The course will present an overview on parallel architectures in general with special focus on parallel machines with distributed memory. Strong and weak points of the different types of architectures are critically discussed and compared. Software development environments and tools for parallel architectures are presented and the importance of these tools for the strongly application oriented user is explained. Models describing the performance of parallel architectures with shared and distributed memory architectures are discussed. After presenting an introduction to numerical modelization in general, the course will discuss the different phases, starting from real experiment, switching to physical and numerical modeling.
Special focus is on the adaptation of numerical approaches to modern parallel architectures.
It will not assume prior expertise in neural networks. The six lessons presented will cover machine learning and biological computation, learning rules and implementations for adaptive artificial neurons, neural network systems, applications like pattern recognition and signal processing, and a review of existing software design tools, hardware implementations, neural network journals, books, and conferences. A major fraction of the course is dedicated to the presentation of the successful implementation of a broad spectrum of applications drawn from physics, engineering, computer science and other areas. The usefulness of the distinct types of parallel architectures for a collection of very CPU-intensive applications is compared, and, depending on the type of architecture, concrete techniques are presented, by means of which these applications can be mapped to the hardware in an optimal fashion. During the five days of the course, there will be six hours of lectures each day, supplemented by informal discussion sessions during the day and in the evening.