COURSE UNIT TITLE  COURSE UNIT CODE  SEMESTER  THEORY + PRACTICE (Hour)  ECTS 
PARALLEL PROGRAMMING 
BİL629 
 
3 + 0 
10 
TYPE OF COURSE UNIT  Elective Course 
LEVEL OF COURSE UNIT  Doctorate Of Science 
YEAR OF STUDY   
SEMESTER   
NUMBER OF ECTS CREDITS ALLOCATED  10 
NAME OF LECTURER(S)  

LEARNING OUTCOMES OF THE COURSE UNIT 
At the end of this course, the students; 1) Learn fundamental concepts of parallel programming. 2) Learn parallelism, their principles and structures. 3) Understand the basics of parallel machine structure. 4) Learn parallel algorithm design, analyze and implementation. 5) Understand the possible limitations of parallel processing.

MODE OF DELIVERY  Face to face 
PREREQUISITES OF THE COURSE  No 
RECOMMENDED OPTIONAL PROGRAMME COMPONENT  None 
COURSE CONTENTS  WEEK  TOPICS 

1^{st} Week  Parallel programming techniques.  2^{nd} Week  Parallel programming techniques.  3^{rd} Week  Parallel programming techniques.  4^{th} Week  Parallel programming techniques.  5^{th} Week  Classification of parallel processing systems.  6^{th} Week  Classification of parallel processing systems.  7^{th} Week  Classification of parallel processing systems.  8^{th} Week  Midterm  9^{th} Week  Parallel computer architectures.  10^{th} Week  Parallel computer architectures.  11^{th} Week  Parallel computer architectures.  12^{th} Week  Parallel computer architectures.  13^{th} Week  A comprehensive study of basic techniques: Parallel Computer Models; MessagePassing Computing; Pipelined Computations; Programming with Shared Memory; Algorithms and Applications connected with Parallel Processing.  14^{th} Week  A comprehensive study of basic techniques: Parallel Computer Models; MessagePassing Computing; Pipelined Computations; Programming with Shared Memory; Algorithms and Applications connected with Parallel Processing. 

RECOMENDED 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(%) 

Midterm  1  30  Quiz  3  15  Project  1  15  Total(%)   60  Contribution of Interm Studies to Overall Grade(%)   60  Contribution of Final Examination to Overall Grade(%)   40  Total(%)   100 

LANGUAGE OF INSTRUCTION  Turkish 
WORK PLACEMENT(S)  No 
 
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO) 
 LO1  LO2  LO3  LO4  LO5  K1     X   K2   X  X    K3     X   K4     X   K5    X  X   K6   X     K7  X   X    K8   X     K9   X     K10     X   K11     X   K12     X   