A specialist course called CS621, Parallel and Distributed Computing, explores the ideas, methods, and tools required to create software systems that can run concurrently on several processors or dispersed among several machines. With a solid grasp of the theoretical underpinnings and real-world applications of distributed and parallel computing, students graduate from this course with the abilities needed to develop, implement, and optimize high-performance computing applications.
An introduction to the foundational ideas of distributed and parallel computing opens the course. Pupils gain knowledge of the distinctions between distributed and parallel systems, the reasons for their use, and the kinds of issues that can profit from distribution and parallelization. The fundamentals of distributed and parallel system architectures are also covered in the course.
The synchronization and communication methods for parallel systems are also covered in detail in this course. Pupils gain knowledge of the difficulties in managing several jobs at once and guaranteeing data consistency. Mutual exclusion, avoiding deadlocks, and synchronization primitives like locks, semaphores, and barriers are covered. They also look at the protocols and communication models that parallel systems utilize to help processors communicate data.
Another essential element of CS621 is distributed computing. Students gain knowledge of the architecture and operation of distributed systems, which consist of networked computers with components that communicate and coordinate their operations through message passing. Distributed system designs, such as client-server, peer-to-peer, and multi-tier models, are covered in the course. Students study important ideas including cloud computing, distributed databases, and distributed file systems.
In CS621, scalability and performance improvement are also prioritized. Students investigate techniques for gauging and enhancing distributed and parallel systems' performance. Data locality, partitioning, load balancing, and the effect of communication overhead on performance are covered. Advanced subjects including grid computing, high-performance computing (HPC), and big data processing frameworks like Apache Hadoop and Spark are also covered in the course.
In distributed and parallel computing, privacy and security are crucial factors to take into account. The difficulties of safeguarding sensitive data and securing dispersed systems are covered throughout the course. Students gain knowledge of methods for guaranteeing data integrity and confidentiality in a distributed setting, as well as encryption, access control, and authentic.
CS621 is replete with practical exercises and projects that give students real-world experience creating distributed and parallel programs. These projects frequently entail creating distributed systems, putting parallel algorithms into practice, and maximizing computing task performance through the use of parallel and distributed frameworks.
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