Master thesis : Discrete Element Simulation of Ice Particle Interaction: Migration to GPU Computing and Subsequent Validation
Bristy, Kaniz Fatema
Promoteur(s) : Hisette, Quentin
Date de soutenance : 15-sep-2022 • URL permanente : http://hdl.handle.net/2268.2/16560
Détails
Titre : | Master thesis : Discrete Element Simulation of Ice Particle Interaction: Migration to GPU Computing and Subsequent Validation |
Auteur : | Bristy, Kaniz Fatema |
Date de soutenance : | 15-sep-2022 |
Promoteur(s) : | Hisette, Quentin |
Membre(s) du jury : | Ferrant, Pierre
Bonnefoy, Félicien |
Langue : | Anglais |
Nombre de pages : | 51 |
Mots-clés : | [en] DEM, GPU, CUDA, Time consumption |
Discipline(s) : | Ingénierie, informatique & technologie > Ingénierie mécanique |
Public cible : | Chercheurs Professionnels du domaine Etudiants Grand public Autre |
Institution(s) : | Université de Liège, Liège, Belgique |
Diplôme : | Master : ingénieur civil mécanicien, à finalité spécialisée en "Advanced Ship Design" |
Faculté : | Mémoires de la Faculté des Sciences appliquées |
Résumé
[en] A numerical simulation code using Discrete Element Method has been developed by HSVA,
which can generate brash ice and ice ridges as well as analyse ship navigation through ice
channels. The current version is simulating the problem in model scale for ease of validation.
This thesis aims to enhance the software's capabilities, reduce the computational time, and to
enhance performance and capabilities by modifying internal source code.
To improve the performance GPU programming has been introduced. GPU programming
extension CUDA, developed by NVIDIA, has led to numerous advances in computing over the
last few years. The CUDA API makes it relatively easy for users to access the graphics card
hardware, which allows users to perform parallel computations with thousands of CUDA cores.
The following report investigates the methodology and advantages of using the CUDA API for
DEM computations. In order to achieve this, existing CPU code had to be rewritten for the
GPU. Both implementations show significant improvements with regard to iteration time, and
performance depending on of GPU architecture.
Additionally, it has been demonstrated that the GPU can be sped up by simply varying certain
parameters, which boosts the code's performance overall. Another investigation dives into the
overhead associated with programming memory intensive scripts to the GPU and shows what
effect this has on the total calculation times for the application. Further a more complex IceStructure interaction algorithm can improve the quality of results.
In this case a different number of simulations is done varying the element number to find out
the dependency of the elements to the computational time. Eventually, several tests were
conducted for different types of brash ice and Ice ridge channels to see how the software would
react.
Fichier(s)
Document(s)
Citer ce mémoire
L'Université de Liège ne garantit pas la qualité scientifique de ces travaux d'étudiants ni l'exactitude de l'ensemble des informations qu'ils contiennent.