MK EN
ФИНКИ

Subject

Parallel and distributed processing

1. Course Title Parallel and distributed processing
Parallel and distributed processing
2. Code F23L3W037
3. Study Programme Computer Science, Cloud Computing
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) First Cycle
6. Academic year / semester 5 / Winter
7. Number of ECTS credits 6
8. Teacher Magdalena Kostoska Gjorgchevska, Marjan Gušev, Vladimir Zdravesk
9. Prerequisites for enrolling in the course Algorithms and Data Structures or Applied Algorithms and Data Structures
10. Objectives of the course programme (competences) The goal of the course is for students to master the methods of parallel and distributed processing, the possibilities for parallelizing a sequential program, distributed processing of large amounts of data, and the problems that must be addressed in that process.
11. Course content Lectures:
1. Introduction
2. Enabling Technologies and Distributed System Models
3. Basic concepts of parallel and distributed architectures
4. Parallel communications
5. Parallel Algorithms
6. Clusters
7. Colloquium 1
8. Clusters - Supplement
9. Grid
10. Big Data and Distributed File Systems
11. MapReduce concepts and implementations (Hadoop)
12. MapReduce concepts and implementations (Hadoop) - supplement
13. Cloud Computing - support for parallel and distributed processing
14. Colloquium 2

Practical Classes:
1.
2. Projects - Introduction
3. Introduction to MPI
4. Examples with MPI
5. Examples of multithreaded programming
6. Technologies and Interconnections
7.
8. Examples of horizontal and vertical scaling
9. Resource Management
10. Hadoop - Introduction
11. Simple examples with Hadoop
12. Examples with Hadoop
13. Advanced Hadoop Capabilities
14.
12. Learning methods Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper, and learning in an electronic environment (forums, consultations).
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 30 + 45 + 15 + 15 + 75 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 30 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 45 hours
16. Other forms of activities
16.1. Project assignments 15 hours
16.2. Independent assignments 15 hours
16.3. Home study 75 hours
17. Assessment method
17.1. Tests 10 points
17.2. Seminar paper / project (presentation: written and oral) 15 points
17.3. Activities and learning 10 points
17.4. Final exam 70 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam Activities 15.2 and 16.1 completed
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. K. Hwang, G. Fox and J. Dongarra | Distributed and Cloud Computing | Morgan Kaufmann | 2011
2. Andrew S. Tanenbaum | Distributed Systems: Principles and Paradigms” | Prentice Hall | 2007
3. T. Rauber, G. Runger | Parallel Programming for Multicore and Cluster Systems | Springer | 2009
4. Holden Karau, Andy Konwinski, Patrick Wendell, Matei Zaharia | Learning Spark: Lightning-Fast Big Data Analysis | O'Reilly | 2015
5. Donald Miner, Adam Shook | MapReduce Design Patterns | O'Reilly | 2013
6. Kai Hwang, Jack Dongarra, Geoffrey C. Fox | Distributed and Cloud Computing: From Parallel Processing to the Internet of Things | Morgan Kaufman | 2013
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects