Faculty of Technical Sciences

Subject: Selected Topics from High Performance Computing and Its Applications in Data Science (17.DRNI22)

General information:
 
Category Scientific-professional
Scientific or art field Applied Computer Science and Informatics
ECTS 10

Acquiring advanced knowledge in the area of high performance computing and its applications in data science.

Enabling students to analyse existing approaches and solutions in the area of high performance computing, as well as introducing them to various applications of the state-of-the-art high performance computing methods for solving problems in data science.

Modern approaches and methods in high performance computing. Modern heterogeneous processors and their programming. Performing general purpose algorithms on graphics processing units (GPGPU).Modern approaches and methods for storage and analysis of large datasets using high performance computer systems. Applications of high performance computing in data science – generating knowledge, visualization, simulation. Independent scientific research study in the field of high performance computing. Analysis and active use of primary scientific resources.

Forms of teaching activities are: lectures, research work, project development, and consultations. Throughout the whole teaching process, students are encouraged to perform intensive communication, critical thinking, independent research work, and active participation in teaching activities. Students are obliged to do the project alone. Writing a research paper is recommended and encouraged.

Authors Title Year Publisher Language
Provost, F., Fawcett, T. Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking 2013 O’Reilly Media, Sebastopol English
N. Matloff Parallel Computing for Data Science: With Examples in R, C++, and CUDA 2015 Chapman&Hall/CRC English
J. Cheng, M. Grossman, T. McKercher Professional CUDA C Programming 2014 Wrox Press English
Eijkhout, V. Introduction to High Performance Scientific Computing 2015 Lulu English
Course activity Pre-examination Obligations Number of points
Oral part of the exam No Yes 50.00
Project Yes Yes 50.00

Assoc. Prof. Kordić Slavica

Associate Professor

Lectures
API Image

Assoc. Prof. Dimitrieski Vladimir

Associate Professor

Lectures

Assoc. Prof. Ivančević Vladimir

Associate Professor

Lectures

Assoc. Prof. Dragan Dinu

Associate Professor

Lectures
API Image

Assoc. Prof. Gajić Dušan

Associate Professor

Lectures

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

Contact:

Address: Trg Dositeja Obradovića 6, 21102 Novi Sad

Phone:  (+381) 21 450 810
(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.