inverted CERN School of Computing 2016

Contribution List

11. Continuous Integration : how can it help?

Joshua Wyatt Smith (Georg-August-Universitaet Goettingen (DE))

29/02/2016, 09:30

Software becomes more complex as the project size and number of developers grow. As these two factors increase, so too does the potential for more errors in the code. Previous time and money can be wasted trying to track down bugs that could have been easily avoided should there have been a good workflow in place.

Continuous Integration (CI) is one such strategy that can...

12. Continuous Delivery and Quality Monitoring

Kamil Henryk Krol (CERN)

29/02/2016, 11:00

We’re all involved in some software/physics projects. As a rule of thumb projects start really simple - a couple of scripts, classes and a few external dependencies. At this phase delivering a release to our clients is simple. We can compile the project locally and deliver compiled sources, for example by e-mail. Unfortunately, in most cases the growth of projects is inevitable. Our simple...

16. A word from the IT Department Head

Frederic Hemmer (CERN)

29/02/2016, 13:30

13. I - Template Metaprogramming for Massively Parallel Scientific Computing - Expression Templates

Jiří Vyskočil (Czech Technical University in Prague)

29/02/2016, 13:45

Large scale scientific computing raises questions on different levels ranging from the fomulation of the problems to the choice of the best algorithms and their implementation for a specific platform. There are similarities in these different topics that can be exploited by modern-style C++ template metaprogramming techniques to produce readable, maintainable and generic code. Traditional...

14. I - Multivariate Classification and Machine Learning in HEP

Thomas Keck (KIT)

29/02/2016, 14:45

Traditional multivariate methods for classification (Stochastic Gradient Boosted Decision Trees and Multi-Layer Perceptrons) are explained in theory and practise using examples from HEP. General aspects of multivariate classification are discussed, in particular different regularisation techniques. Afterwards, data-driven techniques are introduced and compared to MC-based methods.

15. Formal verification - Robust and efficient code: Introduction to Formal Verification

Kim Albertsson (CERN)

29/02/2016, 16:15

LECTURE 1: We will establish two general approaches to FV and where they are applicable: model checking and theorem proving. We will explore the latter in more details and have a brief look at the underlying theory, predicate logic. We will see how this family of logic systems can be used to prove abstract properties of our program and why this is useful. Practical examples will be presented...

0. I - Event reconstruction in Modern Particle Physics

Daniel Martin Saunders (University of Bristol (GB))

01/03/2016, 09:00

Particle physics experiments have always been at the forefront of big data experiments: the upgrade of the LHCb experiment will lead to data rates greater than 10Tb’s per second! This is key to the success of high-energy physics, where large data samples, sophisticated triggers and robust simulations have lead to observing and understanding extremely rare events, including the Higgs Boson. ...

1. I - Detector Simulation for the LHC and beyond: how to match computing resources and physics requirements

Valentina Cairo (Universita della Calabria (IT))

01/03/2016, 10:00

Detector simulation at the LHC is one of the most computing intensive activities. In these lectures we will show how physics requirements were met for the LHC experiments and extrapolate to future experiments (FCC-hh case). At the LHC, detectors are complex, very precise and ambitious: this implies modern modelisation tools for geometry and response. Events are busy and characterised by an...

2. II - Template Metaprogramming for Massively Parallel Scientific Computing - Vectorization with Expression Templates

Jiří Vyskočil (Czech Technical University in Prague)

01/03/2016, 11:30

Large scale scientific computing raises questions on different levels ranging from the fomulation of the problems to the choice of the best algorithms and their implementation for a specific platform. There are similarities in these different topics that can be exploited by modern-style C++ template metaprogramming techniques to produce readable, maintainable and generic code. Traditional...

3. II - Multivariate Classification and Machine Learning in HEP

Thomas Keck (KIT)

01/03/2016, 14:00

A summary of the history of deep-learning is given and the difference to traditional artificial neural networks is discussed. Advanced methods like convoluted neural networks, recurrent neural networks and unsupervised training are introduced. Interesting examples from this emerging field outside HEP are presented. Possible applications in HEP are discussed.

4. Formal Verification - Robust and Efficient code: Why Formal Verification

Kim Albertsson (CERN)

01/03/2016, 15:00

LECTURE 2: In this lecture we will expand on the concepts of the previous lecture and establish formal methods in a broader context, ignoring implementation detail, and investigate how and where these methods are used today, and where they might be used tomorrow. As concrete examples we will be studying how FV can benefit static analysis and comp-cert, and verified C...

5. Accelerating C++ applications in Medical Physics

Pedro Manuel Mendes Correia (University of Aveiro (PT))

01/03/2016, 16:30

The recent developments in multithreading tools in C++, like OpenMP and TBB, taking advantage of the multicore architecture of the nowadays processors, allowed the creation and improvement of powerful softwares for scientific research. This talk will be focused on the development of such software for simulations, data acquisition and image reconstruction in Positron Emission Tomography, one of...

6. II - Event reconstruction in Modern Particle Physics

Daniel Martin Saunders (University of Bristol (GB))

02/03/2016, 09:00

Particle physics experiments have always been at the forefront of big data experiments: the upgrade of the LHCb experiment will lead to data rates greater than 10Tb’s per second! This is key to the success of high-energy physics, where large data samples, sophisticated triggers and robust simulations have lead to observing and understanding extremely rare events, including the Higgs Boson. ...

7. II - Detector simulation for the LHC and beyond : how to match computing resources and physics requirements

Valentina Cairo (Universita della Calabria (IT))

02/03/2016, 10:00

Detector simulation at the LHC is one of the most computing intensive activities. In these lectures we will show how physics requirements were met for the LHC experiments and extrapolate to future experiments (FCC-hh case). At the LHC, detectors are complex, very precise and ambitious: this implies modern modelisation tools for geometry and response. Events are busy and characterised by an...

8. III - Template Metaprogramming for massively parallel scientific computing - Templates for Iteration; Thread-level Parallelism

Jiří Vyskočil (Czech Technical University in Prague)

02/03/2016, 11:30

Large scale scientific computing raises questions on different levels ranging from the fomulation of the problems to the choice of the best algorithms and their implementation for a specific platform. There are similarities in these different topics that can be exploited by modern-style C++ template metaprogramming techniques to produce readable, maintainable and generic code. Traditional...

9. Shared memory and message passing revisited in the many-core era

Aram Santogidis (CERN)

02/03/2016, 14:00

In the 70s, Edsgar Dijkstra, Per Brinch Hansen and C.A.R Hoare introduced the fundamental concepts for concurrent computing. It was clear that concrete communication mechanisms were required in order to achieve effective concurrency. Whether you're developing a multithreaded program running on a single node, or a distributed system spanning over hundreds of thousands cores, the choice of...

10. Volatile Environments with Virtualisation Technologies

Anastasios Andronidis (University of Ioannina (GR))

02/03/2016, 15:00

Sometimes our job or even our interest to learn something new, requires from us to install a lot of different software to allow a specific program to run on our operating system. This (in the best case) might just prohibit your program to run due to conflicts between different library or language versions; in the worst case your operating system will start becoming full of junk and later on...

17. Closing remarks

Alberto Pace (CERN)

02/03/2016, 16:00