Course Name Code Semester T+U Hours Credit ECTS
Parallel Computing BSM 444 8 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Prof.Dr. AHMET ÖZMEN
Course Lecturers
Course Assistants
Course Category
Course Objective This course aims to teach contemporary technology and computing methods of parallel processing. The course covers parallel algorithm design and analysis. It also includes alternative parallel computation methods with their advantages and drawbacks.
Course Content Parallel computation methods, algorithms and parallel architectures. Showing parallel programming languages on some example applications developed for different parallel architectures. Paralel performance measurement and analysis.
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Explains parallel computer systems´ structures and their working principals Lecture, Testing,
2 Calculates parallel computer performance quantitatively Lecture, Testing,
3 Knows inter-networking used in parallel computing Lecture, Question-Answer, Drilland Practice, Testing, Homework,
4 Knows parallel programming methods Lecture, Question-Answer, Drilland Practice, Testing, Homework,
5 Knows GPU programming Lecture, Drilland Practice, Testing, Homework,
6 Knows workload balancing issues at parallel systems Lecture, Drilland Practice, Testing, Homework,
7 Uses parallel performance monitoring tools Lecture, Drilland Practice, Testing, Homework,
Week Course Topics Preliminary Preparation
1 Parallel algorithm design
2 Decomposition techniques, distribution of computational structures and planning (mapping & scheduling)
3 Programming on shared memory systems: OpenMP, Cilk/Cilk++, Pthreads
4 Application development using OpenMP
5 Application development using Cilk/Cilk++
6 Application development using with Pthreads
7 Parallel computer architectures: Shared memory systems and cache coherency, distributed parallel systems, interconnection networks and routing
8 Programming with scalable systems, message passing systems: MPI and PVM
9 Application development using with MPI
10 Application development using with MPI
11 GPU programming with CUDA
12 Application development using with CUDA
13 Parallel performance measurement and analysis
14 Examples of parallel performance measurement and analysis
Course Notes 1. Introduction to Parallel Computing, A. Grama, A. Gupta, G.Karypis, V. Kumar, Addison Wesley.
Course Resources 1. Parallel Computing, Theory and practice, M.J.Quinn, McGraw Hill.
2. Parallel programming wiyh MPI, P.S. Pacheco.Morgan Kaufmann.
3. GPU Gems 1&2, Nvidia.
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions, X
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations X
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently, X
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods, X
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research, X
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies, X
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility, X
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language, X
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology, X
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society, X
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications, X
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems. X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 70
1. Kısa Sınav 10
1. Ödev 10
2. Kısa Sınav 10
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Quiz 2 5 10
Assignment 1 5 5
Final examination 1 15 15
Total Workload 136
Total Workload / 25 (Hours) 5.44
dersAKTSKredisi 5