Galax : Simulate the collision of two galaxies

Description

Galax is the simple implementation of the n-body problem, applied to the simulation of celestial objects. The equations that describe the problem between are listed in equations.pdf.

Code

A naive implementation can be found in Model_CPU_naive.cpp. Note that the implementation uses "magic" numbers at some stages. We do not want to focus on the accuracy of the simulation but on the parallelisation of the nbody problem.

You can generate the hierarchy of the different classes as well as a list of the different attributes and methods used in the project thanks to the doxygen tool. The instructions to perform this operation are detailed in the doc directory.

Means

You will work in groups of 2 (except one group of three). Fill the document here : Groups.

One machine is provided per group.

Here is a link to a tutorial to remote desktop.

Objective

The objective of the course is to accelerate the simulation. You must maximize the number of simulated frames per second, when the display is disabled and the number of simulated particles is 10000. You can use all the techniques that have been seen in the course, and combine them.

2 categories:
- Exact reproduction of Model_CPU_naive.cpp
- Modifications of the algorithm allowed

The best group will be given access to a massively parallel machine to test its code (such as 2xIntel®Xeon™Platinum 8168 CPUs ), or a high performance GPU if necessary.

Evaluation

Project logbook: you will keep an up-to-date document explaining your experiences, your results, your choices, taken from day to day. No need to detail, to put images, to spend time on formatting. You will use text format files that have been created for you here.
Presentation (10 minutes + 5 minutes questions)
- Description of the final implementation
- Demonstration
- Measured results
Deliverables
- Project code

Extras

Any extra feature will be well appreciated and taken into account for the evaluation :

Improvement of the graphical rendering
Inclusion of other libraries into the cmake build automation
Addition of algorithmic variants of the nbody problem (n² -> nlogn)
Include Galax as a wrapper for other languages
...

How to compile and run the code

Galax uses cmake in order to automate building. The following procedure has been tested on a fresh Ubuntu 20.04, but it can be adapted for other platforms.

First steps

Galax needs cmake and the g++ compiler to be compiled.

    git clone --recursive git@github.com:bonben/galax_eleves.git # clone repository and update git submodules
    mkdir galax/build # create a build directory
    cd galax/build # switch to it
    cmake .. -DCMAKE_BUILD_TYPE=Release # configure project with cmake
    make #make

You can then launch the compiled binary :

    ./bin/galax

You can access the help with the following command :

    ./bin/galax -h

For now, no graphical display has been set up and therefore only the number of frame per second (which correspond to the number of simulation time steps executed per second) will be displayed in the terminal.

Graphical display

Galax relies on SDL2 and OpenGL to propose a graphical display. Install those dependencies (already installed on campux).

    sudo apt install freeglut3-dev libglew-dev libsdl2-dev

Now the graphical display should be activated through cmake configuration, and the project be rebuilt.

    # in the "build" directory
    cmake .. -DGALAX_LINK_SDL2=ON
    make

Launching galax should now open a window with the graphical display.

Activating OMP

For now, the performance (FPS: Frame Per Second) is low. It is possible to accelerate the processing by using OpenMP for multithreading and the MIPP project (https://github.com/aff3ct/MIPP) for vectorization.

    # in the "build" directory
    cmake .. -DGALAX_LINK_OMP=ON
    make
    ./bin/galax -c CPU_FAST # use CPU_FAST version with multithreading & vectorization

Activating CUDA

Provided a software platform with a working version of the CUDA toolkit, it is also possible to use the GPU to perform the simulation. Please refer to online documentation to correctly install CUDA on your system (https://docs.nvidia.com/cuda/).

    # in the "build" directory
    cmake .. -DGALAX_LINK_CUDA=ON
    make
    ./bin/galax -c GPU # use GPU version

cyclimse/galax_eleves