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Oct 1, 2021
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N-Body Problem: Added datasets, README for usage and for theory. #2

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47 changes: 47 additions & 0 deletions nbody_problem/README.md
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# NumPy Benchmarks

Benchmarking NumPy in realistic situations.

## Usage

To run the benchmarks, you need to install the following libraries:
```
sudo pacman -S python-numpy
sudo pacman -S python-pythran
pip install transonic
sudo pacman -S python-llvmlite
sudo pacman -S python-setuptools
conda install numba
sudo pacman -S cmake
sudo pacman -S gcc
```

To Run the Files

1. Clone the Repository.
```
git clone https://github.com/numpy/numpyorg-benchmarks
```

2. To execute NumPy and Python Code.
```
taskset -c 6,7,14,15 python python/filename.py data/dataset_filename.txt
```
*Note:* To obtain the accurate results the benchmarking is performed on the 4 isolated CPU cores (6, 7, 14, 15).

3. To execute Algorithms using Compiled Methods.
```
transonic -b BACKEND python/filename.py
TRANSONIC_BACKEND="BACKEND" taskset -c 6,7,15,16 python python/filename.py data/dataset_filename.txt
```

Here, `-b` flag used to set the BACKEND (presently, there are 3 backends in Transonic; `cython`, `numba` and `python`. The default backend is `pythran`. Currently, we used `numba` and `pythran` for our implementation.

## Writing Benchmarks

Obtaining stable and reliable benchmark results requires to tune the system and to analyze the results manually.

Some things to consider:
- The first goal is to avoid the outliers caused by noisy applications.
- To set the system state for benchmarking use the [pyperf system tune command](https://pyperf.readthedocs.io/en/latest/cli.html#system-cmd).
- To reduce the system jitter refer [tune the system for benchmarks](https://pyperf.readthedocs.io/en/latest/system.html#system) page.
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