High Performance Computing for the Molecular Sciences

3 credits · Graduate

This course introduces parallel programming concepts commonly encountered in computational molecular sciences codes, including distributed-memory parallelization, shared-memory parallelization, and GPU parallelization. Students will become familiar with MPI, OpenMP, and CUDA, while also learning specific strategies for addressing performance challenges associated with key computational chemistry algorithms. Special emphasis is placed on the execution of machine learning techniques within a high performance computing environment, as well as challenges arising from processing large amounts of data.