Multicore programming

linfo2355  2021-2022  Louvain-la-Neuve

Multicore programming
5.00 credits
30.0 h + 15.0 h
Main themes
This course treats a specific advanced topic or selection of topics of current research interest in the area of software engineering.
The actual topic(s) may vary from year to year, and will be chosen from a variety of software engineering domains such as data-intensive computing, software analytics, development and analysis of large evolving software systems, big data techniques, software repository mining, software recommendation systems, software visualization, novel programming technologies, software requirements and analysis,model-driven software engineering, software configuration management, software engineering processes, software engineering tools and methods, software testing and quality aspects, etc.
Learning outcomes

At the end of this learning unit, the student is able to :

1 Given the learning outcomes of the "Master in Computer Science and Engineering" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • INFO1.1
  • INFO3.1
  • INFO6.3
Given the learning outcomes of the "Master [120] in Computer Science" program, this course contributes to the development, acquisition and evaluation of the following learning outcomes:
  • SINF1.M3
  • SINF3.1
  • SINF6.3
The students shall acquire advanced theoretical knowledge and technical competences about the topics covered in the course.
This course targets programming models and methods for scalable applications on modern multi-processor and multi-core architectures.

In a first short part, it provides the necessary elements of theory and defines consistency protocols, in order to be able to understand the challenges and tradeoffs associated with shared-memory concurrent programming. The emphasis is on performance and scalability aspects (efficient simultaneous use of multiple cores).

The rest of the course surveys a number of fundamental algorithmic techniques for building shared memory concurrent data structures. It studies the performance implications of these data structures and algorithmic constructs in the context of modern architectures, taking into account various aspects such as the memory and cache hierarchy, hardware consistency protocols, and non-uniform memory accesses (NUMA).

The course is accompanied by a number of practical projects. A multi-core machine is available for the experiments. Students will be able to evaluate the performance and scalability of various algorithms and data structures seen in class.
Teaching methods
  • Lectures
  • Readings and/or short video producing at home
  • Practical sessions
  • Mini project
Evaluation methods
Evaluation methods:
  • Projects and continuous evaluation (40% of the final mark)
  • Exam (60% of the final mark)
The project work is mandatory and cannot be repeated for the second examination session. Project reports will be evaluated only once before the first session and cannot be presented for the second session.
Activities that are evaluated with a formative goal could be evaluated with grades and could replace part or the entirety of the exam in the evaluation mix is the circumstances impose it.
Other information
All relevant course material and slides as well as practical information related to the course will be accessible on Moodle, which will also be the primary means of communication between the teacher(s) and the students.
The Art of Multiprocessor Programming, Maurice Herlihy and Nir Shavit, Morgan Kaufmann. ISBN 978-0-12-370591-4.
UCL library reference 10.620.426
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Data Science Engineering

Master [120] in Computer Science and Engineering

Master [120] in Data Science: Information Technology

Master [120] in Computer Science