Parallel processing

Parallel processing

1.

Subject title

Parallel processing

Паралелно процесирање

2.

Code

m23_w_065

3.

Study program

Cloud Computing, IT management, Bioinformatics, Data science in computer science and engineering, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Inteligent Systems, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Software for embedded systems, Software Engineering, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Зимски

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Дејан Спасов

9.

Prerequisites for enrollment

10.

Subject goals and competencies:


After completing the course, the student is expected to have knowledge of parallel algorithms; parallel architectures; multithreading systems. To be able to create parallel applications and to be able to implement optimal deep learning algorithms

11.

Subject content:


Fundamental concepts of parallel algorithms. Complexity of parallel algorithms.. GPU architecture. Instruction-level parallelism. GPU Programming with CUDA and OpenCL. Coupling networks and clusters. GRID structures. GRID calculations. Performance benchmarking and optimization. Deep Learning Architecture and Capsule Networks.

12.

Learning methods:


Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

20 points

17.2.

Seminar work / project (presentation: written and oral)

50 points

17.3.

Activities and learning

20 points

17.4.

Final exam

20 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.

Condition for signature and taking final exam

реализирани активности

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

No.

Author

Title

Publisher

Year

7978

Yoshiyasu Takefuji

GPU parallel computing for machine learning in Python: how to build a parallel computer

Amazon Digital Services LLC

2017

7979

L. Abell

ADVENCED NEURAL NETWORKS with MATLAB: DEEP LEARNING, CONTROL SYSTEMS, PARALLEL COMPUTING, and DYNAMIC NEURAL NETWORKS

CreateSpace Independent Publishing Platform

2017

7980

Shane Cook

CUDA Programming: A Developer`s Guide to Parallel Computing with GPUs (Applications of Gpu Computing)

Morgan Kaufmann

2012

22.2.

Additional literature

No.

Author

Title

Publisher

Year