✨ GPU N-Body Problem Solver

This repository was created for a seminar on efficient programming for GPUs. It contains efficient Python code to solve the N-body problem by leveraging GPU acceleration through various frameworks. Currently, the implementation focuses on using Numba. The code has been tested on CPU environments for initial verification.

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🚀 Frameworks

The goal of this project is to provide flexible implementations across different popular GPU frameworks. Below is the structure for the currently implemented and planned solutions.

Numba 🐍 (Implemented)

This implementation uses Numba (specifically numba.cuda) to compile Python code for execution directly on NVIDIA GPUs.

• Status: Implemented
• Location: nbody_numba.py (or similar file)
• Key Features: Utilizes JIT compilation for high-performance CUDA kernels.

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CuPy 🌌 (Template)

(This section is a template for future implementation)

This implementation will use the CuPy library, which provides a NumPy-compatible array interface for GPU computation.

• Status: Planned

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PyTorch 🔥 (Template)

(This section is a template for future implementation)

This implementation will use PyTorch tensors and its CUDA backend. While primarily known for deep learning, PyTorch offers robust and efficient general-purpose GPU computing.

• Status: Planned

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📦 Installation

Follow these steps to get the simulation running on your machine.

1. Prerequisites

Before you start, ensure you have the following installed:

• Anaconda or Miniconda: This manages the Python environment. Download here.
• Git: To download the code. Download here.

2. Get the Code

Open your terminal (Mac/Linux) or Anaconda Prompt (Windows) and run the following command to download the project to your computer:

git clone [https://github.com/RubberLanding/gpu_seminar_2025.git](https://github.com/RubberLanding/gpu_seminar_2025.git)
cd gpu_seminar_2025

Run this command to create the virtual environment (this might take a few minutes) and activate it:

conda env create -f environment.yaml
conda activate nbody_numba

Finally, install the project:

pip install .

To make sure that everything is running correctly, try running the simulation with a small number of particles, e.g. with Numba:

python src/nbody/numba/simulation.py