Parallel and Distributed Processing WM-I-PRR-EN

The subject is intended to familiarize participants with the broad issues of distributed systems distributed systems and parallel processing. The purpose of the course is to present issues related to distributed systems: communication between processes, replication and consistency, virtual and real time, and fault tolerance and atomic transactions. The subject also touches on aspects of parallel processing: architecture of computing centers and the cloud, the concept of clusters, the Hadoop system, the Map-Reduce model, the RDD model and Spark, the Scala language.

(in Polish) Dyscyplina naukowa, do której odnoszą się efekty uczenia się

information and communication technology

(in Polish) E-Learning

Term 2023/24_Z:

(in Polish) E-Learning

Term 2021/22_Z:

(in Polish) E-Learning (pełny kurs) z podziałem na grupy

(in Polish) Grupa przedmiotów ogólnouczenianych

(in Polish) nie dotyczy

(in Polish) Opis nakładu pracy studenta w ECTS

Lecture Participation in the lecture: 30 hours Project preparation: 20 hours Consultations: 6 hours Total 56 hours, which corresponds to 2 ECTS Lab Participation in the laboratory: 30 hours Preparation of tasks: 40 hours Consultations: 6 hours A total of 76 hours, which corresponds to 3 ECTS

Subject level

elementary

Learning outcome code/codes

lecture I2_W01, I2_W03 lab I2_U02, I2_U03, I2_U08, I2_U09, I2_U11, I2_K02

Type of subject

obligatory

Preliminary Requirements

Operating systems, computer networks

Course coordinators

Term 2023/24_Z:

Dorota Dąbrowska
Andrzej Duda

Term 2021/22_Z:

Andrzej Duda

Learning outcomes

Lecture

W1 The student has a basic knowledge of distributed systems and parallel processing.

W2 The student knows the issues of multithreaded operation, socket and gRPC interface, critical section algorithms, time synchronization, replication principles, consistency, fault tolerance and transaction validation algorithms.

W3 The student knows the architecture of computing centers and cloud, Hadoop system and HDFS,
Map-Reduce and RDD model, Spark and Scala language.

Labs

U1 The student can program distributed applications using socket and gRPC interface.

U2 The student can program parallel applications on Hadoop using Map Reduce and Sparc.

U3 The student is able to adapt to the latest technologies in distributed systems and parallel processing using the literature.

K1 The student understands the basic principles of distributed systems and parallel processing and can adapt to the latest technologies in this area.

K2 The student is ready to cooperate with specialists in the field of distributed systems and parallel processing..

Assessment criteria

For all learning outcomes, the following assessment criteria are adopted for all forms of verification:
grade 5: fully achieved (no obvious shortcomings),
grade 4.5: achieved almost fully and criteria for awarding a higher grade are not met,
grade 4: largely achieved and the criteria for a higher grade are not met,
grade 3.5: largely achieved - with a clear majority of positives - and the criteria for granting a higher grade are not met,
grade 3: achieved for most of the cases covered by the verification and criteria for a higher grade are not met,
grade 2: not achieved for most of the cases covered by the verification.

Additional information

Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system:

Description of WM-I-PRR-EN in USOSweb