Parallel Computing

The course targets equipping students with foundational knowledge and practical skills in parallel computing. It is designed for students with a keen interest in understanding and harnessing the power of parallel architectures and algorithms to solve complex computational problems. It covers theoretical underpinnings, architecture, algorithm design, programming techniques, and emerging models in the domain. The curriculum unfolds across four main segments: 1. Parallel Computation Models and Parallelism: Exploration of parallelism fundamentals and diverse computation models lays the groundwork. 2. Parallel Architectures: Investigation into various architectural designs, including shared-memory, distributed-memory, and data parallel architectures, alongside interconnection networks and communication basics. 3. Parallel Algorithm Design and Programming: Focuses on crafting and implementing efficient parallel algorithms, emphasizing performance optimization and scalability. 4. Emerging Parallel Computing Models: Introduces cutting-edge paradigms such as grid, cloud, and GPGPU computing, highlighting their significance and application. This approach aims to foster a deep understanding of parallel computing's core principles, alongside the development of competencies in parallel programming on actual parallel systems. Students will navigate through the theoretical aspects, practical implementations, and the evolving landscape of parallel computing, preparing them for advanced research or professional practice in this dynamic field.