Prerequisites: Experience with Linux and C/C++ and familiarity with basic discrete and numerical algorithms. High performance computing algorithms and software technology, with an emphasis on using distributed memory systems for scientific computing. Theoretical and practically achievable performance for processors memory system, and network, for large-scale scientific applications. The state-of-the-art and promise of predictive computational science and engineering. Algorithmic kernels common to linear and nonlinear algebraic systems, partial differential equations, integral equations, particle methods, optimization and statistics. Computer architecture and the stresses put on scientific applications and their underlying mathematical algorithms by emerging architecture. State-of-the-art discretization techniques, solve libraries and execution frameworks.