This is a collaborative, online course with UIUC (University of Illinois) Blue Water Projects. PACE reserch scientists will serve as local instructors for holding  the class, discussion, answering the questions, and grading the assignment and final exams. 

Course Description:

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, solver libraries, and execution frameworks. 

The overall goal is to acquaint students who anticipate doing independent work that may benefit from large-scale simulation with current hardware, software tools, practices, and trends in parallel scientific computing, and to provide an opportunity to build and execute sample parallel codes. The software employed in course examples is freely available. The course is also designed to make students intelligent consumers and critics of parallel scientific computing literature and conferences. 

The principal lecturer will be David Keyes, Professor of Applied Mathematics and Computational Science, KAUST. Guest lecturers will be invited to speak on their specialties. Lectures from Extreme Computing Research Center staff members highlighting open source scientific software will be incorporated into the course. 

Lecture coverage includes:

• Introduction to large-scale predictive simulations: the combined culture of CS&E and HPC

• Introduction to parallel architecture and programming models

• Introduction to MPI, PETSc, and other software frameworks for HPC

• Parallel algorithms for the solution of large, sparse linear systems and nonlinear systems with large, sparse Jacobians

• Parallel algorithms for partial differential equations

• Parallel algorithms for N-body particle dynamics 

This a great opportunity to adapt HPC skills from a well-known icon David Keyes as well as to use one of the most powerful supercomputers in the world. 

This course opens to all GT students and researchers.  For more details, please check course website http://cse8803hnc.gatech.edu/