Subject

Modern Trends in Parallel Processing

Course Title

Modern Trends in Parallel Processing
Contemporary trends in parallel processing

Code

m23_s_057

Study Programme

—

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

10 / Summer

Number of ECTS credits

Teacher

Vladimir Zdraveski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

This course is intended to acquire skills and knowledge about contemporary trends in
Parallel processing, grids, high-performance computing and computing
in the cloud.
The subject has theoretical and practical applications aimed at leveraging the performance of
existing architectures and to provide opportunities for the introduction of parallelism
calculation.
Competencies
Upon successful completion of this course, the student will be able to:
Knowledge and understanding – The student will be able to:
 to clearly understand the impact of abstraction, modeling, and the application of
parallel processing;
 to critically discuss and investigate the key concepts of systems for
parallel computation based on shared-memory architectures and
distributed systems, models, methods and techniques;
 to critically discuss and explore the architectural and design
possibilities, with the ability to generate the most appropriate method and technique for
Utilization of the system's performance;
Intellectual Skills - Students will be able to:
 analyze different parallel architectures and identify the essential
relevant characteristics
 find similarities and differences between the various techniques for parallel
programming
 to apply practical skills and demonstrate knowledge of programming by applying
parallel processing.
Practical skills - The student will be able to:
 used the well-known parallel methods and techniques for programming;
 found a parallel solution with the best performance for the posed problems;
 Set up, configure, and use a cloud computing system

11.

Course content

Parallel Processing Architectures
 Performance and characteristics of parallel systems
 Algorithms and structures for parallel processing
 Concepts of parallel processing and performance
 Competitive processes
Examples of programs that utilize the performance of multiple cores
 Parallel programming of shared-memory processors with OpenMP
 Parallel Programming with Distributed MPI Processors
 Processor fields and algorithm mappings, systolic arrays, processor
fields, data-flow systems

12.

Learning methods

Lectures, exercises, independent work, project assignments, seminar papers

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 30 + 30 + 45 + 45 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	30 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	45 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	45 points
17.3.	Activities and learning	0 points
17.4.	Final exam	0 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

completion of activities 15.1 and 15.2

20.

Language of instruction

Macedonian or English

21.

Method for monitoring the quality of teaching

internal evaluation and surveys, according to the model explained above

22.

Literature

22.1.	Required literature 1.
22.2.	Additional literature No. Author Title Publisher Year