ISTINYE UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
Course Introduction and Application Information
| Course Code: | COE011 | ||||
| Course Name: | Parallel Computing | ||||
| Semester: |
Fall |
||||
| Course Credits: |
|
||||
| Language of instruction: | English | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
|
||||
| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. AMIR SEYYEDABBASI | ||||
| Course Lecturer(s): | Assist. Prof. Dr. Amir Seyyedabbasi | ||||
| Course Assistants: |
Course Objective and Content
| Course Objectives: | The course aims to provide an overview Parallel Computing concepts, Parallel Programming Skills, Parallel Algorithms and Data Structures, Parallel Computing Architectures. |
| Course Content: | The content of the course consists of basic concepts and principles of parallel computing,earning parallel programming languages (OpenMP,MPI, CUDA),techniques for optimizing the performance of parallel programs, learn about the design and analysis of parallel algorithms for solving common computational problems. |
Learning Outcomes
|
The students who have succeeded in this course;
1) Can use parallel concepts of performance, speed-up and efficiency. 2) Can analyze basic parallel algorithms and use them for programming purposes. 3) Can write Distributed Memory Programs using MPI. 4) Can write Shared Memory Programs using OpenMP and Pthreads. 5) Can write GPU-based parallel programs using CUDA. |
Course Flow Plan
| Week | Subject | Related Preparation |
| 1) | Introduction to parallel computing, general principles, taxonomy | |
| 2) | Parallel Computing Architectures, Hardware and Software 1 | |
| 3) | Parallel Computing Architectures, Hardware and Software 2 | |
| 4) | Distributed Memory Programming with MPI (1) | |
| 5) | Distributed Memory Programming with MPI (2) | |
| 6) | Paralel Partitioning Strategies | |
| 7) | Load Balancing | |
| 8) | Midterm Exam | |
| 9) | Programming Shared Memory with Pthreads | |
| 10) | Shared memory programming -1 (OpenMP) | |
| 11) | Shared memory programming -2 (OpenMP) | |
| 12) | GPU programming with CUDA | |
| 13) | GPU programming with CUDA | |
| 14) | Term Project Presentations |
Sources
| Course Notes / Textbooks: | An Introduction to Parallel Programming 2nd Edition. Peter S. Pacheco, Matthew Malensek Introduction to Parallel Computing, A. Grama, A. Gupta, G.Karypis, V. Kumar, Addison Wesley. |
| References: | Parallel Computing, Theory and practice, M.J.Quinn, McGraw Hill. Parallel programming with MPI, P.S. Pacheco.Morgan Kaufmann. GPU Gems 1&2, Nvidia. |
Course - Program Learning Outcome Relationship
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes |
Course - Learning Outcome Relationship
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution |
Assessment & Grading
| Semester Requirements | Number of Activities | Level of Contribution |
| Project | 1 | % 30 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| total | % 100 | |
Workload and ECTS Credit Calculation
| Activities | Number of Activities | Preparation for the Activity | Spent for the Activity Itself | Completing the Activity Requirements | Workload | ||
| Course Hours | 13 | 0 | 2 | 26 | |||
| Laboratory | 13 | 0 | 2 | 26 | |||
| Study Hours Out of Class | 13 | 2 | 26 | ||||
| Project | 1 | 25 | 1 | 26 | |||
| Midterms | 1 | 10 | 2 | 12 | |||
| Final | 1 | 10 | 2 | 12 | |||
| Total Workload | 128 | ||||||