System Requirements

Recommended System Requirements

  • Processors:
    • Intel® Core™ i5 processor 4300M at 2.60 GHz or 2.59 GHz (1 socket, 2 cores, 2 threads per core), 8 GB of DRAM
    • Intel® Xeon® processor E5-2698 v3 at 2.30 GHz (2 sockets, 16 cores each, 1 thread per core), 64 GB of DRAM
    • Intel® Xeon Phi™ processor 7210 at 1.30 GHz (1 socket, 64 cores, 4 threads per core), 32 GB of DRAM, 16 GB of MCDRAM (flat mode enabled)
  • Disk space: 2 to 3 GB
  • Operating systems: Windows® 10, macOS*, and Linux*

 
Minimum System Requirements

  • Processors: Intel Atom® processor or Intel® Core™ i3 processor
  • Disk space: 1 GB
  • Operating systems: Windows* 7 or later, macOS, and Linux
  • Python* versions: 2.7.X, 3.6.X
  • Included development tools: conda*, conda-env, Jupyter Notebook* (IPython)
  • Compatible tools: Microsoft Visual Studio*, PyCharm*
  • Included Python packages: NumPy, SciPy, scikit-learn*, pandas, Matplotlib, Numba*, Intel® Threading Building Blocks, pyDAAL, Jupyter, mpi4py, PIP*, and others.

 
Software

  • PIP and NumPy: Installed with PIP, Ubuntu*, Python 3.6.2, NumPy 1.13.1, scikit-learn 0.18.2
  • Windows: Python 3.6.2, PIP and NumPy 1.13.1, scikit-learn 0.18.2
  • Intel® Distribution for Python* 2018

 
Modifications

  • Scikit-learn: Conda*-installed NumPy with Intel® Math Kernel Library (Intel® MKL) on Windows (PIP-installed SciPy on Windows contains Intel MKL dependency)
  • Black-Scholes on Intel Core i5 processor and Windows: PIP-installed NumPy and Conda-installed SciPy

 
Sizes

This table lists the size of matrices used for benchmarks.

Hardware numpy.dot scipy.linalg.lu scipy.linalg.det scipy.linalg.inv scipy.linalg.cholesky scipy.fftpack.fft
Intel® Xeon® processor (32 core) and Intel® Xeon Phi™ processor (64 core) (20 k, 10 k) and (10 k, 20 k) (20 k, 20 k) (15 k, 15 k) (25 k, 25 k) (40 k, 40 k) 520 k
Intel® Xeon® processor (1 core) (20 k, 5 k) and (5 k, 20 k) (20 k, 20 k) (15 k, 15 k) (10 k, 10 k) (10 k, 10 k) 520 k
Intel® Xeon Phi™ processor (1 core) (20 k, 300) and (300, 20 k) (6 k, 6 k) (4 k, 4 k) (2 k, 2 k) (10 k, 10 k) 520 k

 
Installation Guides

Anaconda* Package
YUM Repository
APT Repository
Docker* Images