`hotwheels` is a codebase for hydrodynamic N-body cosmological simulations, developed by **Antonio Ragagnin**.
`hotwheels` is a codebase for hydrodynamic N-body cosmological simulations, developed by **Antonio Ragagnin**.
As part of my commitment to advancing simulation tools, I am working on a flexible and modular implementation of a [Gadget](https://wwwmpa.mpa-garching.mpg.de/gadget/)-like code (temporarily named `hotwheels`). This new implementation incorporates lessons learned from over a decade of experience working with HPC and Gadget codes (e.g., OpenGadget3, see Dolag et al., in prep).
As part of my commitment to advancing simulation tools, I am working on a flexible and modular implementation of a [Gadget](https://wwwmpa.mpa-garching.mpg.de/gadget/)-like code (temporarily named `hotwheels`). This new implementation incorporates lessons learned from over a decade of experience working with HPC and Gadget codes (e.g., OpenGadget3, see Dolag et al., in prep).
`hotwheels` is designed to leverage CPU and GPU parallelism, with a strong emphasis on modularity. This modular approach facilitates collaboration with HPC engineers and GPU vendors, enabling efficient utilization of HPC systems. Modularity also enhances testability, simplifies the addition of new features, and allows rethinking code structure based on past experiences.
`hotwheels` is designed to leverage CPU and GPU parallelism, with a strong emphasis on modularity. This modular approach facilitates collaboration with HPC engineers and GPU vendors, enabling efficient utilization of HPC systems. Modularity also enhances testability, simplifies the addition of new features, and allows rethinking code structure based on past experiences.
Check the here the [TODO list](TODO)
Check the here the [TODO list](TODO).
[TOC]
## Key Features
# Key Features
-**Modularity:** Each module is self-contained, adhering to the [Open-Closed Principle](https://en.wikipedia.org/wiki/Open%E2%80%93closed_principle). `hotwheels` consists of C libraries wrapped in Python for high-level processing, following the successful paradigm of machine learning frameworks ([reference](https://www.mdpi.com/2078-2489/11/4/193)). Developers can focus solely on their module’s files without altering other parts of the code.
-**Modularity:** Each module is self-contained, adhering to the [Open-Closed Principle](https://en.wikipedia.org/wiki/Open%E2%80%93closed_principle). `hotwheels` consists of C libraries wrapped in Python for high-level processing, following the successful paradigm of machine learning frameworks ([reference](https://www.mdpi.com/2078-2489/11/4/193)). Developers can focus solely on their module’s files without altering other parts of the code.
-**Mini-Apps:** From over 10 years of experience collaborating with HPC engineers and GPU vendors, I found it crucial to deploy the code as minimal, standalone applications. This approach allows engineers to focus on improving code quality and porting it to different architectures without being distracted by unrelated components.
-**Mini-Apps:** From over 10 years of experience collaborating with HPC engineers and GPU vendors, I found it crucial to deploy the code as minimal, standalone applications. This approach allows engineers to focus on improving code quality and porting it to different architectures without being distracted by unrelated components.
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-**Parallelism:** Recognizing the dominance of **GPUs** in HPC, especially driven by AI advancements, `hotwheels` is designed from scratch to run efficiently on GPUs, multi-core CPUs, and MPI-parallel systems.
-**Parallelism:** Recognizing the dominance of **GPUs** in HPC, especially driven by AI advancements, `hotwheels` is designed from scratch to run efficiently on GPUs, multi-core CPUs, and MPI-parallel systems.
-**Documentation:** Navigation through a large codebase can be challenging. `hotwheels` prioritizes comprehensive documentation, including high-level overviews and detailed API references for each component. Documentation is generated automatically, and commits to the `main` branch are rejected if they fail the indentation and documentation pipelines.
-**Documentation:** Navigation through a large codebase can be challenging. `hotwheels` prioritizes comprehensive documentation, including high-level overviews and detailed API references for each component. Documentation is generated automatically, and commits to the `main` branch are rejected if they fail the indentation and documentation pipelines.
## Modules
# Modules
-[core](https://www.ict.inaf.it/gitlab/hotwheels/core): Provides essential utilities for: (1) Compiling and executing C code from Python (`core.make`); (2) Generating and managing particle data structures as structures of arrays (SoAs) (`core.soa`); (3) General C utilities (e.g., MPI patterns, MyMalloc, panic handling, and timers in `core.c_utils`).
-[core](https://www.ict.inaf.it/gitlab/hotwheels/core): Provides essential utilities for: (1) Compiling and executing C code from Python (`core.make`); (2) Generating and managing particle data structures as structures of arrays (SoAs) (`core.soa`); (3) General C utilities (e.g., MPI patterns, MyMalloc, panic handling, and timers in `core.c_utils`).
-[IO](https://www.ict.inaf.it/gitlab/hotwheels/io): Handles parallel reading and writing of Gadget1-2 snapshots (thus including [N-GenIC](https://www.h-its.org/2014/11/05/ngenic-code/) and [MUSIC](https://www-n.oca.eu/ohahn/MUSIC/) formats), as well as HDF5 files.
-[IO](https://www.ict.inaf.it/gitlab/hotwheels/io): Handles parallel reading and writing of Gadget1-2 snapshots (thus including [N-GenIC](https://www.h-its.org/2014/11/05/ngenic-code/) and [MUSIC](https://www-n.oca.eu/ohahn/MUSIC/) formats), as well as HDF5 files.
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-[PESPH](https://www.ict.inaf.it/gitlab/hotwheels/PESPH): **[To-Do]** A pressure-entropy smoothed particle hydrodynamics (SPH) module.
-[PESPH](https://www.ict.inaf.it/gitlab/hotwheels/PESPH): **[To-Do]** A pressure-entropy smoothed particle hydrodynamics (SPH) module.
-[SH03](https://www.ict.inaf.it/gitlab/hotwheels/SH03): **[To-Do]** A star formation recipe based on [Springel & Hernquist (2003)](https://ui.adsabs.harvard.edu/abs/2003MNRAS.339..289S/abstract), with cooling processes handled by [Grackle](https://grackle.readthedocs.io/en/latest/).
-[SH03](https://www.ict.inaf.it/gitlab/hotwheels/SH03): **[To-Do]** A star formation recipe based on [Springel & Hernquist (2003)](https://ui.adsabs.harvard.edu/abs/2003MNRAS.339..289S/abstract), with cooling processes handled by [Grackle](https://grackle.readthedocs.io/en/latest/).
## Run your simulations
# Run your simulations
Currently it is possible to run particle-mesh gravity only simulations with and without comoving integrations:
Currently it is possible to run particle-mesh gravity only simulations with and without comoving integrations:
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-**soon:** A cosmological PM DMO run with fixed timesteps
-**soon:** A cosmological PM DMO run with fixed timesteps
## Licence
# Licence
This code is currently unlicensed. The majority of the code has been written from scratch by Antonio Ragagnin, and redistribution of this code requires explicit authorization from the author.
This code is currently unlicensed. The majority of the code has been written from scratch by Antonio Ragagnin, and redistribution of this code requires explicit authorization from the author.
