Subject

Parallel Processing

Course Title

Parallel Processing
Parallel processing

Code

m23_w_065

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

9 / Winter

Number of ECTS credits

Teacher

Dejan Spasov

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

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

11.

Course content

Fundamental concepts of parallel algorithms. Complexity of parallel algorithms. GPU architecture. Instruction-level parallelism. GPU programming with CUDA and OpenCL. Shared-memory networks and clusters. GRID structures. GRID computations. Performance evaluation and optimization. Architecture for deep learning and capsule networks.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

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

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	20 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

completed activities

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. Yoshiyasu Takefuji | GPU Parallel Computing for Machine Learning in Python: How to Build a Parallel Computer | Amazon Digital Services LLC | 2017
2. L. Abell | ADVANCED NEURAL NETWORKS with MATLAB: DEEP LEARNING, CONTROL SYSTEMS, PARALLEL COMPUTING, and DYNAMIC NEURAL NETWORKS | CreateSpace Independent Publishing Platform | 2017
3. 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