Self presentations Room: Elite Hotel Ideon Getting to know fellow participants, and exchanging with them, is a major added value of our schools. To facilitate this, we will have a self-presentation session on the first day of the school. Each participant, lecturer and organiser will have a possibility to present him-/herself during one minute, using one single slide. You may want to use this opportunity to mention where you come from, your current work, your professional interests - but also your hobbies, things you've done and you're proud of (or not 🙂) etc. The slides will be visible only to other participants of the school, but not public.

Welcome and opening session

Announcements

Spare

Introduction to Physics Computing L1: Hadron Collider Physics

• Arnulf Quadt (Georg August Universitaet Goettingen (DE))

Here we will focus on the physics of particle collisions, theoretical aspects of the standard model of particle physics, its predictive power as well as its shortcomings. Experimental aspects such as collider facilities and modern particle physics experiments as well as example physics questions and corresponding data analyses will be discussed. Furthermore, the compute models with the resulting amount of recorded data and simulated Monte Carlo events will be described.

Tools and Techniques L1: Introduction

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

First, we discuss some of the characteristics of software projects for high energy physics, and some of the issues that arise when people want to contribute to them. We then continue with a brief introduction to software engineering from the perspective of the individual contributor, both as a formal process and how it actually affects what you do. We discuss the examples of unit testing and memory access problems.

Tools and Techniques L2: Tools for Collaboration, Software Engineering Across the Project

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

We continue the track with a discussion of system performance, and what you can (and can't) to affect it. We examine tools to help with that, discussing how they work and how they can mislead. We then discuss source control as a tool for collaboration. Using examples from basic to large and advanced, we show how individual choices can affect the building of large systems.

Exercise 1: Tools and Techniques

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

The exercises provide some direct experience with the tools and techniques described in the Lectures. Teams of two students will work together on examples designed to show the strengths and weaknesses of various tools and approaches. Basic and advanced exercises are available so that students can work at their own level. The very first step is find somebody to work with and sit down together. The two of you will be working together on these exercises. These have been designed to work best two people (or occasionally three), not with one! To start the exercises, open the instructions URL (below) in your favorite web browser and follow those instructions. Please read that index page’s instructions all the way through. We’ve put some general info at the top that you should know, and put some reference links at the bottom that you might later discover you need.

Exercise 2: Tools and Techniques

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

This is a continuation of the Tools and Techniques exercises

Introduction to Physics Computing L2: Digital Data, Simulation and Reconstruction in Modern Particle Physics Experiments

• Arnulf Quadt (Georg August Universitaet Goettingen (DE))

Here, a focus will be placed on specific detector sub-components and their data readout concepts as well as data reconstruction techniques, simulation techniques and analysis techniques.

Software Design L1: Parallelism in a Modern HEP Data Processing Framework

• Stephan Hageboeck (CERN)
• Andrei Gheata (CERN)

Even though the miniaturization of transistors on chips continues like predicted by Moore's law, computer hardware starts to face scaling issues, so-called performance 'walls'. Probably, the best known is the 'power wall', which limits clock frequencies. Amongst others, a way of increasing processor performance remains now to integrate many cores in the same chip. At the same time, the upcoming LHC upgrade will increase the required CPU power drastically. Both problems challenge the current way of software design in high energy physics (HEP). Developers in high energy physics are forced to re-think their ways of software design and need to move to massively parallel applications. This lecture will explain the current HEP software design, the hardware and physics issues that need to be tackled, and possible approaches to achieve the required level of parallelization.

Announcements

Data Science L1: Tools for interactive data exploration

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

High energy physics has a rich history of interactive exploration of physics data, starting with tools like PAW and ROOT. The explosion of Data Science has created new tools for interactive exploration of large and ad-hoc datasets. This lecture introduces some of these, and shows how they can be used to find new and useful features starting with available data.

Software Design L2: Base Concepts of Parallel Programming: A Pragmatic Approach

• Stephan Hageboeck
• Andrei Gheata

This and the following lecture will explain the concepts behind various parallelization methodologies. First, a theoretical introduction to threads, thread-safety and concurrent data access will be given. As the recent C++ standards (starting from C++11) provide build-in support for parallel programming, their most commonly used concurrency features will be shown. Finally, concrete solutions for problems specific to concurrent programming will be discussed.

Exercise 3: Tools and Techniques

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

The exercises provide some direct experience with the tools and techniques described in the Lectures. Teams of two students will work together on examples designed to show the strengths and weaknesses of various tools and approaches. Basic and advanced exercises are available so that students can work at their own level.

Exercise 4:

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

Pub quiz dinner at Elite Hotel Ideon

Data Science L2: Interactive exploration of non-numeric data

• Bob Jacobsen (Lawrence Berkeley National Lab. (US))

This lecture continues the exploration of interactive tools, using a learn-by-doing approach. It covers approaches for statistical simulation, geographical analysis, and textual data.

Software Design L3: Understanding, Debugging and Profiling a Complex Multithreaded Application

• Stephan Hageboeck (CERN)
• Andrei Gheata (CERN)

Dealing with a parallel application is complex. We need to use procedures to rise fences to protect against mistakes, like static analysis tools allowing to find bugs in an automatic way. We also need to use tools to inspect and manipulate the behavior of programs at runtime, like the GDB debugger. Finally, profilers such as igprof can help us understand the performance bottlenecks of an application and get more insight on its efficiency. The objective of this lecture is to become familiar with these tools and be able to apply them in multithreaded programs.

Announcement

Data Analysis L1: Introduction

• Toni Sculac

In this lecture we will explain what are the main goals of data analysis. We will introduce statistics as the powerful mathematical tool for data analysis. We will define probability and random variables as key concepts in statistics for data analysis.

Data Analysis L2: Probability density functions and Monte Carlo methods

• Toni Sculac

In this lecture we will discuss what probability density functions (PDFs) are, and what are their main properties. We will mention the most important PDFs and their properties both for discrete and continuous random variables. The importance of the Gaussian distribution lies in the Central Limit Theorem that will be discussed. Finally, we will discuss the concept of Monte Carlo methods and their usage in High Energy Physics and Data Analysis.

Exercises 1: Software Design

• Stephan Hageboeck
• Andrei Gheata

The exercises will cover the topics of lectures 1 to 4 at a hands-on basis, based on C++11, TBB and Spark. It covers examples for the new C++11 functionality related to threads and thread safety. In addition, there will be examples for concurrent access to data, lock and lock-free data structures, and task based programming. Finally, there will be an exercise to practise the Map-Reduce pattern by using the Spark parallel data processing framework.

Exercises 2: Software Design

• Stephan Hageboeck
• Andrei Gheata

The exercises will cover the topics of lectures 1 to 4 at a hands-on basis, based on C++11, TBB and Spark. It covers examples for the new C++11 functionality related to threads and thread safety. In addition, there will be examples for concurrent access to data, lock and lock-free data structures, and task based programming. Finally, there will be an exercise to practise the Map-Reduce pattern by using the Spark parallel data processing framework.

Software Design L4: Patterns for Parallel Software Development

• Stephan Hageboeck (CERN)
• Andrei Gheata (CERN)

This lecture will present a set of common patterns in parallel programming. The sequential origin of these patterns will be discussed, as well as the restrictions that they impose. A particularly successful combination of patterns, Map-Reduce, will be described in detail and examples of its everyday use at large scale will be given. On the other hand, it will be shown how high-level features like C++ lambdas, the TBB library or the Spark framework can help get started with the aforementioned parallel patterns.

Software Security L1: Introduction

• Sebastian Lopienski (CERN)

The first lecture starts with a definition of computer security and an explanation of why it is so difficult to achieve. The lecture highlights the importance of proper threat modelling and risk assessment. It then presents three complementary methods of mitigating threats: protection, detection, reaction; and tries to prove that security through obscurity is not a good choice.

Announcements

Data Analysis L3: Parameter estimation

• Toni Sculac

In this lecture we will introduce the concept of test statistics and estimators. We will explain what are the key properties of a good estimator and how to obtain it using the Maximum Likelihood and Least Squares methods.

Data Analysis L4: Confidence intervals

• Toni Sculac

In this lecture we will define confidence intervals and make a strong statement on their statistical interpretation when discussing scientific results. We will discuss Neiman confidence belt and Bayesian confidence intervals. Finally, we will learn how to derive confidence intervals for the Maximum likelihood and Least Squares methods.

Photo

Afternoon excursion

Software Security L2: Security in different phases of software development

• Sebastian Lopienski

The second lecture addresses the following question: how to create secure software? It introduces the main security principles (like least privilege, or defense in depth) and discusses security in different phases of the software development cycle. The emphasis is put on the implementation part: most common pitfalls and security bugs are listed, followed by advice on best practice for security development.

Data Analysis L5 - Hypothesis testing and p-value

• Toni Sculac

We will learn about the hypothesis testing procedure and all of its key concepts. We will discuss how to choose a critical region and learn about errors of first and second kind. We will learn the importance of a blinded analysis and understand all the needed steps before looking at the data. Finally, we will discuss when can we announce a discovery in science and the concept of a p-value.

Announcements

Exercises 1: Data Analysis

• Toni Sculac

There will be 4 sets of exercises covering basic properties of PDFs and Monte Carlo generators, Maximum Likelihood fit, and (Advanced) Hypothesis testing. Students will be given realistic but simplified problems where key concepts from statistics need to be applied in order to provide scientific interpretation of data. Each set of exercises consists of 5 problems that will help guide the student. There are clear instructions to help you choose problems based on your pre-knowledge level. Data is provided in a simple text file and can be analysed with any programming language that offers libraries for statistical analysis (Python or C++ are recommended).

Exercises 2: Data Analysis

• Toni Sculac

There will be 4 sets of exercises covering basic properties of PDFs and Monte Carlo generators, Maximum Likelihood fit, and (Advanced) Hypothesis testing. Students will be given realistic but simplified problems where key concepts from statistics need to be applied in order to provide scientific interpretation of data. Each set of exercises consists of 5 problems that will help guide the student. There are clear instructions to help you choose problems based on your pre-knowledge level. Data is provided in a simple text file and can be analysed with any programming language that offers libraries for statistical analysis (Python or C++ are recommended).

Exercise 3: Software Design

• Stephan Hageboeck (CERN)
• Andrei Gheata (CERN)

This is a continuation of the Software Design exercises.

Exercise 4: Software Design

• Stephan Hageboeck (CERN)
• Andrei Gheata (CERN)

This is a continuation of the Software Design exercises.

Data Management L1: Setting the scene: Storage technologies, Storage reliability

• Alberto Pace

The lecture presents the various Storage Models, and the supporting management techniques. The lecture will then go in details on techniques to deliver arbitrary reliability and performance and discuss the solutions for long data preservation and trading between reliability, performances and costs.

Exercise 3: Data Analysis

• Toni Sculac (University of Split Faculty of Science (HR))

There will be 4 sets of exercises covering basic properties of PDFs and Monte Carlo generators, Maximum Likelihood fit, and (Advanced) Hypothesis testing. Students will be given realistic but simplified problems where key concepts from statistics need to be applied in order to provide scientific interpretation of data. Each set of exercises consists of 5 problems that will help guide the student. There are clear instructions to help you choose problems based on your pre-knowledge level. Data is provided in a simple text file and can be analysed with any programming language that offers libraries for statistical analysis (Python or C++ are recommended).

Announcements Room: Building 5, Auditorium

Exercise 4: Data Analysis

• Toni Sculac (University of Split Faculty of Science (HR))

There will be 4 sets of exercises covering basic properties of PDFs and Monte Carlo generators, Maximum Likelihood fit, and (Advanced) Hypothesis testing. Students will be given realistic but simplified problems where key concepts from statistics need to be applied in order to provide scientific interpretation of data. Each set of exercises consists of 5 problems that will help guide the student. There are clear instructions to help you choose problems based on your pre-knowledge level. Data is provided in a simple text file and can be analysed with any programming language that offers libraries for statistical analysis (Python or C++ are recommended).

Free time

Sunday Excursion

Data Management L2: Cryptography, authentication, authorization and accounting 1

• Alberto Pace (CERN)

This lectures give elements of computer security that are relevant to data management. The lectures address the various cryptographic technologies used in data storage systems to ensure data encryption, integrity, confidentiality and access control. The Public Key infrastructure standard will be described as an example.

Introduction to Machine Learning L1

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Announcements

Q1

Q2

Exercises 1: Software Security

• Sebastian Lopienski (CERN)

In the practice session, a range of typical security vulnerabilities will be presented. The goal is to learn how they can be exploited (for privilege escalation, data confidentiality compromise etc.), how to correct them, and how to avoid them in the first place! Students will be given small pieces of source code in different programming languages, and will be asked to find vulnerabilities and fix them. The online course documentation will gradually reveal more and more information to help students in this task. Additionally, students will have a chance to try several source code analysis tools, and see how such tools can help them find functionality bugs and security vulnerabilities.

Exercises 2: Software Security

• Sebastian Lopienski (CERN)

In the practice session, a range of typical security vulnerabilities will be presented. The goal is to learn how they can be exploited (for privilege escalation, data confidentiality compromise etc.), how to correct them, and how to avoid them in the first place! Students will be given small pieces of source code in different programming languages, and will be asked to find vulnerabilities and fix them. The online course documentation will gradually reveal more and more information to help students in this task. Additionally, students will have a chance to try several source code analysis tools, and see how such tools can help them find functionality bugs and security vulnerabilities.

Study or sports time

Quantum Intro Evening Lecture TBC

Data Management L3: Cryptography, authentication, authorization and accounting 2

• Alberto Pace (CERN)

This lecture will continue the discussion on various authentication technologies and then move to authorization. Accounting will also be addressed.

Introduction to Machine Learning L2

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Announcements

Software Security L3: Web application security, exercise debriefing

• Sebastian Lopienski (CERN)

- This third hour consists of a debriefing of the exercises, and in particular those web-related. Various vulnerabilities typical to web applications (such as Cross-site scripting, SQL injection, cross-site request forgery etc.) are introduced and discussed.

Exercise 3: Software Security - Sebastian Lopienski

• Sebastian Lopienski (CERN)

- In the practice session, a range of typical security vulnerabilities will be presented. The goal is to learn how they can be exploited (for privilege escalation, data confidentiality compromise etc.), how to correct them, and how to avoid them in the first place! Students will be given small pieces of source code in different programming languages, and will be asked to find vulnerabilities and fix them. The online course documentation will gradually reveal more and more information to help students in this task. Additionally, students will have a chance to try several source code analysis tools, and see how such tools can help them find functionality bugs and security vulnerabilities.

Exercice 1: Data Technologies

• Andreas Joachim Peters (CERN)

The first part of hands-on exercises aims to improve understanding of basic parameters in IO systems: • network and media latency • access patterns • OS caching • bottlenecks and optimization strategies for local and remote data access. Few essential Linux tools will be introduced to monitor and measure IO performance avoiding bias introduced by OS caching. Students will experience and measure the impact of latency and access patterns on IO performance. The second part covers the concept of parallelism and redundancy in storage system. We will apply the technology of Cloud storage systems to store and retrieve files in our local desktop cluster using a distributed hash table to locate files or file fragments and a REST interface to do GET, PUT or DELETE operations on these. The exercises conclude with the implementation and performance tuning of a RAID verification algorithm.

Exercises 2: Data Technologies

• Andreas Joachim Peters (CERN)

The first part of hands-on exercises aims to improve understanding of basic parameters in IO systems: • network and media latency • access patterns • OS caching • bottlenecks and optimization strategies for local and remote data access. Few essential Linux tools will be introduced to monitor and measure IO performance avoiding bias introduced by OS caching. Students will experience and measure the impact of latency and access patterns on IO performance. The second part covers the concept of parallelism and redundancy in storage system. We will apply the technology of Cloud storage systems to store and retrieve files in our local desktop cluster using a distributed hash table to locate files or file fragments and a REST interface to do GET, PUT or DELETE operations on these. The exercises conclude with the implementation and performance tuning of a RAID verification algorithm.

Study or sports time

Introduction to Machine Learning L3

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Data Management L4: Distributed Hash Tables, Data Replication, Caching, Monitoring, Alarms and Quota 1

• Alberto Pace (CERN)

This lecture describes the various possible technologies used to implement distributed hash tables, data workflows and complex data transfer processes. It also discusses problems with data caching and Garbage Collection to conclude on monitoring and quota enforcement.

Announcements Room: Building 5, Auditorium

Exercises 3: Data Technologies

• Andreas Joachim Peters (CERN)

This is a continuation of the Data Technologies exercises.

Exercises 4: Data Technologies

• Andreas Joachim Peters (CERN)

This is a continuation of the Data Technologies exercises.

Exercise 1: Machine Learning

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Exercise 2: Machine Learning

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Study or sports time

Introduction to Machine Learning L4

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Introduction Machine Learning L5

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Announcements Room: Building 5, Auditorium

Exercise 3: Machine Learning

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Exercise 4: Machine Learning

• Judith Katzy (Deutsches Elektronen-Synchrotron (DE))

Spare

Study or sports time

Exam

Traditional football match

Graduation ceremony

Breakfast and check out