Scientific computing

Teacher(s)

Absil Pierre-Antoine; Legat Benoît;

Language

English
> French-friendly

Prerequisites

Basic training in numerical methods and programming (level of LEPL1104).

Main themes

• Numerical software in C++ and Python
• Parallel computing
• Numerical methods for partial differential equations

Learning outcomes

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

Contribution of the course to the program objectives (Nr) : AA1.1, AA1.2, AA1.3 AA2.2, AA2.3, AA2.4 AA3.2 AA6.1, AA6.3 After successful completion of this course, the student will be able to: write, modify and use numerical software in C++, Julia et Python; write, modify and use scientific software for partial differential equations; employ parallel programming techniques; profile and optimize the time, memory and power consumption of scientific software. Transversal learning outcomes : Use a reference book in English. Use programming languages for scientific computing. Release software along with suitable user documentation.

Content

• Programming concepts in C++, Julia and Python
• Numerical software engineering in C++, Julia and Python
• Analysis of partial differential equations
• Numerical methods for partial differential equations
• Introduction to accelerating computation on CPU (SIMD) and GPU
• Introduction to parallel computation with OpenMP
• Introduction to distributed computation with MPI
• Other topics related to the course themes.

Teaching methods

• Interactive lectures
• Homework assignments, exercises, or laboratory work under the supervision of the teaching assistants

Evaluation methods

• Work carried out during the term: homework assignments, exercises, or laboratory work. These activities are thus organized (and evaluated) only once per academic year.
• Exam: written, or sometimes oral depending on the circumstances.
• Bonus points for the best projects or for contributions to the open source resources of the course.

The final grade is computed as follows. Let HW be the homework grade (out of 20) and EX the exam grade (also out of 20). If both HW and EX are above 10, we take their average. If HW is below 5 or if EX is below 5, we use min(HW, EX). To interpolate between these cases, we define a weight function f(x) = min(max(0, (x - 5)/10), 0.5). If EX < HW, we compute G1 = f(EX) * HW + (1 - f(EX)) * EX; if HW ≤ EX, we compute G2 = (1 - f(HW)) * HW + f(HW) * EX. In other words, the grade combining homework and exam is min(G1, G2). The bonus points are added on top of this grade (without exceeding 20).

Further information is provided in the "Course outline" document available on Moodle (see "Online resources" below).

Other information

The organisation details are given every year in the course outline.

Online resources

https://moodle.uclouvain.be/course/view.php?id=2951 and https://blegat.github.io/LINMA2710/

Bibliography

• Textbook
• Complementary documents posted on Moodle

Further information is provided in the "Course outline" document available on Moodle.

Faculty or entity

> MAP