A word from the IT Department Head

• Frederic Hemmer (CERN)

Room: 31/3-004 - IT Amphitheatre

Introduction to the Inverted CSC

• Sebastian Lopienski (CERN)

Room: 31/3-004 - IT Amphitheatre

A practical approach to Convolutional Neural Networks (lecture) (Lectures and exercises)

• Daniel Hugo Campora Perez (CERN + University of Seville)

Room: 31/3-004 - IT Amphitheatre The field of Artificial Intelligence is blooming with many techniques and developments in the last years. Convolutional Neural Networks is a class of feed-forward neural networks that are typically used to analyze images and extract information. We will explore the mathematical foundation of CNNs, join the hype and get some practical hands-on experience with the technology that will change our everyday lives.

A Scientist's Guide to FPGAs (Lectures and exercises)

• Alexander Ruede (CERN + KIT-IPE)

Room: 31/3-004 - IT Amphitheatre Field Programmable Gate Arrays (FPGAs) have become ubiquitous in a variety of technological and scientific fields. Their versatility make them an ideal match not only for computing intensive tasks but also for the differing requirements of custom electronics that often can be found in experimental setups. This seminar leads the audience into the fully programmable and intrinsically parallel world of FPGAs. After an introduction to digital design and the anatomy of an FPGA, the design flow and required way of thinking will be presented. The seminar will be completed by a comparison of hardware and software-driven computation as well as an overview of the application of FPGAs in different fields and tasks.

Lunch break / WIT Diversity Talk Room: 31/3-004 - IT Amphitheatre During lunchtime, CERN's Women in Technology (WIT) community will have a Diversity Talk with Tim Smith. More details: https://indico.cern.ch/event/792031/ Inverted CSC participants are welcome to join this very interesting event.

Global track finding algorithms (Lectures and exercises)

• Dmitry Neverov (Nagoya University)

Room: 31/3-004 - IT Amphitheatre In high energy physics experiments the reconstruction of tracks of charged particles provides the core for the measurements of these particles' properties. Track finding algorithms can roughly be divided into two main categories: local and global. Local track finding algorithms try to link individual hits one by one while utilizing a variety of smart techniques to mitigate combinatorial complexity, whereas global track finding algorithms treat all hits simultaneously. In this lecture we will look into track finding algorithms in wire chambers that are performed on all hits at once. The search is done by converting individual hit parameters to a curve in a dual space using Legendre or Hough transform, with the intersection of multiple curves corresponding to a track compatible with given hits. Then, the problem of finding a track is translated to the problem of finding most densely populated regions in the dual space which can be done effectively and quickly by a quadtree search.

Hardware Acceleration Through FPGAs - Basic Concepts (lecture 1) (Lectures and exercises)

• Giorgio Lopez (CERN)

Room: 31/3-004 - IT Amphitheatre FPGAs are a more and more ubiquitous technology. They offer the benefits of fast, application-tailored hardware, typically associated with ASICs, while enabling fast prototyping, upgradability and low costs. This makes them an ideal ally in HEP computing, specifically in areas where high performance is needed and/or specifications and needs may vary. The lectures will focus on the intrinsic parallel processing characteristics of FPGAs, emphasizing how they can be exploited to implement data-intensive algorithms. Focus will also be put on concepts like hardware/software partitioning (very important to help the most performing parts of the systems in collaborating with the legacy CPU oriented codebase). A simple hands on exercises session will be added to let the students get acquainted with the main tools and the VHDL language.

A practical approach to Convolutional Neural Networks (exercise 1) (Lectures and exercises)

• Daniel Hugo Campora Perez (CERN + University of Seville)

Room: 513/1-024 The field of Artificial Intelligence is blooming with many techniques and developments in the last years. Convolutional Neural Networks is a class of feed-forward neural networks that are typically used to analyze images and extract information. We will explore the mathematical foundation of CNNs, join the hype and get some practical hands-on experience with the technology that will change our everyday lives.

A practical approach to Convolutional Neural Networks (exercise 2) (Lectures and exercises)

• Daniel Hugo Campora Perez (CERN + University of Seville)

Room: 513/1-024 The field of Artificial Intelligence is blooming with many techniques and developments in the last years. Convolutional Neural Networks is a class of feed-forward neural networks that are typically used to analyze images and extract information. We will explore the mathematical foundation of CNNs, join the hype and get some practical hands-on experience with the technology that will change our everyday lives.

Hardware Acceleration Through FPGAs - Basics of VHDL (lecture 2) (Lectures and exercises)

• Giorgio Lopez (CERN)

Room: 31/3-004 - IT Amphitheatre FPGAs are a more and more ubiquitous technology. They offer the benefits of fast, application-tailored hardware, typically associated with ASICs, while enabling fast prototyping, upgradability and low costs. This makes them an ideal ally in HEP computing, specifically in areas where high performance is needed and/or specifications and needs may vary. The lectures will focus on the intrinsic parallel processing characteristics of FPGAs, emphasizing how they can be exploited to implement data-intensive algorithms. Focus will also be put on concepts like hardware/software partitioning (very important to help the most performing parts of the systems in collaborating with the legacy CPU oriented codebase). A simple hands on exercises session will be added to let the students get acquainted with the main tools and the VHDL language.

Tensor Networks - Introduction and Matrix Product States (lecture 1) (Lectures and exercises)

• Patrick Emonts (Max Planck Institute of Quantum Optics)

Room: 31/3-004 - IT Amphitheatre In recent years, tensor networks have become a viable alternative to Monte Carlo calculations and exact diagonalization for the simulation of many-body systems. As they represent a formulation of quantum mechanical wavefunctions with polynomially many parameters, they make calculations of large systems feasible. They have already found wide application in condensed matter physics and start to be an interesting tool for high energy physics as well. In this lecture series, I will introduce the basic concepts of tensor networks. We will start with an introduction of the necessary basics of quantum mechanics and linear algebra and focus on the algorithmic side of tensor networks in the second lecture.

Tensor Networks - The iTEBD Algorithm (lecture 2) (Lectures and exercises)

• Patrick Emonts (Max Planck Institute of Quantum Optics)

Room: 31/3-004 - IT Amphitheatre In recent years, tensor networks have become a viable alternative to Monte Carlo calculations and exact diagonalization for the simulation of many-body systems. As they represent a formulation of quantum mechanical wavefunctions with polynomially many parameters, they make calculations of large systems feasible. They have already found wide application in condensed matter physics and start to be an interesting tool for high energy physics as well. In this lecture series, I will introduce the basic concepts of tensor networks. We will start with an introduction of the necessary basics of quantum mechanics and linear algebra and focus on the algorithmic side of tensor networks in the second lecture.

Big Data Technologies and Physics Analysis with Apache Spark (lecture 1) (Lectures and exercises)

• Evangelos Motesnitsalis (CERN)

Room: 31/3-004 - IT Amphitheatre The Large Hadron Collider is scheduled to shut down for a 2 years maintenance period since December 2018. However, the already collected data -which are stored in a dedicated custom storage service- between April 2015 and November 2018, exceed 150 PBs in total. To analyse these data, more and more teams at CERN decide to use Big Data Technologies to perform Physics Analysis and "Data Reduction", i.e. produce smaller reusable datasets for frequent access. These technologies show great potential in speeding up the existing procedures. This lecture will provide an overview of the latest trending big data technologies in the Hadoop and Spark ecosystems with focus on their main architecture characteristics, and then will target a number of important questions: How can we perform Physics Analysis with Big Data Technologies? What are the problems faced? What are the challenges and the available data sources? What are the other domain in which Big Data Analytics are applied at CERN?

Big Data Technologies and Physics Analysis with Apache Spark (lecture 2) (Lectures and exercises)

• Evangelos Motesnitsalis (CERN)

Room: 31/3-004 - IT Amphitheatre The Large Hadron Collider is scheduled to shut down for a 2 years maintenance period since December 2018. However, the already collected data -which are stored in a dedicated custom storage service- between April 2015 and November 2018, exceed 150 PBs in total. To analyse these data, more and more teams at CERN decide to use Big Data Technologies to perform Physics Analysis and "Data Reduction", i.e. produce smaller reusable datasets for frequent access. These technologies show great potential in speeding up the existing procedures. This lecture will provide an overview of the latest trending big data technologies in the Hadoop and Spark ecosystems with focus on their main architecture characteristics, and then will target a number of important questions: How can we perform Physics Analysis with Big Data Technologies? What are the problems faced? What are the challenges and the available data sources? What are the other domain in which Big Data Analytics are applied at CERN?

Hardware Acceleration Through FPGAs - First Experiments in VHDL (exercise 1) (Lectures and exercises)

• Giorgio Lopez (CERN)

Room: 513/1-024 FPGAs are a more and more ubiquitous technology. They offer the benefits of fast, application-tailored hardware, typically associated with ASICs, while enabling fast prototyping, upgradability and low costs. This makes them an ideal ally in HEP computing, specifically in areas where high performance is needed and/or specifications and needs may vary. The lectures will focus on the intrinsic parallel processing characteristics of FPGAs, emphasizing how they can be exploited to implement data-intensive algorithms. Focus will also be put on concepts like hardware/software partitioning (very important to help the most performing parts of the systems in collaborating with the legacy CPU oriented codebase). A simple hands on exercises session will be added to let the students get acquainted with the main tools and the VHDL language.

Hardware Acceleration Through FPGAs - Easy DSP Applications (exercise 2) (Lectures and exercises)

• Giorgio Lopez (CERN)

Room: 513/1-024 FPGAs are a more and more ubiquitous technology. They offer the benefits of fast, application-tailored hardware, typically associated with ASICs, while enabling fast prototyping, upgradability and low costs. This makes them an ideal ally in HEP computing, specifically in areas where high performance is needed and/or specifications and needs may vary. The lectures will focus on the intrinsic parallel processing characteristics of FPGAs, emphasizing how they can be exploited to implement data-intensive algorithms. Focus will also be put on concepts like hardware/software partitioning (very important to help the most performing parts of the systems in collaborating with the legacy CPU oriented codebase). A simple hands on exercises session will be added to let the students get acquainted with the main tools and the VHDL language.

Tensor Networks - Singular Value Decomposition (exercise 1) (Lectures and exercises)

• Patrick Emonts (Max Planck Institute of Quantum Optics)

Room: 513/1-024 In recent years, tensor networks have become a viable alternative to Monte Carlo calculations and exact diagonalization for the simulation of many-body systems. As they represent a formulation of quantum mechanical wavefunctions with polynomially many parameters, they make calculations of large systems feasible. They have already found wide application in condensed matter physics and start to be an interesting tool for high energy physics as well. In this lecture series, I will introduce the basic concepts of tensor networks. We will start with an introduction of the necessary basics of quantum mechanics and linear algebra and focus on the algorithmic side of tensor networks in the second lecture.

Tensor Networks - Application of the iTEBD Algorithm (exercise 2) (Lectures and exercises)

• Patrick Emonts (Max Planck Institute of Quantum Optics)

Room: 513/1-024 In recent years, tensor networks have become a viable alternative to Monte Carlo calculations and exact diagonalization for the simulation of many-body systems. As they represent a formulation of quantum mechanical wavefunctions with polynomially many parameters, they make calculations of large systems feasible. They have already found wide application in condensed matter physics and start to be an interesting tool for high energy physics as well. In this lecture series, I will introduce the basic concepts of tensor networks. We will start with an introduction of the necessary basics of quantum mechanics and linear algebra and focus on the algorithmic side of tensor networks in the second lecture.

How container orchestration can strengthen your micro-services: the approach of Kubernetes (lecture) (Lectures and exercises)

• Riccardo Poggi (University of Geneva)

Room: 31/3-004 - IT Amphitheatre With the rise of container technologies during the past few years there have been many paradigms shifts in terms of software development, deployment and maintenance, especially in conjunction with micro-service architectures. The lecture covers these fundamental concepts and focuses on the challenges of container orchestration. Crucial aspects like horizontal and vertical scaling, availability, fault-tolerance and rolling updates are among the topics covered by the lecture which will then also be experienced during the hands-on exercises.

Data Centre visit Room: 31/3-004 - IT Amphitheatre

Efficient C++ implementation of custom FEM kernel with Eigen (Lectures and exercises)

• Mikhail Sizov (Budker Institute of Nuclear Physics)

Room: 31/3-004 - IT Amphitheatre We will start from examples of problems solved by finite element method - equilibrium magnetic fields, structural deflection calculations. Then we will talk about foundation of FEM method key concepts such as stiffness matrix and impact of high matrix dimensions and sparse characteristic to ways data can be calculated more efficiently. To implement kernel we will introduce Eigen, a C++ linear algebra library that eliminates intermediate temporary objects by utilizing expression templates technique and generates efficient high-level math code with most of complexity taken from you.

How container orchestration can strengthen your micro-services: the approach of Kubernetes (exercise 1) (Lectures and exercises)

• Riccardo Poggi (University of Geneva)

Room: 513/1-024 With the rise of container technologies during the past few years there have been many paradigms shifts in terms of software development, deployment and maintenance, especially in conjunction with micro-service architectures. The lecture covers these fundamental concepts and focuses on the challenges of container orchestration. Crucial aspects like horizontal and vertical scaling, availability, fault-tolerance and rolling updates are among the topics covered by the lecture which will then also be experienced during the hands-on exercises.

How container orchestration can strengthen your micro-services: the approach of Kubernetes (exercise 2) (Lectures and exercises)

• Riccardo Poggi (University of Geneva)

Room: 513/1-024 With the rise of container technologies during the past few years there have been many paradigms shifts in terms of software development, deployment and maintenance, especially in conjunction with micro-service architectures. The lecture covers these fundamental concepts and focuses on the challenges of container orchestration. Crucial aspects like horizontal and vertical scaling, availability, fault-tolerance and rolling updates are among the topics covered by the lecture which will then also be experienced during the hands-on exercises.

Big Data Technologies and Physics Analysis with Apache Spark (exercise 1) (Lectures and exercises)

• Evangelos Motesnitsalis (CERN)

Room: 513/1-024 The Large Hadron Collider is scheduled to shut down for a 2 years maintenance period since December 2018. However, the already collected data -which are stored in a dedicated custom storage service- between April 2015 and November 2018, exceed 150 PBs in total. To analyse these data, more and more teams at CERN decide to use Big Data Technologies to perform Physics Analysis and "Data Reduction", i.e. produce smaller reusable datasets for frequent access. These technologies show great potential in speeding up the existing procedures. This lecture will provide an overview of the latest trending big data technologies in the Hadoop and Spark ecosystems with focus on their main architecture characteristics, and then will target a number of important questions: How can we perform Physics Analysis with Big Data Technologies? What are the problems faced? What are the challenges and the available data sources? What are the other domain in which Big Data Analytics are applied at CERN?

Big Data Technologies and Physics Analysis with Apache Spark (exercise 2) (Lectures and exercises)

• Evangelos Motesnitsalis (CERN)

Room: 513/1-024 The Large Hadron Collider is scheduled to shut down for a 2 years maintenance period since December 2018. However, the already collected data -which are stored in a dedicated custom storage service- between April 2015 and November 2018, exceed 150 PBs in total. To analyse these data, more and more teams at CERN decide to use Big Data Technologies to perform Physics Analysis and "Data Reduction", i.e. produce smaller reusable datasets for frequent access. These technologies show great potential in speeding up the existing procedures. This lecture will provide an overview of the latest trending big data technologies in the Hadoop and Spark ecosystems with focus on their main architecture characteristics, and then will target a number of important questions: How can we perform Physics Analysis with Big Data Technologies? What are the problems faced? What are the challenges and the available data sources? What are the other domain in which Big Data Analytics are applied at CERN?