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The requested file was not found.
Please click here to go to the home page.
Fastrack to Julia cheatsheet
MATLAB-Julia-Python comparative cheatsheet (by QuantEcon group)
Plots.jl cheatsheet
Numerical Recipes. Useful reference that also contains a lot of references to "classical" literature on numerics.
Numerical Partial Differential Equations: Finite Difference Methods, by J. W. Thomas. Mostly about FDM (parabolic/hyperbolic equations, stability analysis, matrix methods).
Numerical heat transfer and fluid flow, by Patankar. Covers more advanced CFD (Navier-Stokes, pressure-velocity coupling etc.) topics and has intuitive explanations of the finite volume method.
Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, by Hirsh. A comprehensive resource (700-pages) about CFD in particular, not PDE's in general.
Computational Science and Engineering, by G. Strang (MIT). It develops a framework for the equations and numerical methods of applied mathematics.
Computational methods for Geodynamics, by P. Tackley (ETHZ). CFD, Stokes equations and finite-difference method (with application to convection - Geodynamics).
Introduction to Numerical Geodynamic Modelling, by T. Gerya (ETHZ). Diffusion (heat) and Stokes equations derivation and implementation, finite-differences (with application to thermo-mechanical models - Geodynamics).
any further relevant suggestions are welcome - open a PR
Solving differential equations in parallel on GPUs @JuliaCon2021:
Advanced GPU Programming with Julia by Sam Omlin (CSCS) and Tim Besard (JuliaComputing) material
Solving differential equations in parallel with Julia @EGU2021
Solving Nonlinear Multi-Physics on GPU Supercomputers with Julia @JuliaCon2021:
Julia for High-Performance Computing by Carsten Bauer at High Performance Computing Center Stuttgart (HLRS)
Julia language main website
Julia Discourse (Q&A - help)
Fastrack to Julia cheatsheet
MATLAB-Julia-Python comparative cheatsheet (by QuantEcon group)
Plots.jl cheatsheet
Numerical Recipes. Useful reference that also contains a lot of references to "classical" literature on numerics.
Numerical Partial Differential Equations: Finite Difference Methods, by J. W. Thomas. Mostly about FDM (parabolic/hyperbolic equations, stability analysis, matrix methods).
Numerical heat transfer and fluid flow, by Patankar. Covers more advanced CFD (Navier-Stokes, pressure-velocity coupling etc.) topics and has intuitive explanations of the finite volume method.
Numerical Computation of Internal and External Flows: The Fundamentals of Computational Fluid Dynamics, by Hirsh. A comprehensive resource (700-pages) about CFD in particular, not PDE's in general.
Computational Science and Engineering, by G. Strang (MIT). It develops a framework for the equations and numerical methods of applied mathematics.
Computational methods for Geodynamics, by P. Tackley (ETHZ). CFD, Stokes equations and finite-difference method (with application to convection - Geodynamics).
Introduction to Numerical Geodynamic Modelling, by T. Gerya (ETHZ). Diffusion (heat) and Stokes equations derivation and implementation, finite-differences (with application to thermo-mechanical models - Geodynamics).
any further relevant suggestions are welcome - open a PR
Solving differential equations in parallel on GPUs @JuliaCon2021:
Advanced GPU Programming with Julia by Sam Omlin (CSCS) and Tim Besard (JuliaComputing) material
Solving differential equations in parallel with Julia @EGU2021
Solving Nonlinear Multi-Physics on GPU Supercomputers with Julia @JuliaCon2021:
Julia for High-Performance Computing by Carsten Bauer at High Performance Computing Center Stuttgart (HLRS)
Julia language main website
Julia Discourse (Q&A - help)
Final projects will provide 35% of the course grade. We recommend you work in teams of two, but being your own teammate is fine too.
Selecting a project of your choice among 3 possible directions (A/, B/ or C/):
Multi-GPU Navier-Stokes in 3D
Implement simple Navier-Stokes solver using Chorin's projection method and advection based on the method of characteristics. For the projection step, you'll implement a Poisson solver for the pressure. You could use the pseudo-transient solver from class or write your own. Examples would be the multigrid solvers or Fourier-transform-based spectral solvers. The only requirement is that your solver has to work on GPU and scale (reasonably) well. Feel free to take inspiration in the 2D reference implementation developed last year.
Free convection simulation
Use the ParallelStencil.jl miniapp as a starting point to implement your own 3D multi-GPU thermo-mechanical convection solver (lava-lamp).
Water circulation in saline aquifers.
This project share many similarities with the the thermal porous convection. The main difference is that density variations are not due to temperature, but to variable concentration of salt dissolved in the water.
Hydro-mechanical flow localisation
Use the ParallelStencil.jl miniapp as a starting point to implement your own 3D multi-GPU hydro-mechanical "two-phase flow" solver to capture the formation and propagation of solitary waves of porosity.
Wave physics
Elastic wave propagation is central in computational seismology as it allows to "image" the subsurface. It has also application far beyond geosciences. Implement your 3D elastic wave solver using, e.g., as starting point the acoustic wave solver from the ParallelStencil.jl miniapp miniapp.
If you are interested in GPU code optimisation, you can go through the advanced optimisation material (Lecture 10) and, e.g., add shared memory support and manual register queuing to accelerate the 3D thermal porous convection solver from the class or select among the ParallelStencil.jl miniapp miniapps.
Show your creativity by coming up with your own problem that could be modelled using PDEs (e.g. related to another project or future study/research direction of yours). We’ll do our best to help you implementing it. Relativistic MHD? Phase separation in alloys? Electromagnetic waves propagation? Spectral methods for PDEs? Name your own! Ideally, come with papers and equations related to it.
Head to Logistics in order to find infos on getting started and submission.
Final projects will provide 35% of the course grade. We recommend you work in teams of two, but being your own teammate is fine too.
Selecting a project of your choice among 3 possible directions (A/, B/ or C/):
Multi-GPU Navier-Stokes in 3D
Implement simple Navier-Stokes solver using Chorin's projection method and advection based on the method of characteristics. For the projection step, you'll implement a Poisson solver for the pressure. You could use the pseudo-transient solver from class or write your own. Examples would be the multigrid solvers or Fourier-transform-based spectral solvers. The only requirement is that your solver has to work on GPU and scale (reasonably) well. Feel free to take inspiration in the 2D reference implementation developed last year.
Free convection simulation
Use the ParallelStencil.jl miniapp as a starting point to implement your own 3D multi-GPU thermo-mechanical convection solver (lava-lamp).
Water circulation in saline aquifers.
This project share many similarities with the the thermal porous convection. The main difference is that density variations are not due to temperature, but to variable concentration of salt dissolved in the water.
Hydro-mechanical flow localisation
Use the ParallelStencil.jl miniapp as a starting point to implement your own 3D multi-GPU hydro-mechanical "two-phase flow" solver to capture the formation and propagation of solitary waves of porosity.
Wave physics
Elastic wave propagation is central in computational seismology as it allows to "image" the subsurface. It has also application far beyond geosciences. Implement your 3D elastic wave solver using, e.g., as starting point the acoustic wave solver from the ParallelStencil.jl miniapp miniapp.
If you are interested in GPU code optimisation, you can go through the advanced optimisation material (Lecture 10) and, e.g., add shared memory support and manual register queuing to accelerate the 3D thermal porous convection solver from the class or select among the ParallelStencil.jl miniapp miniapps.
Show your creativity by coming up with your own problem that could be modelled using PDEs (e.g. related to another project or future study/research direction of yours). We’ll do our best to help you implementing it. Relativistic MHD? Phase separation in alloys? Electromagnetic waves propagation? Spectral methods for PDEs? Name your own! Ideally, come with papers and equations related to it.
Head to Logistics in order to find infos on getting started and submission.
| Assignment | Due date | Submission | Notes |
|---|---|---|---|
| Lect. 1 | 27.09.2023 - 23h59 CET | ||
| Lect. 2 | 04.10.2023 - 23h59 CET | ||
| Lect. 3 | 11.10.2023 - 23h59 CET | ||
| Lect. 4 | 18.10.2023 - 23h59 CET | ||
| Lect. 5 | 25.10.2023 - 23h59 CET | ||
| Lect. 6 | 01.11.2023 - 23h59 CET | ||
| Lect. 7 | |||
| Lect. 8 | |||
| Lect. 9 |
| Assignment | Due date | Submission | Notes |
|---|---|---|---|
| Lect. 1 | 27.09.2023 - 23h59 CET | ||
| Lect. 2 | 04.10.2023 - 23h59 CET | ||
| Lect. 3 | 11.10.2023 - 23h59 CET | ||
| Lect. 4 | 18.10.2023 - 23h59 CET | ||
| Lect. 5 | 25.10.2023 - 23h59 CET | ||
| Lect. 6 | 01.11.2023 - 23h59 CET | ||
| Lect. 7 | |||
| Lect. 8 | |||
| Lect. 9 |
🎉 Welcome to ETH's course 101-0250-00L on solving partial differential equations (PDEs) in parallel on graphical processing units (GPUs) with the Julia programming language.
Announce: 2023 edition starts Tuesday Sept. 19, 12h45. Welcome!
This course aims to cover state-of-the-art methods in modern parallel GPU computing, supercomputing and code development with applications to natural sciences and engineering.
The goal of this course is to offer a practical approach to solve systems of differential equations in parallel on GPUs using the Julia programming language. Julia combines high-level language conciseness to low-level language performance which enables efficient code development. The Julia GPU applications will be hosted on a git-platform and implement modern software development practices.
Part 1 Introducing Julia & PDEs
The Julia language: hands-on
Solving physical processes: advection, reaction, diffusion & wave propagation
Spatial and temporal discretisation: finite-differences and explicit time-stepping
Software development tools: Git, Continuous Integration
Part 2 Solving PDEs on GPUs
Steady-state, implicit & nonlinear solutions
Efficient iterative algorithms
Parallel and GPU computing
Simulation performance limiters
Part 3 Projects
Multi-GPU computing and optimisations
xPU computing
Distributed computing
Advanced optimisations
Final projects
Solve a solid mechanics or fluid dynamics problem of your interest, such as:
the shallow water / ice equation
acoustic wave propagation, nonlinear diffusion
viscous flow or elastic deformation
viscous or elastic poromechanics
frictional heating
Navier-Stokes equations
Detailed content is available on ETHZ course catalogue
Course repository: https://github.com/eth-vaw-glaciology/course-101-0250-00
Ludovic Räss - Unil / ETHZ
Mauro Werder - WSL / ETHZ
Ivan Utkin - ETHZ / WSL
Samuel Omlin - CSCS, ETHZ
Teaching Assistant: Giacomo Aloisi - ETHZ
🎉 Welcome to ETH's course 101-0250-00L on solving partial differential equations (PDEs) in parallel on graphical processing units (GPUs) with the Julia programming language.
Announce: 2023 edition starts Tuesday Sept. 19, 12h45. Welcome!
This course aims to cover state-of-the-art methods in modern parallel GPU computing, supercomputing and code development with applications to natural sciences and engineering.
The goal of this course is to offer a practical approach to solve systems of differential equations in parallel on GPUs using the Julia programming language. Julia combines high-level language conciseness to low-level language performance which enables efficient code development. The Julia GPU applications will be hosted on a git-platform and implement modern software development practices.
Part 1 Introducing Julia & PDEs
The Julia language: hands-on
Solving physical processes: advection, reaction, diffusion & wave propagation
Spatial and temporal discretisation: finite-differences and explicit time-stepping
Software development tools: Git, Continuous Integration
Part 2 Solving PDEs on GPUs
Steady-state, implicit & nonlinear solutions
Efficient iterative algorithms
Parallel and GPU computing
Simulation performance limiters
Part 3 Projects
Multi-GPU computing and optimisations
xPU computing
Distributed computing
Advanced optimisations
Final projects
Solve a solid mechanics or fluid dynamics problem of your interest, such as:
the shallow water / ice equation
acoustic wave propagation, nonlinear diffusion
viscous flow or elastic deformation
viscous or elastic poromechanics
frictional heating
Navier-Stokes equations
Detailed content is available on ETHZ course catalogue
Course repository: https://github.com/eth-vaw-glaciology/course-101-0250-00
Ludovic Räss - Unil / ETHZ
Mauro Werder - WSL / ETHZ
Ivan Utkin - ETHZ / WSL
Samuel Omlin - CSCS, ETHZ
Teaching Assistant: Giacomo Aloisi - ETHZ
Welcome to ETH's course 101-0250-00L on solving partial differential equations (PDEs) in parallel on graphical processing units (GPUs) with the Julia language.
Agenda
💡 Welcome words & The small print
📚 Why GPU computing
💻 Intro to Julia
🚧 Exercises:
Numerical solutions
Predictive modelling
Visualisation
Ludovic Räss – the good
Ivan Utkin – the pro
Samuel Omlin – the bad
Mauro Werder – the ugly
Giacomo Aloisi (Teaching Assistant)
All you need should be available on the course website:
https://pde-on-gpu.vaw.ethz.ch
If something is missing, ping us so we can add it (or even better do a pull request
at https://github.com/eth-vaw-glaciology/course-101-0250-00/)
Let's have a brief tour of the course website
We use the Element-chat as the main communication channel for the course, both between the teachers and the student, and hopefully also between students.
Join Element (https://chat.ethz.ch/) by logging in with you NETHZ username & password.
Login link is available on Moodle.
Join the General (course infos) and Helpdesk (exercises Q&A) rooms.
Weekly exercises 5 (out of 6) as homework (alone or groups of 2) 30%
Consolidation project (alone or groups of 2) 35%
Final project (groups of 2) 35%
Bring your laptop to all lectures!
Who has access to an Nvidia GPU?
What operating system are you on?
In the first few lecture we will use JupyterHub (accessible within Moodle) to get you started. Later we plan to get you all setup with a personal Julia installation.
(Almost) All infos on https://pde-on-gpu.vaw.ethz.ch
Get started with Element (https://chat.ethz.ch/)
If some important info is missing, ping us so we can add it or make a PR.
Suggestion:
Bookmark https://pde-on-gpu.vaw.ethz.ch
Why we do it
Why it is cool (in Julia)
Examples from current research
Predict the evolution of natural and engineered systems.
e.g. ice cap evolution, stress distribution, etc...
Physical processes that describe those systems are complex and often nonlinear
no or very limited analytical solution is available
👉 a numerical approach is required to solve the mathematical model
A numerical solution means solving a system of (coupled) differential equations
\[ \mathbf{mathematical ~ model ~ → ~ discretisation ~ → ~ solution}\\ \frac{∂C}{∂t} = ... ~ → ~ \frac{\texttt{C}^{i+1} - \texttt{C}^{i}}{\texttt{∆t}} = ... ~ → ~ \texttt{C} = \texttt{C} + \texttt{∆t} \cdot ... \]Solving PDEs is computationally demanding
ODEs - scalar equations
but...
PDEs - involve vectors (and tensors) 👉 local gradients & neighbours
Computational costs increase
with complexity (e.g. multi-physics, couplings)
with dimensions (3D tensors...)
upon refining spatial and temporal resolution
Use parallel computing to address this:
The "memory wall" in ~ 2004
Single-core to multi-core devices
GPUs are massively parallel devices
SIMD machine (programmed using threads - SPMD) (more)
Further increases the Flop vs Bytes gap
👉 We are memory bound: requires to re-think the numerical implementation and solution strategies
Price vs Performance; Close to 1.5TB/s memory throughput (nonlinear diffusion) that one can achieve 🚀
Availability (less fight for resources); Still not many applications run on GPUs
Workstation turns into a personal Supercomputers; GPU vs CPUs peak memory bandwidth: theoretical 10x (practically more)
Solution to the "two-language problem"
Single code for prototyping and production
Backend agnostic:
Single code to run on single CPU or thousands of GPUs
Single code to run on various CPUs (x86, Power9, ARM)
and GPUs (Nvidia, AMD)
Interactive:
No need for 3rd-party visualisation software
Debugging and interactive REPL mode
Efficient for development
more ...
These slides are a Jupyter notebook; a browser-based computational notebook.
Code cells are executed by putting the cursor into the cell and hitting shift + enter. For more info see the documentation.
Julia
Matlab, Python, Octave, R, ...
C, Fortran, ...
Pascal, Java, C++, ...
Lisp, Haskell, ...
Assembler
Coq, Brainfuck, ...
Julia is a modern, interactive, and high performance programming language. It's a general purpose language with a bend on technical computing.
first released in 2012
reached version 1.0 in 2018
current version 1.8.1 (09.2022)
thriving community, for instance there are currently around 8300 packages registered
An example solving the Lorenz system of ODEs:
using OrdinaryDiffEq, Plots
+ Lecture 1 Lecture 1
Welcome to ETH's course 101-0250-00L on solving partial differential equations (PDEs) in parallel on graphical processing units (GPUs) with the Julia language.
Agenda
💡 Welcome words & The small print
📚 Why GPU computing
💻 Intro to Julia
🚧 Exercises:
Numerical solutions
Predictive modelling
Visualisation
💡 Note This week's homework have to be handed in as Jupyter notebook. See here for details.
\(\texttt{The small print of}\) Solving partial differential equations in parallel on GPUs
The Team
Ludovic Räss – the good
Ivan Utkin – the pro
Samuel Omlin – the bad
Mauro Werder – the ugly
Giacomo Aloisi (Teaching Assistant)
Logistics
All you need should be available on the course website:
https://pde-on-gpu.vaw.ethz.ch
If something is missing, ping us so we can add it (or even better do a pull request
at https://github.com/eth-vaw-glaciology/course-101-0250-00/)
Let's have a brief tour of the course website
Discussion & help
We use the Element-chat as the main communication channel for the course, both between the teachers and the student, and hopefully also between students.
Join Element (https://chat.ethz.ch/) by logging in with you NETHZ username & password.
Login link is available on Moodle.
Join the General (course infos) and Helpdesk (exercises Q&A) rooms.
Grading
Weekly exercises 5 (out of 6) as homework (alone or groups of 2) 30%
Consolidation project (alone or groups of 2) 35%
Final project (groups of 2) 35%
Computing
Bring your laptop to all lectures!
Who has access to an Nvidia / AMD GPU?
What operating system are you on?
In the first few lecture we will use JupyterHub (accessible within Moodle) to get you started. Later we plan to get you all setup with a personal Julia installation.
Summary
(Almost) All infos on https://pde-on-gpu.vaw.ethz.ch
Get started with Element (https://chat.ethz.ch/)
If some important info is missing, ping us so we can add it or make a PR.
Suggestion:
Bookmark https://pde-on-gpu.vaw.ethz.ch
Why solve PDEs on GPUs? & The tool for the job
Why solve PDEs on GPUs?
A brief intro about GPU computing:
Why we do it
Why it is cool (in Julia)
Examples from current research
Propaganda
Why we do it
Predict the evolution of natural and engineered systems.
e.g. ice cap evolution, stress distribution, etc...
Physical processes that describe those systems are complex and often nonlinear
no or very limited analytical solution is available
👉 a numerical approach is required to solve the mathematical model
A numerical solution means solving a system of (coupled) differential equations
\[ \mathbf{mathematical ~ model ~ → ~ discretisation ~ → ~ solution}\\ \frac{∂C}{∂t} = ... ~ → ~ \frac{\texttt{C}^{i+1} - \texttt{C}^{i}}{\texttt{∆t}} = ... ~ → ~ \texttt{C} = \texttt{C} + \texttt{∆t} \cdot ... \] Solving PDEs is computationally demanding
ODEs - scalar equations
\[ \frac{∂C}{∂t} = -\frac{(C-C_{eq})}{ξ} \] but...
PDEs - involve vectors (and tensors) 👉 local gradients & neighbours
\[ \frac{∂C}{∂t} = D~ \left(\frac{∂^2C}{∂x^2} + \frac{∂^2C}{∂y^2} \right) \] Computational costs increase
with complexity (e.g. multi-physics, couplings)
with dimensions (3D tensors...)
upon refining spatial and temporal resolution
Use parallel computing to address this:
The "memory wall" in ~ 2004
Single-core to multi-core devices
GPUs are massively parallel devices
SIMD machine (programmed using threads - SPMD) (more)
Further increases the Flop vs Bytes gap
👉 We are memory bound: requires to re-think the numerical implementation and solution strategies
Why it is cool
GPU are cool
Price vs Performance; Close to 1.5TB/s memory throughput (nonlinear diffusion) that one can achieve 🚀
Availability (less fight for resources); Still not many applications run on GPUs
Workstation turns into a personal Supercomputers; GPU vs CPUs peak memory bandwidth: theoretical 10x (practically more)
Julia is cool
Solution to the "two-language problem"
Single code for prototyping and production
Backend agnostic:
Single code to run on single CPU or thousands of GPUs
Single code to run on various CPUs (x86, Power9, ARM)
and GPUs (Nvidia, AMD)
Interactive:
No need for 3rd-party visualisation software
Debugging and interactive REPL mode
Efficient for development
Examples from current research
-
-
-
-
-
more ...
Tool for the job: introduction to Julia
Aside 1: Jupyter Notebooks
These slides are a Jupyter notebook; a browser-based computational notebook.
💡 Note You can follow the lecture along live from the Moodle-based JupyterHub server. Code cells are executed by putting the cursor into the cell and hitting shift + enter. For more info see the documentation.
Aside 2: What is your previous programming experience?
Julia
Matlab, Python, Octave, R, ...
C, Fortran, ...
Pascal, Java, C++, ...
Lisp, Haskell, ...
Assembler
Coq, Brainfuck, ...
The Julia programming language
Julia is a modern, interactive, and high performance programming language. It's a general purpose language with a bend on technical computing.
first released in 2012
reached version 1.0 in 2018
current version 1.8.1 (09.2022)
thriving community, for instance there are currently around 8300 packages registered
What does Julia look like
An example solving the Lorenz system of ODEs:
using OrdinaryDiffEq, Plots
function lorenz(x, p, t)
σ = 10
@@ -406,7 +406,7 @@ Packages
Note package installation does not work on the moodle-Jupyterhub. But it will work on your local installation.
All public Julia packages are listed on https://juliahub.com/ui/Packages.
You can install a package, say UnPack.jl by
-using Pkg
+using Pkg
Pkg.add("UnPack.jl")
using UnPack
In the REPL, there is also a package-mode (hit ]) which is for interactive use.
@@ -695,7 +695,7 @@ Question 2
Edit this page on
- Last modified: September 15, 2023. Website built with Franklin.jl and the Julia programming language.
+ Last modified: September 17, 2023. Website built with Franklin.jl and the Julia programming language.
diff --git a/lecture10/index.html b/lecture10/index.html
index 6d1c50a9..fbcc1213 100644
--- a/lecture10/index.html
+++ b/lecture10/index.html
@@ -417,7 +417,7 @@ Edit this page on
- Last modified: September 15, 2023. Website built with Franklin.jl and the Julia programming language.
+ Last modified: September 17, 2023. Website built with Franklin.jl and the Julia programming language.
Suggestion: Bookmark this page for easy access to all infos you need for the course.
Each lecture contains material on physics, numerics, technical concepts, as well as exercises. The lecture content is outlined in its introduction using the following items for each type of content:
📚 Physics: equations, discretisation, implementation, solver, visualisation
💻 Code: technical, Julia, GitHub
🚧 Exercises
The course will be taught in a hands-on fashion, putting emphasis on you writing code and completing exercises; lecturing will be kept at a minimum.
Online attendance will be possible on Zoom (ETH Students only - Password and password-less login available on Moodle).
Exercise session follow the lectures; they will not be broadcasted (no online support will be provided during the exercise session).
Schedule to be defined (on Element/Zoom or in-person)
We plan to use the Element-chat (https://chat.ethz.ch/) as the main communication channel for the course, both between the teachers and the students, and hopefully also between students. We encourage ETH students to ask course, exercises and technical questions there.
Head to the Course chat space (Element) link on Moodle to get started with Element:
Select Start Student-Chat.
Login using your NETHZ credentials to start using the browser-based client.
Join the General and Helpdesk rooms (you may see an error upon accessing the rooms - refreshing the app should solve the issue).
Download the Element Desktop/Mobile client for more comfortable access.
Homework tasks will be announced after each week's lecture. The exercise session following the lecture will get you started.
Homework due date will be Wednesday 23h59 CET every following week (8 days) to allow for Q&A during the following in-class exercise session.
Homework assignments can be carried-out by groups of 2. However, note that every student has to hand in a personal version of the homework.
➡ Check out the Homework page for an overview on expected hand-in and deadlines.
Submission of scripts (weeks 1 & 2), GitHub commit hash (or SHA) (week 3 and onwards) or other documents happens on the course's Moodle.
Actions and tasks related to GitHub will happen on your private course-related GitHub repository.
Starting from lecture 3 and onwards, homework script submission happens on GitHub.
🚧 More infos to come soon.
After submission deadline, you will find relevant scripts in a shared Polybox folder. Information on how to access it is available on Moodle.
Personal feedback and points (not grades) for weekly homework exercises will be shared individually among participants using a private file or folder on Polybox.
Starting from lecture 7 (until lecture 9), homework contribute to the course's first project. The goal of this project is to have a multi-xPU thermal porous convection solver in 3D.
For information about topics for the final project, head to Information about final projects page.
Enrolled ETHZ students will have to hand in on Moodle (& GitHub):
5 (out of 6) weekly assignments (30% of the final grade) during the course's Part 1. Weekly coding exercises can be done alone or in groups of two.
A project during Part 2 (35% of the final grade). Projects submission includes codes in a git repository and an automatic generated documentation.
A final project during Part 3 (35% of the final grade). Final projects submission includes codes in a git repository and an automatic generated documentation.
Suggestion: Bookmark this page for easy access to all infos you need for the course.
Each lecture contains material on physics, numerics, technical concepts, as well as exercises. The lecture content is outlined in its introduction using the following items for each type of content:
📚 Physics: equations, discretisation, implementation, solver, visualisation
💻 Code: technical, Julia, GitHub
🚧 Exercises
The course will be taught in a hands-on fashion, putting emphasis on you writing code and completing exercises; lecturing will be kept at a minimum.
Online attendance will be possible on Zoom (ETH Students only - Password and password-less login available on Moodle).
Exercise session follow the lectures; they will not be broadcasted (no online support will be provided during the exercise session).
Schedule to be defined (on Element/Zoom or in-person)
We plan to use the Element-chat (https://chat.ethz.ch/) as the main communication channel for the course, both between the teachers and the students, and hopefully also between students. We encourage ETH students to ask course, exercises and technical questions there.
Head to the Course chat space (Element) link on Moodle to get started with Element:
Select Start Student-Chat.
Login using your NETHZ credentials to start using the browser-based client.
Join the General and Helpdesk rooms (you may see an error upon accessing the rooms - refreshing the app should solve the issue).
Download the Element Desktop/Mobile client for more comfortable access.
Homework tasks will be announced after each week's lecture. The exercise session following the lecture will get you started.
Homework due date will be Wednesday 23h59 CET every following week (8 days) to allow for Q&A during the following in-class exercise session.
Homework assignments can be carried-out by groups of 2. However, note that every student has to hand in a personal version of the homework.
➡ Check out the Homework page for an overview on expected hand-in and deadlines.
Submission of scripts (weeks 1 & 2), GitHub commit hash (or SHA) (week 3 and onwards) or other documents happens on the course's Moodle.
Actions and tasks related to GitHub will happen on your private course-related GitHub repository.
Starting from lecture 3 and onwards, homework script submission happens on GitHub.
🚧 More infos to come soon.
After submission deadline, you will find relevant scripts in a shared Polybox folder. Information on how to access it is available on Moodle.
Personal feedback and points (not grades) for weekly homework exercises will be shared individually among participants using a private file or folder on Polybox.
Starting from lecture 7 (until lecture 9), homework contribute to the course's first project. The goal of this project is to have a multi-xPU thermal porous convection solver in 3D.
For information about topics for the final project, head to Information about final projects page.
Enrolled ETHZ students will have to hand in on Moodle (& GitHub):
5 (out of 6) weekly assignments (30% of the final grade) during the course's Part 1. Weekly coding exercises can be done alone or in groups of two.
A project during Part 2 (35% of the final grade). Projects submission includes codes in a git repository and an automatic generated documentation.
A final project during Part 3 (35% of the final grade). Final projects submission includes codes in a git repository and an automatic generated documentation.
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