Studying at the University of Verona

Here you can find information on the organisational aspects of the Programme, lecture timetables, learning activities and useful contact details for your time at the University, from enrolment to graduation.

Study Plan

A.A. 2024/2025 A.A. 2023/2024 A.A. 2022/2023 A.A. 2021/2022 A.A. 2020/2021 A.A. 2019/2020 A.A. 2018/2019 A.A. 2017/2018 A.A. 2016/2017

This information is intended exclusively for students already enrolled in this course.
If you are a new student interested in enrolling, you can find information about the course of study on the course page:

Laurea magistrale in Medical bioinformatics - Enrollment from 2025/2026

The Study Plan includes all modules, teaching and learning activities that each student will need to undertake during their time at the University.
Please select your Study Plan based on your enrollment year.

activated in the A.Y. 2024/2025
ModulesCreditsTAFSSD
3 courses among the following
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
INF/01
6
B
ING-INF/05
6
B
ING-INF/05
Further linguistic skills (C1 English suggested)
3
F
-
Stages
3
F
-
Final exam
24
E
-
Modules Credits TAF SSD
Between the years: 1°- 2°
12
D
-

Legend | Type of training activity (TTA)

TAF (Type of Educational Activity) All courses and activities are classified into different types of educational activities, indicated by a letter.

A Basic activities
B Characterizing activities
C Related or complementary activities
D Activities to be chosen by the student
E Final examination
F Other training activities
S Placements in companies, public or private institutions and professional associations

Parallel programming (2024/2025)

Teaching code

4S009833

Credits

6

Coordinator

Nicola Bombieri

Language

English en

Also offered in courses:

Scientific Disciplinary Sector (SSD)

ING-INF/05 - INFORMATION PROCESSING SYSTEMS

Courses Single

Authorized

MoodleMoodle Seminars 0

The teaching is organized as follows:

Part 2

Credits

3

Period

Semester 2

Academic staff

Nicola Bombieri

Lessons timetable
Part 1

Credits

3

Period

Semester 2

Academic staff

Nicola Bombieri

Lessons timetable

Learning objectives

This course aims at providing theoretical and practical knowledge about programming and analysis of advanced computational architectures, with emphasis on multiprocessor and GPU platforms. At the end of the course the student will have to demonstrate the ability to apply the knowledge necessary to: identify techniques for parallel programming, also in a research context, through analysis of application efficiency and by considering both functional and non-functional design constraints (correctness, performance, energy consumption). This knowledge will allow the student to be able to analyze performance and to perform code profiling, by identifying critical zone and the corresponding optimizations by considering the architectural characteristics of the platform. At the end of the course the student will be able to compare parallel patterns and to select the best one by considering the use case; by defining the structure of the optimized code, demonstrate the ability to identify the proper architectural choices, by considering the target application and platform contexts. During the definition of the optimized code structure, the student will have the ability to continue the study autonomously in the field of the parallel programming languages and of the Software development for parallel embedded platforms.

Prerequisites and basic notions

Basic programming in C

Program

Theory:
- Parallel architectures
- Parallel programming models
- Performance measurement
- Perspective on Parallel Programming
- Designing parallel programs
- GPUs and CUDA:
overview , parallel programming model, threads
memory hierarchy/model
performance considerations
optimizations
- Graph algorithms on GPUs
data representations: Adj. matriX/lists, edge lists
Parallel algorithms for graph traversal (BFS)
Parallel algorithms for graph analysis (SSSP, APSP)
Parallel algorithms for graphs: load balancing and memory accesses: issues and management
Lab:
- OpenMP
- MPI
- CUDA

Bibliography

Vai alla bibliografia
Visualizza la bibliografia con Leganto, strumento che il Sistema Bibliotecario mette a disposizione per recuperare i testi in programma d'esame in modo semplice e innovativo.

Didactic methods

Frontal lessons for theory
Frontal lessons and code development in lab

Learning assessment procedures

Exercises with open answers (total time 2 h)

Students with disabilities or specific learning disorders (SLD), who intend to request the adaptation of the exam, must follow the instructions given HERE

Evaluation criteria

To pass the exam, the student has to demonstrate:
- he/she has understood the principles related to the parallel programming
- he/she is able to describe the concepts in a clear and exhaustive way without digressions
- he/she is able to apply the acquired knowledge to solve application scenarios described by means of exercises, questions and projects.

Criteria for the composition of the final grade

The exam consists of a written test, which contains questions with multiple answers, questions with open answers, and exercises related both the theoretical and lab modules. The max score of each exercise depends on the exercise complexity. The sum of max scores is 32/30. The student can elaborate a project assigned by the teacher for a bonus (up to +5 points).

Exam language

English