Parallel computing principles, architectures, and technologies. Parallel application development, performance, and scalability. Designed to prepare students to formulate and develop parallel algorithms to implement effective applications for parallel computing systems. Three lecture hours a week for one semester. Statistics and Data Sciences 394C and Statistics and Scientific Computation 394C may not both be counted. Prerequisite: Graduate standing, a third-semester calculus course or the equivalent, a linear algebra course or a matrices course, and prior programming experience using C or Fortran on Linux or Unix systems.