iPython-bench

iPython-bench is a set of benchmarks of Intel's Python and its extensions such as dpctl, dpnp or numba-dpex included in the oneAPI toolkit. This repository also includes versions for native Python and SYCL. The benchmarks include:

• Gemm: a matrix-matrix multiplication.
• Calc_pi: a Monte Carlo method for PI calculation.
• Nbody: simulates the interactions between a large number of particles, such as stars or planets, in a gravitational field.
• VCA: Vertex Component Analysis (VCA) is a signal processing technique used for hyperspectral unmixing, which refers to the process of decomposing a mixed spectrum into its constituent spectral signatures.

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1. Requirements

To run the code, you will need to install the following dependencies beforehand:

• >= CMake 3.13
• >= Make 4.2
• >= Python 3.6 (but only tested over 3.10), as well as, had installed numpy.
• >= Intel oneAPI Base Toolkit 2023.1, which contains the Intel C++ compiler and the oneMKL library.
• >= Intel AI Analytics Toolkit 2023.1, which contains the Intel Python.

2. Setting up

To run Intel Python and SYCL benchmarks you will need to set the oneAPI variables up, to do so:

$ source /opt/intel/oneapi/setvars.sh

2.1 Python dependencies

The best way to install python dependencies is by using a virtual environment, to do so:

$ sudo apt install virtualenv
$ virtualenv -p python3 venv
$ source venv/bin/activate
$ pip install numpy

To deactivate virtualenv, do by:

$ deactivate

3. Running

3.1 Intel Python

In the case you want to launch Intel Python benchmarks, you can select which device by using the variable "ONEAPI_DEVICE_SELECTOR" (more info). For example:

$ ONEAPI_DEVICE_SELECTOR=gpu python3 ipython/vca.py data/Cuprite 19 0 float32

3.2 SYCL

Moving to SYCL, you have to previously build the benchmarks, e.g.:

$ cd sycl
$ mkdir build
$ cd build
$ cmake ..
$ make
$ ONEAPI_DEVICE_SELECTOR=cpu vca/vca.exe ../../data/Cuprite 19 1

Publications

• Faqir-Rhazoui, Y., García, C. (2023). Exploring Heterogeneous Computing Environments: A Preliminary Analysis of Python and SYCL Performance. In: Naiouf, M., Rucci, E., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2023. Communications in Computer and Information Science, vol 1828. Springer, Cham.
  • DOI: https://doi.org/10.1007/978-3-031-40942-4_1

Acknowledgements

This paper has been supported by the EU (FEDER), the Spanish MINECO and CM under grants S2018/TCS-4423, PID2021-126576NB-I00 funded by MCIN/AEI/10.13039/501100011033 and by “ERDF A way of making Europe”.