https://github.com/intelpython/blackscholes_bench

Benchmark computing Black Scholes formula using different technologies
https://github.com/intelpython/blackscholes_bench

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Benchmark computing Black Scholes formula using different technologies

• Host: GitHub
• URL: https://github.com/intelpython/blackscholes_bench
• Owner: IntelPython
• License: mit
• Created: 2017-02-24T00:42:27.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2024-08-14T19:21:17.000Z (9 months ago)
• Last Synced: 2025-04-12T21:06:55.393Z (about 1 month ago)
• Language: Python
• Size: 108 KB
• Stars: 14
• Watchers: 4
• Forks: 7
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS
  • Security: SECURITY.md

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# BlackScholes benchmark

Benchmark computing Black Scholes formula using different technologies.

## Prerequisites

- `icc`, if compiling native benchmarks. Intel Distribution for Python*

  2019 Gold benchmarks used icc 17.0.1.

- `mkl`, if compiling native benchmarks with MKL.

## Setup

### Linux & Mac

- Run `. activate-conda.sh` to install miniconda on Linux and Mac

- Run `make` to build and run native benchmarks

  - Run `make mkl` to build and run MKL version

  - Run `make nomkl` to build and run non-MKL version

  - Run `make black_scholes_mkl` to only build MKL version

  - Run `make black_scholes` to only build non-MKL version

### Windows

- Download & install Miniconda3 and MSYS2

- Run bash from MSYS2 and activate miniconda environment

- Run `./install-conda-envs.sh` to install Python environments

## Usage

### Native benchmarks

- Non-MKL version: Run the compiled binary `./black_scholes`.

- MKL version: Run the compiled binary `./black_scholes_mkl`.

### Python benchmarks

```

usage: {bs_erf_*.py|run.sh} [-h]

                       [--steps STEPS] [--step STEP] [--chunk CHUNK]

                       [--size SIZE] [--repeat REPEAT] [--dask DASK]

                       [--text TEXT]

optional arguments:

  -h, --help       show this help message and exit

  --steps STEPS    Number of steps

  --step STEP      Factor for each step

  --chunk CHUNK    Chunk size for Dask

  --size SIZE      Initial data size

  --repeat REPEAT  Iterations inside measured region

  --dask DASK      Dask scheduler: sq, mt, mp

  --text TEXT      Print with each result

```

## See also

"[Accelerating Scientific Python with Intel Optimizations](http://conference.scipy.org/proceedings/scipy2017/pdfs/oleksandr_pavlyk.pdf)" by Oleksandr Pavlyk, Denis Nagorny, Andres Guzman-Ballen, Anton Malakhov, Hai Liu, Ehsan Totoni, Todd A. Anderson, Sergey Maidanov. Proceedings of the 16th Python in Science Conference (SciPy 2017), July 10 - July 16, Austin, Texas