19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research
The 19th edition of ACAT will bring together experts to explore and confront the boundaries of computing, automated data analysis, and theoretical calculation technologies, in particle and nuclear physics, astronomy and astrophysics, cosmology, accelerator science and beyond. ACAT provides a unique forum where these disciplines overlap with computer science, allowing for the exchange of ideas and the discussion of cutting-edge computing, data analysis and theoretical calculation technologies in fundamental physics research.
There is a fundamental shift occurring in how computing is used in research in general and data analysis in particular. The abundance of inexpensive, powerful, easy to use computing power in the form of CPUs, GPUs, FPGAs, etc., has changed the role of computing in physics research over the last decade. The rise of new techniques, like deep learning, means the changes promise to keep coming. Even more revolutionary approaches, such as Quantum Computing, are now closer to becoming a reality.
Please join us to explore these future changes, and learn about new algorithms and ideas and trends in scientific computing physics. Most of all, join us for the discussions and the sharing of expertise in the field.
Empowering the Revolution: Bringing Machine Learning to High Performance Computing
As often has happened in the development of AI, theoretical advances in the domain have opened the door to exceptional perspectives of application in the most diverse fields of science, business and society at large. Today the introduction of Machine (Deep) Learning is no exception, and beyond the hype we can already see that this new family of techniques and approaches may usher a real revolution in various fields. However, as it has happened time and again in the past, we start realizing that one of the limitations of Deep Learning is sheer computing power. While these techniques allow to tackle extremely complex problems on very large amount of data, the computational cost, particularly of the training phase, is rising fast. The introduction of meta-optimizations such as hyper-parameter scans may further enlarge the possibilities of Machine Learning, but this in turn will require substantial improvements in code performance.
At the same time, High Performance Computing is also in full evolution, offering new solutions and perspectives, both in hardware and in software. In this version of ACAT we would like to focus on how this renewed momentum in the HPC world may provide the necessary power to fulfill the revolutionary promises offered by recent breakthroughs in AI at large and in Machine Learning in particular.
Saas-Fee, a resort village in the Swiss Alps near the Italian border, is known for its proximity to mountains more than 4,000m above sea level, or 4-thousanders. It's a gateway to more than 100km of pistes for skiing and snowboarding, plus sledding and toboggan runs. The Mittelallalin Ice Pavilion is a frozen grotto carved into the Fee Glacier. In the summer, the surrounding area draws hikers and rock climbers.
Elevation: 1,800 m
Weather: 1°C, Wind S at 32 km/h, 74% Humidity
Twin towns: Steamboat Springs (USA), Rocca di Cambio (Italy)
More information at https://en.wikipedia.org/wiki/Saas-Fee
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Many people are working together to bring you this conference! The organization page has some details. F. Carminati is chair of the Scientific Program Committee, F.Rademakers is the chair of the Local Organizing Committee.