This course is a collaborative, online course with UIUC (University of Illinois) Blue Waters project. The instructor for the course is Dr. David E. Keyes, Director of the Extreme Computing Research and Founding Dean of the Mathematical and Computer Sciences and Engineering Division at King Abdullah University of Science and Technology (KAUST), who is very famous in High Performance Computing Community.  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.

This course introduces high performance computing algorithms and software technology, which an emphasis on using distributed memory systems for scientific computing. The topic 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

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.

Office Hours

Local Instructors	Prof. Fang (Cherry) Liu Dr. Mehmet Belgin Dr. Blake Fleischer	Thursday 1:00-2:00pm on webex
Class meets	Mon, Wed 4:30 - 6:00, Van Leer Room C457

Prerequisites

This course is designed to be entry level graduate course as well as senior level undergraduate course, and it opens to all GT students. 

Expectations of the students:

• Students will need their own laptop or desktop system
• Students are expected to view the recorded lectures on class meeting time
• Students will be asked to submit quizzes for self-assessment purposes
• Students will be asked to submit a mid-term and a final exam for determining 

Schedule (tentative)

Date		Topic	Mon	Wed	Events
Aug	22, 24	* Course syllabus * Course survey * Course introduction Introduction to Parallelism	slides video resource list: A, B, C	slides 1, 2 video resource list: A, B, C, D, E, F	problem set 1 is out reading list: A, B, C, D, E, F
Aug	29, 31	The Versatile Laplacian Computational Science & Engineering	Lecture 3	Lecture 4	problem set 1 is due (Wednesday 11:55pm)
Sep	5,7	Structure and Unstructured Grids	Holiday	Lecture 5	problem set 2 is out A, B, C, D, E
Sep	12,14	Structure and Unstructured Grids Introduction to PETSc	Lecture 6	Lecture 7
Sep	19,21	Nonlinear Root Finding	Lecture 8	Lecture 9	problem set 3 is out
Sep	26,28	Nonlinear Root Finding Fast Multipole Methods	Lecture 10	Lecture 11	problem set 2 is due (Wednesday 11:55pm) Term project is assigned
Oct	3,5	Direct Solver for Dense Linear System	Lecture 12	Lecture 13	problem set 3 is due (Wednesday 11:55pm)
Oct	10,12	BlueWater Cluster Usage	Fall Break	Lecture 14	Term project proposal is due in email (Wednesday 11:55pm)
Oct	17,19	Direct Solver for Sparse Liner System	Lecture 15	Lecture 16
Oct	24,26	MidTerm review Algorithmic Adaptations to Exascale	Lecture 17	Lecture 18	problem set 4 is out bluewaters usage
Oct	31,2		Lecture 19
Nov	7,9	Course review example Multigrid Method	Lecture 20	Lecture 21	problem set 4 is due (Wednesday 11:55pm)
Nov	14,16		Lecture 22	Lecture 23	Term project progress report is due in email (Monday 11:55pm)
Nov	21		Lecture 24
Nov	28		Lecture 25	Term Project Presentation/Final review	3 slides for 5 mins presentation is due on Tuesday 11:55pm, use default black and white scheme in PowerPoint. Term project is due Wednesday 11:55pm,suggested format is IEEE report format template
Dec	5		Final Exam

Grading

Late Submissions Policy

• No late homework allowed. However, there are no penalties for medical reasons or emergencies. You must submit a doctor's note or an official letter explaining the emergency.

Textbooks, references, and reading materials

• Introduction to High Performance Scientific Computing, by V. Eijkhout (Creative Commons, 2015)
• Using MPI; Portable Parallel Programming with the Message-Passing Interface, Third Edition, by W. D. Gropp et al. (MIT Press, 2014)
• Introduction to High Performance Computing for Scientists and Engineers, by G. Hager and G. Wellein (CRC Press, 2011)
• Applied Parallel Computing, by Y. Deng (World Scientific, 2011)
• Petascale Computing: Algorithms and Applications, by D. Bader, ed. (Chapman and Hall, 2008)
• Scientific Parallel Computing, by L. R. Scott et al. (Princeton, 2005)
• An Introduction to Parallel Computing: Design and Analysis of Algorithms,2^nd ed., by A. Grama et al. (Pearson Addison Wesley, 2003)
• Sourcebook of Parallel Computing by J. Dongarra et al., eds. (Morgan-Kaufmann, 2002)
• Parallel Computer Architecture: A Hardware/Software Approach by D. E. Culler et al. (Morgan Kaufmann, 1999)
• High Performance Computing, K. Dowd and C. Severance (O'Reilly, 1998)