Check the original README in the master branch, or the original repo (https://github.com/cmaureir/gravidy).

GraviDy is a new GPU, direct-summation N-body integrator written from scratch and based on the Hermite scheme. The most important features of the code are:

• Written in C/C++,
• Using parallel computing techniques and libraries like OpenMP, MPI and CUDA,
• full double-precision resolution,
• its high modularity, which allows users to readily introduce new physics into it,
• the exploitation of all high-performance computing resources available,
• its maintenance and improvement cycle,
• the fact that the code is publicly released under a BSD license and will be maintained via planned, public, regular updates.

Publication reference

Maureira-Fredes, C and Amaro-Seoane, P. "GraviDy, a GPU modular, parallel direct-summation N−body integrator: Dynamics with softening", MNRAS, vol. 473, pp. 3113-3127, Jan . 2018.

First, clone the repositotry

git clone https://github.com/chongchonghe/gravidy
cd gravidy/src

Then, depending on the OS you are on,

• On UMD Astronomy machines, the default g++ is version 8, which is outdated. Version 11 is available in PATH as g++11

make CXX="g++11"

The original version of Gravidy use Boost library to do command parsing. I rewrote that part using pure C++ and removed this dependency, so the code does not rely on any library. If you want to use the original version with Boost, pass use_boost=1 as an argument to make.

• On macOS, use your latest version of g++ isntalled via homebrew. The default g++, or Apple clang, does not support omp. The Boost library failed on macOS, so Makefile automatically set use_boost to 0 on Mac.

make CXX="g++-13"

• On Zaratan

CPU (with OpenMP):

module load gcc/11.3.0
# module load boost/1.79.0
make

To use Boost command parsing, uncomment the line that loads boost, and pass use_boost=1 as an argument to make.

MPI: Be sure to run make distclean before recompiling if you changed your module version or modified Makefile.

module load gcc/11.3.0
module load openmpi/4.1.4
# module load boost/1.79.0
make mpi

CUDA: failed. I couldn't make it work. Let me know if you can.

module load gcc/8.4.0
module load cuda/10.2.89
# module load boost/1.72.0
make gpu

• On Nvidia Cluster (for Hackathon)

module load gcc/11.2.0
make cpu
module load openmpi/gcc/64/4.1.2
make mpi
module unload openmpi/gcc/64/4.1.2
module load cuda-11.7.1
module load cuda11.7/toolkit/11.7.1
# module load nvhpc-23.3-nompi
make gpu

To run the test

module load conda
conda create -n gravidy python=3.9
conda activate gravidy
conda init bash
exec bash
module load conda
conda activate gravidy
conda install numpy pandas matplotlib
bash test2.bash

You can render UML diagrams using Mermaid. For example, this will produce a sequence diagram:

Here is a flow chart for 32k particles:

TODO:

• There are gaps in CUDA HW which are not included in the following chart. They could related to cudaMemcpyAsync but I don’t know exactly what they are about.

graph TB
A[Pure CPU, 80s] --> B[HtoD, 0.5ms] --> C((k_prediction, <0.1ms)) --> D[DtoH, 0.2ms] --> E[HtoD, <0.1ms] --> F((k_update, 0.4ms)) --> G[DtoH, <0.1ms] --> H[HtoD, 0.3ms] -- 11k times --> C

Copyright 2014 Cristián Maureira-Fredes

Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:

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