Parallel Computing

Catalog description

This course introduces the principles and practice of parallel computing, with a focus on modern applications in artificial intelligence. Students will learn models of parallel computation, parallel architectures, and programming techniques such as message passing, shared memory, and GPU computing. The course emphasizes design, analysis, and implementation of parallel algorithms, highlighting their role in accelerating AI and machine learning tasks. Topics include parallel sorting and searching, matrix operations, and neural network training. Case studies will demonstrate how parallel computing enables large-scale deep learning, and other AI-related applications. By the end of the course, students will be able to analyze problems for parallelism, implement parallel solutions, and evaluate performance trade-offs. Open to juniors and seniors. Cross-listed with CMPG-771 Parallel Computing.