High Performance Computing for Science and Engineering

As manufacturing processes approach the physical limits of transistor density, efficient code must exploit parallelism to scale with available computing resources.

Scientific software developers must therefore adopt a think-parallel mindset to solve complex problems across academia, industry, and society.

This course introduces parallel programming and its relationship to computer architectures, with an emphasis on high performance computing.

Students develop experience with programming models such as OpenMP, MPI, and CUDA.