Parallelrechner - Architektur und Anwendung

The lecture on parallel computers and their architecture provides a comprehensive overview of the principles and design of parallel computing systems. It begins by addressing the motivation for parallelism, emphasizing its role in meeting the growing demands of high-performance applications such as scientific simulations and artificial intelligence.

Key topics include classifying parallel systems using Flynn’s taxonomy, such as Single Instruction Multiple Data (SIMD) and Multiple Instruction Multiple Data (MIMD). The lecture explores shared-memory and distributed-memory architectures, focusing on communication models, synchronization techniques, and the role of interconnection networks like mesh, torus, and hypercube topologies.

Performance considerations are a central theme, with discussions on speedup, scalability, load balancing, and memory hierarchy bottlenecks. The session also introduces programming models like MPI for message passing and OpenMP for shared-memory systems, linking theoretical concepts to practical tools.

Real-world examples illustrate the principles discussed. Emerging trends, including heterogeneous architectures like GPUs and the integration of quantum computing, are highlighted alongside challenges such as programming complexity and power efficiency.