Statistical Methods and Applications

General goals The course aims to introduce HPC (High Performance Computing) systems, their architecture, and their principles of operation. Additionally, the course aims to introduce parallel and distributed programming, with the goal of reducing resolution times for particularly complex problems through the coordinated use of a large number of computing units. Knowledge and comprehension Students will understand the principles underlying HPC systems and how to organize a resolution strategy for an algorithm that can benefit from the presence of multiple computing units. Applying knowledge and comprehension Upon completion of the course, students will be able to create simple parallel and distributed applications that leverage the increased computing capacity of an HPC system. Students will also be able to execute the developed algorithms using an existing computing infrastructure. Judgement skills Students will develop the ability to identify particular types of problems for which the use of a parallel or distributed approach is significantly helpful. Communication skills The students will acquire the technical-scientific language commonly used in this discipline, also thanks to the study and to the practice. Learning skills Students who pass the exam will have learned the paradigms to apply parallel and distributed computing techniques to solve complex problems, utilizing the computing capabilities of an HPC system.