cse633-mpi-nbody-simulation

Introduction

Simple gravitational N-body simulation using MPI for CSE633 - Parallel Algorithms.

Approach

The algorithm is a simple one for a serial implementation:

• Calculate force on each particle from every other particle
• Compute the net force on each particle based on above
• Update the positions and velocities of each particle based on net force calculated
• Repeat

The parallel algorithm in MPI simply parallelizes the first step where each particle's computation happens on a single core in parallel. More details are available in pseudo_code.txt.

File Description

• main.c: The actual MPI implementation of a simple n-body calculation; the no of particles and no of iterations are hard-coded but can be parameterized
• generate.py: Generates a random dataset for initial positions and velocities of the particles; PARTICLE_COUNT is defined in the file
• visualize.py: Creates a Tkinter based animation that visualizes each iteration of the particles being acted upon by mutual gravitational forces
• input.txt: Output redirected to file from generate.py containing the initial positions for each particle
• viz_data_25_25.txt: Sample viz data generated for 25 particles for 25 iterations that can be used by visualize.py to visualize the simulation
• ./job_*/job_*.sh: Directory contains a job script for CCR; pattern defined as 1-node, 1-core-per-node, 100-particles, 100-iterations for each job
• pseudo_code.txt: Simple description of the algorithm used
• results.png: Simple graph for the final cumulative results
• README.md: Contains the MD content for this README page

Steps to run

• Run python3 generate.py | tee input.txt to generate initial data in input.txt
• Set required values of particle count, iterations, etc. and compile main.c with below command: mpicc main.c -lm -o nbody_<particle_count>_<iteration_count>
• Run with mpirun -n <core_count> nbody
• Measure the time based on the output print for the run
• [Optional] Edit the file name in visualize.py, if changed, and run with python3 visualize.py to view visualization

Final Results

The results can be interpreted as each line representing the runtime of different combinations of complexity (particles * iterations) for different PEs (no of processing elements aka nodes * cores per nodes)

NOTE: Check corresponding job.sh script to see how mpirun is called for each job & refer to UB-CCR documentation on how to run these job files.