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
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:
Master's degree in Medical Bioinformatics - Enrollment from 2025/2026The 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.
1° Year
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3 courses among the following2° Year activated in the A.Y. 2025/2026
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3 courses among the following| Modules | Credits | TAF | SSD |
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3 courses among the following| Modules | Credits | TAF | SSD |
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3 courses among the following| Modules | Credits | TAF | SSD |
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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.
Parallel programming (2025/2026)
Teaching code
4S009833
Credits
6
Language
English
Also offered in courses:
- Parallel Programming - Parte 1 - teoria of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 1 - lab of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 2 - teoria of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 2 - lab of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 1 - teoria of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 1 - lab of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 2 - teoria of the course Master's degree in Computer Science and Engineering
- Parallel Programming - Parte 2 - lab of the course Master's degree in Computer Science and Engineering
- Embedded AI - PARTE I - Theory of the course Master's degree in Artificial intelligence
- Embedded AI - PARTE I - Laboratory of the course Master's degree in Artificial intelligence
- Embedded AI - PARTE I - Teoria of the course Master's degree in Artificial intelligence
- Embedded AI - PARTE I - Laboratorio of the course Master's degree in Artificial intelligence
Scientific Disciplinary Sector (SSD)
ING-INF/05 - INFORMATION PROCESSING SYSTEMS
Courses Single
Authorized
The teaching is organized as follows:
Part 2 - theory
Part 1 - theory
Part 2 - lab
Part 1 - lab
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 C Programming
Program
Theory
- Intro to parallel and heterogeneous architectures
- Intro to CUDA C
- GPU parallelism model
- Memory and data locality
- Thread Execution Efficiency
- Memory Access Performance
- Parallel Computation Patterns (Histogram)
- Parallel Computation Patterns (Stencil)
- Parallel Computation Patterns (Reduction)
- Parallel Computation Patterns (Scan)
- Floating-Point Considerations
- GPU as Part of the PC Architecture
- Efficient Host-Device Data Transfer
- Application Case Study: Advanced MRI Reconstruction
Lab
CUDA
MPI
Didactic methods
Frontal lessons for theory
Frontal lessons and code development in lab
Learning assessment procedures
Open-ended exercises (total time 2 hours)
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
