechanisms that today computing elements and systems provide in terms of monitoring and control the power and energy dissipation. Finally it will introduce and give hand ons on a set of tools for reducing the energy consumption in HPC devices.  The school is organized in four main sessions driving the audience from the physical and engineering principles underlying power consumption in supercomputing systems to the practical usage of state-of-the-art tools for monitoring and controlling the energy efficiency of supercomputing machines and workloads. The tools that will be covered are the MSR-SAFE (LLNL), MERIC (IT4I), COUNTDOWN (UNIBO), GEOPM (Intel) libraries. 

Skills:
By the end of the course, participants will be expected to:


have a good understanding of the principles underlying the power consumption, energy dissipation in high performance computing nodes
recognize trade-offs and implications of changing the power consumption in scientific computing systems during the execution of scientific computing applications
have a clear view on the state-of-the-art and of practice in controlling the power consumption and energy efficiency of supercomputing nodes and processors.
learn the internals and the usage of a set of user-space run-time libraries for controlling/optimizing the power consumption and energy efficiency in x86 computing nodes while executing user's applications.
learn how to use these tools for optimize the energy consumption of your codes.


Target Audience:
Researchers, student, system administrator, application developers who may wish to limit the environmental impact of their computations, as well as reducing the cost of energy provisioning in supercomputing system - promoting a more sustainable supercomputing ecosystem.

Pre-requisites:

Knowledge of Fortran or C/C++. Elementary notions of Linux/Unix.
Knowledge of computing architecture
Experience in running HPC applications or systems Basic knowledge of parallel programming OpenMP and/or MPI 

 

Grant

The course is FREE of charge.
The lunch for the two days will be offered to all the participants and some grants are available. The only requirement to be eligible is to be not funded by your institution to attend the course and to work and live in an institute outside the Bologna area. The grant  will be 200 euros for students working and living outside Italy and 100 euros for students working and living in Italy (outside Bologna area). Some documentation will be required and the grant will be paid only after a certified presence of minimum 80% of the lectures.

Further information about how to request the grant, will be provided at the confirmation of the course: about 3 weeks before the starting date.
events.prace-ri.eu/event/833/
Feb 21 9:00 to Feb 22 17:00
23
 
24
 
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.

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, JSC

Contact
For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
events.prace-ri.eu/event/817/
Feb 25 9:00 to Feb 27 16:30
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.

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, JSC

Contact
For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
events.prace-ri.eu/event/817/
Feb 25 9:00 to Feb 27 16:30
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

Agenda: To be announced

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
Muhammad Owais


13:00-14:30
Lunch
 


14:30-18:00
Hands-on
Muhammad Owais



events.prace-ri.eu/event/770/
Feb 26 9:00 18:00
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.

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, JSC

Contact
For any questions concerning the course please send an e-mail to g.cavallaro@fz-juelich.de.
events.prace-ri.eu/event/817/
Feb 25 9:00 to Feb 27 16:30
Description

This course addresses hybrid programming by combining OpenMP and MPI, as well as more advanced topics in MPI. Also, parallel I/O is discussed and exemplified in the course. The course consists of lectures and hands-on exercises.

Learning outcome

After the course the participants should have an idea about more advanced techniques and best practices in parallel programming, and on how to scale up parallel applications and optimize them to different platforms.

Prerequisites

The PTC course Introduction to Parallel Programming or similar background knowledge together with fluency in Fortran and/or C programming languages will be assumed.


Agenda (tentative)

Day 1: Wednesday, February 27



09.00-09.45 Course intro, MPI & OpenMP recap


09.45-10.00 Coffee break


10.00-11.00 Exercises


11.00-11.30 Hybrid MPI + OpenMP programming I


11.30-12.00 Exercises


12.00-13.00 Lunch break


13.00-13.45 Hybrid MPI + OpenMP programming II


13.45-14.30 Exercises


14.30-14.45 Coffee break


14.45-15.15 Advanced MPI I: Communication topologies


15.15-16.15 Exercises


16.15-16.30 Summary of Day 1



Day 2: Thursday, February 28



09.00-09.45 Advanced MPI II: User-defined datatypes


09.45-10.00 Coffee break


10.00-11.15 Exercises


11.15-12.00 Advanced MPI III: One-sided communication


12.00-13.00 Lunch break


13.00-14.30 Exercises


14.30-14.45 Coffee break


14:45-15:15 Parallel I/O with Posix


15.15-16.15 Exercises


16.15-16.30 Summary of Day 2



Day 3: Friday, March 1



09.00-09.45 Parallel I/O with MPI


09.45-10.00 Coffee break


10.00-11.15 Exercises


11.15-12.00 Parallel I/O with MPI cont'd


12.00-13.00 Lunch break


13.00-14.15 Exercises


14.15-14.30 Coffee break


14.30-15.15 Parallel I/O with HDF5


15.15-16.15 Exercises


16.15-16.30 Summary of Day 3




Lecturers: 

Jussi Enkovaara (CSC),  Martti Louhivuori (CSC)

 

Language:  English
Price:          Free of charge
events.prace-ri.eu/event/848/
Feb 27 8:00 to Mar 1 15:00
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, Carlos Tripiana, Oscar Hernandez, Rubén Ramos, Félix Ramos, Pablo Ródenas, Jorge Rodríguez, Marta Renato, Cristian Morales

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. 27) 9am - 5pm

Session 1 / 9:00am – 1:00 pm  (2:45 h lectures, 45’ practical)

09:00h - 09:30h Introduction to BSC, PRACE PATC and this training (David Vicente)

09:30h - 10:30h MareNostrum 4 – the view from System administration group (Javier Bartolomé)

10:30h – 11:00h COFFEE BREAK

11:00h - 11:30h Deep Learning and Big data tools on MN4 (Carlos Tripiana)

11:30h - 12:15h How to use MN4 – Basics: Batch system, file systems, compilers, modules, DT, DL, BSC commands (Oscar Hernandez, Rubén Ramos, Félix Ramos)

12:15h - 13:00h Hands-on I (Oscar Hernandez, Rubén Ramos, Félix Ramos)

13:00h - 14:30h LUNCH (not hosted)

 

Session 2 / 2:30pm – 5:15 pm (3:30 h practical)

14:30h - 15:15h How to use MN4 – HPC architectures and parallelism (Jorge Rodriguez, Pablo Ródenas)

15:15h - 16:00h How to use MN4 – Parallel programming: MPI, MPI IO, MKL, GREASY (Pablo Ródenas, Jorge Rodríguez)

16:00h - 16:15h COFFEE BREAK

16:15h - 16:45h Hands-on II (Pablo Ródenas, Jorge Rodriguez)

16:45h - 17:15h Optional: MareNostrum 4 visit (In the chapel)

17:15h - Adjourn

 

DAY 2 (Feb. 28) 9:00am – 1:00 pm 

Session 3 / 9:00am – 1:00 pm  (2 h lectures, 1:30 h practical)

09:00h - 09:30h Optional: Doubts + Continue previous hands-on + Tunning your app (David Vicente, Jorge Rodríguez)

09:30h - 10:00h How can I get resources from you? (RES Marta Renato)

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

10:30h - 11:00h COFFEE BREAK

11:00h - 11:30h Debugging on MareNostrum, from GDB to DDT (Pablo Ródenas, Oscar Hernandez)

11:30h - 12:30h Hands-on III – Debugging your application (Pablo Ródenas, Oscar Hernandez)

12:30h - 13:00h Wrap-up : Can we help you in your porting ? How ? when ? (David Vicente)

13:00h - Adjourn

 

END of COURSE

 



END of COURSE
events.prace-ri.eu/event/763/
Feb 27 9:00 to Feb 28 13:00
Description

This course addresses hybrid programming by combining OpenMP and MPI, as well as more advanced topics in MPI. Also, parallel I/O is discussed and exemplified in the course. The course consists of lectures and hands-on exercises.

Learning outcome

After the course the participants should have an idea about more advanced techniques and best practices in parallel programming, and on how to scale up parallel applications and optimize them to different platforms.

Prerequisites

The PTC course Introduction to Parallel Programming or similar background knowledge together with fluency in Fortran and/or C programming languages will be assumed.


Agenda (tentative)

Day 1: Wednesday, February 27



09.00-09.45 Course intro, MPI & OpenMP recap


09.45-10.00 Coffee break


10.00-11.00 Exercises


11.00-11.30 Hybrid MPI + OpenMP programming I


11.30-12.00 Exercises


12.00-13.00 Lunch break


13.00-13.45 Hybrid MPI + OpenMP programming II


13.45-14.30 Exercises


14.30-14.45 Coffee break


14.45-15.15 Advanced MPI I: Communication topologies


15.15-16.15 Exercises


16.15-16.30 Summary of Day 1



Day 2: Thursday, February 28



09.00-09.45 Advanced MPI II: User-defined datatypes


09.45-10.00 Coffee break


10.00-11.15 Exercises


11.15-12.00 Advanced MPI III: One-sided communication


12.00-13.00 Lunch break


13.00-14.30 Exercises


14.30-14.45 Coffee break


14:45-15:15 Parallel I/O with Posix


15.15-16.15 Exercises


16.15-16.30 Summary of Day 2



Day 3: Friday, March 1



09.00-09.45 Parallel I/O with MPI


09.45-10.00 Coffee break


10.00-11.15 Exercises


11.15-12.00 Parallel I/O with MPI cont'd


12.00-13.00 Lunch break


13.00-14.15 Exercises


14.15-14.30 Coffee break


14.30-15.15 Parallel I/O with HDF5


15.15-16.15 Exercises


16.15-16.30 Summary of Day 3




Lecturers: 

Jussi Enkovaara (CSC),  Martti Louhivuori (CSC)

 

Language:  English
Price:          Free of charge
events.prace-ri.eu/event/848/
Feb 27 8:00 to Mar 1 15:00
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, Carlos Tripiana, Oscar Hernandez, Rubén Ramos, Félix Ramos, Pablo Ródenas, Jorge Rodríguez, Marta Renato, Cristian Morales

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. 27) 9am - 5pm

Session 1 / 9:00am – 1:00 pm  (2:45 h lectures, 45’ practical)

09:00h - 09:30h Introduction to BSC, PRACE PATC and this training (David Vicente)

09:30h - 10:30h MareNostrum 4 – the view from System administration group (Javier Bartolomé)

10:30h – 11:00h COFFEE BREAK

11:00h - 11:30h Deep Learning and Big data tools on MN4 (Carlos Tripiana)

11:30h - 12:15h How to use MN4 – Basics: Batch system, file systems, compilers, modules, DT, DL, BSC commands (Oscar Hernandez, Rubén Ramos, Félix Ramos)

12:15h - 13:00h Hands-on I (Oscar Hernandez, Rubén Ramos, Félix Ramos)

13:00h - 14:30h LUNCH (not hosted)

 

Session 2 / 2:30pm – 5:15 pm (3:30 h practical)

14:30h - 15:15h How to use MN4 – HPC architectures and parallelism (Jorge Rodriguez, Pablo Ródenas)

15:15h - 16:00h How to use MN4 – Parallel programming: MPI, MPI IO, MKL, GREASY (Pablo Ródenas, Jorge Rodríguez)

16:00h - 16:15h COFFEE BREAK

16:15h - 16:45h Hands-on II (Pablo Ródenas, Jorge Rodriguez)

16:45h - 17:15h Optional: MareNostrum 4 visit (In the chapel)

17:15h - Adjourn

 

DAY 2 (Feb. 28) 9:00am – 1:00 pm 

Session 3 / 9:00am – 1:00 pm  (2 h lectures, 1:30 h practical)

09:00h - 09:30h Optional: Doubts + Continue previous hands-on + Tunning your app (David Vicente, Jorge Rodríguez)

09:30h - 10:00h How can I get resources from you? (RES Marta Renato)

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

10:30h - 11:00h COFFEE BREAK

11:00h - 11:30h Debugging on MareNostrum, from GDB to DDT (Pablo Ródenas, Oscar Hernandez)

11:30h - 12:30h Hands-on III – Debugging your application (Pablo Ródenas, Oscar Hernandez)

12:30h - 13:00h Wrap-up : Can we help you in your porting ? How ? when ? (David Vicente)

13:00h - Adjourn

 

END of COURSE

 



END of COURSE
events.prace-ri.eu/event/763/
Feb 27 9:00 to Feb 28 13:00
 

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