Parallel Computing

Catalog description

Parallel computing for science and engineering applications: parallel programming and performance evaluation, parallel libraries and problem-solving environments, models of parallel computing and run-time support systems, and selected applications. Typically offered Fall Spring Summer. Course Learning Outcomes 1. Understand the motivation for parallel programming due to multi-core and many-core architectures. 2. Know fundamental principles of parallel programming. 3. Understand performance issues due to various parallel architectures. 4. Implement shared memory parallel algorithms using OpenMP. 5. Implement parallel algorithms on a GPU using OpenCL. 6. Implement message passing parallel algorithms using MPI. View Class Schedule