Parallel and Distributed Numerical Algorithms and Tools
Information Systems Engineering.
Institute of Natural and Applied Sciences
Third Cycle (Doctorate Degree)
| Course Unit Title | Course Unit Code | Type of Course Unit | Level of Course Unit | Year of Study | Semester | ECTS Credits |
|---|---|---|---|---|---|---|
| Parallel and Distributed Numerical Algorithms and Tools | BTM602 | Elective | Doctorate degree | 1 | Fall | 8 |
Name of Lecturer(s)
Prof. Dr. Hikmet Hakan GÜREL
Associate Prof. Dr. Süleyman EKEN
Research Assistant Seda BALTA
Learning Outcomes of the Course Unit
1) Knows how to use numerical algorithms and tools in parallel and distributed environments.
2) Knows MPI programming
3) Knows Hybrid programming: OpenMP + MPI
4) Knows Direct and Iterative Methods
5) Knows parallel algorithms for Ordinary and Partial Differential Equations.
Program Competencies-Learning Outcomes Relation
| Program Competencies | ||||||||||||||
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | ||
| Learning Outcomes | ||||||||||||||
| 1 | Middle | Middle | Middle | Middle | High | High | High | Middle | Middle | Middle | Middle | High | High | |
| 2 | Middle | Middle | Middle | Middle | High | High | High | Middle | Middle | Middle | Middle | High | High | |
| 3 | Middle | Middle | Middle | Middle | High | High | High | Middle | Middle | Middle | Middle | High | High | |
| 4 | Middle | Middle | Middle | Middle | High | High | High | Middle | Middle | Middle | Middle | High | High | |
| 5 | Middle | Middle | Middle | Middle | High | High | High | Middle | Middle | Middle | Middle | High | High | |
Mode of Delivery
Face to Face
Prerequisites and Co-Requisites
None
Recommended Optional Programme Components
Not Required
Course Contents
Message passing computing (MPI): Communicators and groups, derived data types, virtual topologies, parallel I / O, remote memory access and dynamic memory management. Graph theory. Area parsing and graphic segmentation algorithms. Numerical Algorithms: Canon, Fox, DNS Algorithms. Direct Methods: Gauss Elimination, LU Decomposition, QR factoring. Recursive methods: Jacobi, Gauss-Seidel, SOR. Red-Black ranking. Thomas algorithm. Parallel algorithms for Ordinary and Partial Differential Equations: Multiple-staining algorithms. Hybrid programming: OpenMP + MPI. Non-stationary iterative methods: CG and GMRES.
Recommended or Required Reading
Planned Learning Activities and Teaching Methods
Assessment Methods and Criteria
Language of Instruction
Turkish
Work Placement(s)
Required