Top 8 C++ Numerical Processing Libraries in 2024

Here are some of the famous C++ Numerical Processing Libraries. The use cases of these libraries include Scientific Computing, Financial Modeling, Machine Learning, Data Analysis, and Image and Video Processing.  

C++ numerical processing libraries are collections of functions and classes that allow developers to perform numerical computations in C++. These libraries are used for various applications, including scientific computing, data analysis, engineering, and financial analysis.   

Let us look at some of these famous libraries. 

opencv

• Offers support for both CPU and GPU acceleration for many of its algorithms. 
• Allows developers to implement complex algorithms quickly. 
• Is highly optimized for memory and speed usage.

GSL

• Is highly portable and can be used on different platforms, including Windows, Linux, Unix, and Mac OS.
• Includes extensive documentation for developers.
• Is available under the GNU General Public License and is open source.

QuantLib

• Provides a comprehensive suite of numerical tools for pricing financial derivatives and other instruments. 
• Provides a wide range of APIs, allowing developers to customize and extend the library for their needs. 
• Provides extensive functionality for pricing complex derivatives and instruments, like Monte Carlo simulations, path-dependent options, and exotic options. 

xtensor

• Uses advanced template techniques to enable fast and efficient vectorization of operations. 
• Supports broadcasting and universal functions. 
• Provides a powerful expression system that allows users to write complex expressions with minimal code. 

Pangolin

• Leverages multi-threading, SIMD vectorization, and other techniques to maximize performance. 
• Provides a simple and intuitive interface for working with numerical data. 
• Offers various customization options, allowing users to tailor the library to their specific needs. 

enoki

• Offers a wide range of highly optimized SIMD vector operations, allowing users to use their processor's capabilities.  
• Supports GPU acceleration, allowing users to leverage the power of their GPUs for faster numerical processing. 
• Offers an easy-to-use API for distributed computing. 

Trilinos

• Offers an extensive suite of robust, reliable, and accurate numerical algorithms for solving challenging scientific and engineering problems.
• Is designed with a modular architecture, allowing users to add or remove components quickly and easily.
• Is designed for scalability, allowing for efficient performance on a wide range of computing resources, from single-processor computers to parallel supercomputers.

vexcl

• Supports both OpenCL and CUDA-enabled GPUs. 
• Has Vector Expressions (VEX) language, which allows one to express numerical operations concisely and readably.  
• Offers a range of data-distribution options, allowing users to customize their application's data layout.