building numpy and scipy with intel compilers and intel mkl

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Okay, let's dive into a comprehensive tutorial on building NumPy and SciPy with Intel Compilers and Intel MKL (Math Kernel Library). This approach can significantly boost the performance of numerical computations, especially on Intel hardware.

*Why Use Intel Compilers and MKL?*

*Performance Optimization:* Intel Compilers are designed to generate highly optimized code for Intel CPUs. They leverage advanced processor features like AVX (Advanced Vector Extensions) and other instruction sets for faster execution.
*Intel MKL Integration:* MKL is a highly optimized library for mathematical functions (BLAS, LAPACK, FFT, etc.). NumPy and SciPy extensively rely on these functions, and using MKL can substantially improve their speed.
*Vectorization and Parallelization:* Intel compilers and MKL are adept at automatic vectorization and parallelization, which can further enhance performance, especially for large datasets.
*Compatibility:* This setup is generally very well-aligned with Intel platforms.

*Prerequisites:*

1. *Intel oneAPI Base Toolkit:* You'll need to download and install the Intel oneAPI Base Toolkit from the official Intel website. This toolkit includes the Intel compilers (C++, Fortran), MKL, and other essential tools. Choose the online or offline installer based on your needs and system configuration. Make sure to install it in a location you remember (e.g., `/opt/intel/oneapi`).

2. *Python:* Ensure you have a Python installation (preferably Python 3.7 or later). We highly recommend using a virtual environment to keep your dependencies isolated.

3. *NumPy and SciPy Source Code:* You'll need to download the source code for NumPy and SciPy. The standard approach is to get these from their respective Git repositories:



4. *Dependencies:* Both NumPy and SciPy have build-time dependencies. These include:

Cython
Meson and Ninja
Build essentials (C/C++ compiler, make, etc.) - Generally av ...

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#scipy
#IntelCompilers