| Course Type | Elective Course |
|---|
| Course Level | Master's Degree Without Thesis |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 10 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Learn fundamental concepts of parallel programming.
- Learn parallelism, their principles and structures.
- Understand the basics of parallel machine structure.
- Learn parallel algorithm design, analyze and implementation.
- Understand the possible limitations of parallel processing.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | Parallel programming techniques. Classification of parallel processing systems. Parallel computer architectures. A comprehensive study of basic techniques: Parallel Computer Models; Message-Passing Computing; Pipelined Computations; Programming with Shared Memory; Algorithms and Applications connected with Parallel Processing. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Parallel programming techniques. |
|
2nd Week |
Parallel programming techniques. |
|
3rd Week |
Parallel programming techniques. |
|
4th Week |
Parallel programming techniques. |
|
5th Week |
Classification of parallel processing systems. |
|
6th Week |
Classification of parallel processing systems. |
|
7th Week |
Classification of parallel processing systems. |
|
8th Week |
Mid-term |
|
9th Week |
Parallel computer architectures. |
|
10th Week |
Parallel computer architectures. |
|
11th Week |
Parallel computer architectures. |
|
12th Week |
Parallel computer architectures. |
|
13th Week |
A comprehensive study of basic techniques: Parallel Computer Models; Message-Passing Computing; Pipelined Computations; Programming with Shared Memory; Algorithms and Applications connected with Parallel Processing. |
|
14th Week |
A comprehensive study of basic techniques: Parallel Computer Models; Message-Passing Computing; Pipelined Computations; Programming with Shared Memory; Algorithms and Applications connected with Parallel Processing. |
|
| Recommended or Required Reading | 1. Introduction to Parallel Computing, V. Kumar, A. Grama, A. Gupta and G. Karypis, V. Kumar (second edition), 2003
2. Addison Wesley Parallel Programming with MPI, P. Pacheco, Morgan Kaufmann Publishers, Inc., 1997.
3. MPI Related Materials Scalable Parallel Computing, Kai Hwang and Zhiwei Xu, 2000
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Questions/Answers
- Presentation
- Experiment
- Practice
- Problem Solving
- Project
- Report Preparation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Quiz |
3 |
15 |
| Project |
1 |
15 |
| Total(%) |
|
60 |
| Contribution of In-term Studies to Overall Grade(%) |
|
60 |
| Contribution of Final Examination to Overall Grade(%) |
|
40 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
2 |
2 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
11 |
154 |
| Preparation for Final exam |
1 |
69 |
69 |
| Course hours |
14 |
3 |
42 |
| Preparation for Midterm exam |
1 |
44 |
44 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
2 |
2 |
| Homework |
|
|
|
| Total Work Load | 313 |
| Total Work Load / 30 (h) | 10.43 |
| ECTS Credit of the Course | 10 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
